Food loss reduction in emerging economies by exploiting short sea opportunities Auke Schripsema Han Soethoudt Seth Tromp Bhairavi Jani Priyanca Vaishnav Anjali Anit Sibasish Pradhan Report 1819 Colophon Title Food loss reduction in emerging economies by exploiting short sea opportunities Author(s) Auke Schripsema Han Soethoudt Seth Tromp Bhairavi Jani Priyanca Vaishnav Anjali Anit Sibasish PradhanAuthor Number 1819 Date of publication 30 April 2018 Version End version Confidentiality Yes, until 01-06-2018 Approved by Nicole Koenderink Review Intern Name reviewer Nina Waldhauer Sponsor The World Bank Client The World Bank Wageningen Food & Biobased Research P.O. Box 17 NL-6700 AA Wageningen Tel: +31 (0)317 480 084 E-mail: info.fbr@wur.nl Internet: www.wur.nl/foodandbiobased-research © Wageningen Food & Biobased Research, institute within the legal entity Stichting Wageningen Research All rights reserved. No part of this publication may be reproduced, stored in a retrieval system of any nature, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without the prior permission of the publisher. The publisher does not accept any liability for inaccuracies in this report. 2 © Wageningen Food & Biobased Research, institute within the legal entity Stichting Wageningen Research Content Abstract 6 1. Introduction 8 Project background 8 Project objectives 9 Definitions 9 1.3.1. Definitions of short sea shipping 9 1.3.2. Working definition: short sea shipping methodology 10 Report structure 10 2. Literature review 12 Requirements of short sea shipping 12 2.1.1. Availability of infrastructure and equipment 13 2.1.2. Harmonizing administrative and legal procedures and regulations 13 2.1.3. Overcoming the poor image of short sea shipping 14 2.1.4. Provision of short sea shipping service which meets shippers’ needs 14 Advantages of short sea shipping 14 2.2.1. Decreased environmental impact 14 2.2.2. Better utilization of infrastructure 15 2.2.3. Potential cost efficiencies 15 2.2.4. Coastal economic development 15 A decision tool for the implementation of short sea shipping 15 A short sea shipping methodology for comparing road and short sea transport of agri- food products 17 Summary 17 3. Methodology development 20 Selection of product-route combinations 20 3.1.1. Route selection 21 3.1.2. Product selection 23 3.1.3. Initial feasibility check 26 Selection of farmer-market combinations 26 3.2.1. Market research 28 3.2.2. Farmer research 33 3.2.3. Match analysis 35 Supply chain design 38 3.3.1. Physical design 40 3.3.2. Logistical control 40 3.3.3. Information 42 3.3.4. Organization 43 3.3.5. Supply chain evaluation 46 3 Test implementation 48 3.4.1. Scope 49 3.4.2. Test implementation team 49 3.4.3. Measurement protocol 49 4. Methodology application 51 Selection of product-route combinations 52 4.1.1. Route selection 52 4.1.2. Product Selection 70 4.1.3. Initial feasibility check 103 4.2. Selection of farmer-market combinations 105 4.2.1. Market research 106 4.2.2. Farmer research 114 4.2.3. Match analysis 118 4.3. Supply chain design 124 4.3.1. Physical design 125 4.3.2. Logistical control 128 4.3.3. Information 129 4.3.4. Organization 132 4.3.5. Supply chain evaluation 133 4.4. Test implementation 136 4.4.1. Scope 136 4.4.2. Define test implementation team 137 4.4.3. Measurement protocol 138 5. Conclusion and discussion 144 References 147 Appendices 148 Appendix 1: Number of shipping lines servicing the CEZs (criteria d, 4.1.1.4) 149 Appendix 2: Minimum Shipping Connection to Other Ports (criteria e, 4.1.2) 151 Appendix 3: Feeder Vessels Plying per week between Ports (criteria g, 4.1.3) 152 Appendix 4: Availability of Cranes, Trailers, Side Loaders and Carriers or Ro-Ro services (criteria h, 4.1.3) 154 Appendix 5: Draft of Kolkata Port (criteria i, 4.1.3) 155 Appendix 6: Container Freight Stations in a 15-Km radius of Ports (criteria j, 4.1.3) 156 Appendix 7: Charging points for reefers at Container Freight Stations (criteria j, 4.1.3) 158 Appendix 8: Port-to-Port connectivity (4.1.4, Sc. 1) 161 Appendix 9: Port-to-Port Connectivity (4.1.4, Sc. 2) 165 Appendix 10: Distance between all ports from selected CEZs and Hazira port 167 Appendix 11: PORT - PORT CALCULATION 168 4 Appendix 12: Port-to-port turnaround time from origin to each destination port in each selected CEZ. 171 Appendix 13: Selected products with their average production quantities (in MT) and total quantities per CEZ for each of the seven CEZs 177 Appendix 14: District-wise three-year as well as average production data in each of the selected CEZs. 180 Appendix 15: Perishability of Fruits and Vegetables 194 Appendix 16: Oversupply calculations for each of the selected CEZs. 195 Appendix 17: Average monthly prices derived across 12 months from three most recent years for viable CEZs. 198 Appendix 18: Wholesale market rates for Coconut in CEZ Suryapur 203 Appendix 19: Total cost of transport from Farm in Malabar to Buyer in Suryapur via short sea shipping 204 Appendix 20: Capacity 40-ft Cargo Container for Coconut 205 Appendix 21: Pilot design 206 Appendix 22: Completed Market Player Questionnaire - 1 211 Appendix 23: Completed Market Player Questionnaire – 2 214 Appendix 24: Completed Market Player Questionnaire - 3 216 Appendix 25: Farm Aggregator Questionnaire 219 Appendix 26: Measurements of the tender coconuts under test implementation 224 Appendix 27: An Analytical Study on Agriculture in Kerala 225 5 Abstract This document is the result of research into the development of short sea shipping supply chains. This research is carried out by Wageningen Food & Biobased Research (WFBR) in 2017 and 2018 in collaboration with the SCA Research and Development in India at the request of The World Bank, who also financed the research The main goal of this project is to exploit efficient new urban short sea food supply chains within emerging economies that significantly reduce urban food losses and improve business profitability. Therefore, this study covers the development and piloting of a methodology for new short sea urban supply chains in India and its local knowledge transfer by capacity building and setting up partnerships that can implement the newly designed urban food supply chain. The documented methodology in this report consists of four steps: 1. The selection of product-route combinations resulting in a table including all, from a cost point of view, potentially feasable short sea shipping routes between coastal economic zones(CEZs), and a selection of fruits and vegetable products which are promising and suitable in terms of quality decay to be transported on these routes. 2. With respect to these product-route combinations, the selection of farmer-market combinations is established resulting in a table with the potentially viable combinations of farmers and market players (both by name or pseudonym). This table quantitatively scores both the market player attractiveness and the match between market player requirements and farmer status showing the most feasible farmer-market combination. At this stage the product label and variety are specified. 3. With respect to these farmer-market combinations, an evaluated supply chain design, (proof-of-concepts) with physical design, logistical control, information and organisation designs determined. 4. With respect to these supply chain designs, a pilot setting is created resulting in a proof- of-principle that reduces the risk and uncertainty of the envisioned implementation of a short sea shipping supply chain. The results of these for steps are: 1. The only product-route combination, that meets all the criteria of the methodology, is the route from CEZ Malabar to CEZ Suryapur for coconuts. 2. The most attractive farmer-market combination is the supply of Tender coconuts from farmer AAR to market player FARMFRESH. 3. Three supply chain design scenarios (ambient, partial cooling, maximum cooling), that differ on physical design only, are feasible from a business perspective. However, the effect of the supply chain design on quality loss could not be determined based on theory alone. 6 4. A test implementation (pilot setting) was performed to be able to eliminate uncertainties with regard to the effect on quality loss. Through the results of the laboratory simulation it was observed that of the three scenarios, partial cooling and maximum cooling are both feasible for the purpose of this study. The ambient scenario showed that the Tender coconuts will not remain in consumable quality till the end of the duration of the supply chain, and therefore is not feasible. With regard to future applicability, the developed methodology can be applied to other coastal areas with a similar scope. Additionally, three types of projects with a smaller scope could benefit from the presented methodology as well: - Operational projects with a specific market player and farmer (group): these projects might be initiated by the private sector that already has a source (farmer, farmer group, region) and the market player (retailer, hotel chain, etc.) in mind. - Strategic projects to improve a country’s food security, food quality or export opportunities: These kinds of projects are usually executed by government bodies. - Other modalities: Although the methodology developed is dedicated to short sea shipping, it can be applied to other modalities as well. In fact, the methodology can be used to realize a new agro-logistical supply chain in general. 7 1. Introduction This document is the result of research into the development of short sea shipping. This research was carried out by Wageningen Food & Biobased Research (WFBR) researchers in 2017 and 2018 at the request of The World Bank, who also financed the research. The researchers involved in this study carried out objective and independent research to realize the project objectives. This Introduction chapter describes the background of the project (1.1), including the project objectives (1.2). Additionally, it presents a working definition of short sea shipping and the – to be designed – methodology (1.3) and a report structure (1.4). Project background To significantly reduce food losses (1.3 billion ton per year), a lot of research describes important barriers to conquer which will improve the well-being and life standard of many 1. Some examples: The World bank lists limited access to inputs; high transport and logistical costs; opaque and unpredictable trade policies; costly and dangerous border crossings; and inefficient distribution services as key barriers to reduce food losses (especially considering smallholders) along the African value chain [2]. Another example is the World Bank’s conclusion: “If every country improved just two key supply chain barriers even halfway to the world’s best practices, global GDP could increase by $2.6 trillion (4.7%) and exports by $1.6 trillion (14.5%)”. 2 Fruits and vegetable losses (harvest to consumption) in emerging economies such as South and Southeast Asia, including India, are estimated to be higher that 50% [1]. Investment in agriculture, improving storage and supply chains is a priority for the World Bank Group to improve food security creating life-long effects on the social, physical, and mental well-being of millions of young people 3. The above facts are not new, but an additional approach to conquer many barriers to reducing food losses at once is: exploit new short sea opportunities to urban food supply chains, in order to increase food security to these urban areas. Many ports in emerging economies are being developed rapidly, making on-time and reliable shipments from one coastal economic zone (CEZ) to another a valuable concept. Once short sea shipping of food between CEZs is implemented, many logistical and infrastructural barriers could be reduced for a substantial part resulting in significant reduction of urban food supply chain losses. Additionally, new markets can be reached to create new business. 2 http://blogs.worldbank.org/endpovertyinsouthasia/global-supply-chain-barriers-lowest-hanging-fruit 3 http://www.worldbank.org/en/topic/foodsecurity/overview 8 Project objectives The main goal of this project is to exploit efficient new urban short sea food supply chains within emerging economies that significantly reduce urban food losses and improve business profitability. Therefore, it covers the development and piloting of a methodology for new short sea urban supply chains in India and its local knowledge transfer by capacity building through setting up partnerships that can implement the newly designed urban food supply chain. A short sea shipping methodology is a tool that facilitates a (potential) business (consortium) in selecting and evaluating a short sea shipping scenario and comparing this scenario with existing supply chains, both on business effectivity and on the effect with regard to the reduction of food losses. Fruit and vegetable production covers a significant proportion of the total Indian fresh food production. Many smallholder farmers are involved in production and food losses are relatively high among these categories. Therefore, this project focusses on short sea shipping of fresh fruits and vegetables in India. The study will be conducted in close collaboration with policy-makers, local authorities, firms and local organizations. Definitions The definitions of ‘short sea shipping’ which are used in the context of this project and report are described below. 1.3.1. Definitions of short sea shipping There is no generally applicable and accepted definition of short sea shipping. Instead, a variety of definitions is in use, often specified for specific (unions of) countries. The European Commission has defined short sea shipping within Europe as follows [3]: The movement of cargo and passengers by sea between ports situated in geographical Europe or between those ports and ports situated in non-European countries having a coastline on the enclosed seas bordering Europe. Short sea shipping includes domestic and international maritime transport, including feeder services, along the coast and to and from the islands, rivers and lakes. The concept of short sea shipping also extends to maritime transport between the Member States of the Union and Norway and Iceland and other States on the Baltic Sea, the Black Sea and the Mediterranean. This definition is formulated by Transport Canada [4]: Short sea shipping involves the movement of cargo or passengers by water over relatively short distances. It can occur within lakes and river systems and along coast lines. It consists of mainly domestic shipping but can also include cross-border traffic (Canada–US–Mexico). It does not consist of shipping across the world’s major oceans. The Ministry of Shipping of the Government of India presents the following definition in their concept note on the Sagar Mala Project [5]. 9 Coastal shipping, short sea shipping, are all terms to describe a method of freight. Coastal shipping, as is most commonly known, is freight movement that happens on the water without crossing a major ocean or leaving a continent. The most widely accepted definition of short sea shipping, by the US Maritime Administration, is as follows [6]: Commercial waterborne transportation that does not transit an ocean and utilizes inland and coastal waterways to move commercial freight. Because of the general nature of this definition by the US Maritime Administration, this definition of short sea shipping will be the basis for the definition used in this project. However, since this project doesn’t include inland shipping, the definition of the US Maritime Administration is adapted in order to exclude inland shipping: Commercial waterborne transportation that does not transit an ocean and utilizes coastal waterways to move commercial freight. 1.3.2. Working definition: short sea shipping methodology A short sea shipping methodology is a tool that facilitates a (potential) business (consortium) in selecting and evaluating a short sea shipping scenario and comparing this scenario with existing supply chains, both on business effectivity and on the effect with regard to the reduction of food losses. The methodology can be applied by any stakeholder that is involved, or ambitions to be involved, in realizing an improved supply chain. Report structure Chapter 2 presents an overview of existing methodologies, their usability and their value related to the goals of this specific project. The literature review shows advantages and bottlenecks related to short sea shipping initiatives and is thereby valuable with regard to additional scoping of this project. Chapter 3 is dedicated to the development of the methodology, including the definition of the criteria which are required to select the most feasible routes and products for short sea shipping. This methodology is applied to India in Chapter 4. A conclusion is shown in Chapter 5. As the development methodology is applied for the first time during this study, a discussion on learnings and opportunities for further improvements of the methodology itself and future applications of it is presented in Chapter 5 as well. 10 One could note that the authors have chosen to split the development of the methodology and its application in two separate chapters, The authors have done so because, it is the expectation of the authors that the readers of this report are most likely interested in either one of them as a stand-alone result. However, if both chapters are of value to a reader, it is recommended to read the relevant subparagraphs of both chapters: 3.1, 4.1. 3.2, 4.2. etc. 11 2. Literature review Auke Schripsema, Han Soethoudt, Seth Tromp This chapter contains a literature overview of methodologies (and parts thereof) which support decisions about the use of short sea shipping for the transport of agri-food products. Very little information was found on short sea shipping methodologies. Most literature sources consider specific aspects of such a potential methodology for short sea shipping. These aspects can be summarized as follows: 1. Requirements of short sea shipping. These requirements are needed in order to overcome the disadvantages of short sea shipping compared to other transport modes such as road and rail; 2. Advantages of short sea shipping. These advantages represent certain benefits of short sea compared to other transport modes. These two aspects of a potential short sea shipping methodology are described in section 2.1 and 2.2 respectively. A decision tool developed by Yonge & Henesey Yonge [7], which contains a list of decision factors supporting or limiting the prospects and opportunities for short sea shipping at an unnamed port in the United States, is described in section 2.3. In section 2.4, a methodology is described that specifically supports the implementation of short sea shipping for the transport of agri-food products. Section 2.5 discusses in what sense the methodologies (and parts thereof) as described in literature contribute to our interest of developing a methodology that supports the application of short sea shipping of agri-food products. We conclude which specific elements (critical success factors and key performance indicators) as described in literature are relevant for the development of a methodology supporting operational decisions about applying short sea shipping for the transport of agri-food products. Requirements of short sea shipping According to literature, in general, the following requirements exist for introducing short sea shipping: a. Availability of infrastructure and equipment; b. Harmonizing administrative procedures and regulations; c. Overcoming the poor image of short sea shipping; d. Provision of short sea shipping service which meets shippers’ needs. 12 These four requirements will be described in detail below, including a summary of the related arguments in literature. 2.1.1. Availability of infrastructure and equipment In order to enable the implementation of short sea shipping, and also to increase its chances of success, sufficient infrastructure and equipment is required, as is described by [8], [9], [10], [11], [12], [13] and [7]. These sources elaborate the following for the required infrastructure and equipment: 1. The width and depth of shipping channels ought to be sufficient for short sea shipping vessels [8]; 2. The dock structure ought to be appropriate and up to date [8] and [9]; 3. Ports ought to have up to date container handling equipment available [8] and [9], including sufficient crane and stevedore cargo handling capacity [10] and preferably roll on roll off ramps [12] to make certain that there is no capacity limitation [11]; 4. Ports ought to be accessible by different modalities [10] and [12], including good access by road [11]; 5. A sufficient number of berths ought to be available at ports [10]to make certain that there’s no capacity limitation [11]; 6. Availability of a computerized management system in ports and electronic transfer of data [12]. 7. The described infrastructure and equipment is ideally specifically dedicated to short sea shipping [12] and [13]. 8. The ability of ports to handle cargo transported by short sea shipping quickly and efficiently [9], [13] and [7]. 2.1.2. Harmonizing administrative and legal procedures and regulations The harmonization of administrative and legal procedures and regulations, both in terms of removing differences between different modalities and different countries or states, is another recurring requirement for short sea shipping. Proposed measures to meet this requirement, as described by [9], [11], [12], [13] and [7] are: 1. Administrative and bureaucratic procedures for short sea shipping, including clearance procedures of cargo, can be complex and time consuming. Administrative barriers ought to be removed in order to encourage short sea shipping [9], [13]and [22], for example by establishing one stop administrative shopping windows [12]; 2. Regulations can be inconsistent between states and countries. These inconsistencies ought to be reduced, and preferably removed altogether, in order to make short sea shipping a more attractive transport option [9]. This can be achieved by an integration of border crossing systems and establishing a multi-national jurisdiction environment [7]; 3. Customs procedures are often lengthy for goods transported by means of short sea shipping and ought to be reduced [11], which can be achieved by implementing automation of customs and immigration security systems [7]; 13 4. More restrictive regulations for road transport to make it less competitive to short sea shipping, for example by restricting the number of driving hours and increasing or introducing road haulage taxes [13]. 2.1.3. Overcoming the poor image of short sea shipping In literature are mentions of the image of (short sea) shipping as slow, unreliable, obsolete [6] and old-fashioned [12]. This poor image might be improved by providing better statistical data on the performance of short sea shipping, by developing new marketing and commercial approaches [13], and raising awareness of the available series [7]. 2.1.4. Provision of short sea shipping service which meets shippers’ needs Shippers require continuity, stability, variability and door-to-door service with regard to transport services. These requirements have to be met, or shippers have to be selected for whom these requirements don’t strictly apply, in order to make the implementation of short sea shipping successful [14] [11]. Advantages of short sea shipping Advantages of short sea shipping refer to the benefits which can be achieved by implementing short sea shipping compared to alternative transport modalities. Throughout literature, these are the advantages which are contributed to short sea shipping: a. Decreased environmental impact; b. Better utilization of infrastructure; c. Potential cost efficiencies; d. Coastal economic development. These advantages will be described in detail below. For each advantage, the relevant arguments in literature will be summarized. 2.2.1. Decreased environmental impact Among others [6], [11], [12] and [7] write about the environmental benefits of short sea shipping and the reduced environmental impact of transport it can accomplish: - Short sea shipping relies on ships rather than trucks; Ships emit significantly less greenhouse gasses per ton-kilometre, are more sustainable and require less energy. Because of this, short sea shipping is more energy efficient and reduces air pollution compared to other transport modalities [6], [11] and [12]. - Short sea shipping also reduces noise by utilizing ships rather than trucks [6]. - By reducing congestion on roads, short sea shipping indirectly also reduces the greenhouse gas emissions and energy consumption of remaining road transport [6] and [7]. - Due to decreased environmental impact, short sea shipping can fit in with the corporate social responsibility of companies [6]. 14 2.2.2. Better utilization of infrastructure Because short sea shipping utilizes the sea rather than (rail) roads which have to be specifically constructed, this leads to the following advantages as described by [6], [12] and [7]: - Expansion of transportation network capacity with few infrastructure costs [6], [12] and [13]; - Reduction of pressure on other modalities [12]; - Potential development of peripheral regions [13]. 2.2.3. Potential cost efficiencies As described by [15], [16], [11] and [12], short sea shipping provides potential cost advantages. Especially on longer distances, short sea shipping has been shown to be less expensive than transport by road or train, although this cost effectiveness depends on the exact locations and distances along which goods are transported. 2.2.4. Coastal economic development The efficient hinterland connectivity and port led development enable coastal economic development. This includes ship repair, ship building clusters, ship breaking industries, bunkering facilities, container freight stations, dry ports and warehousing facilities. Systematic development of these facilities around ports would power economic growth in the coastal economic region [5]. A decision tool for the implementation of short sea shipping Yonge and Henesy [7] have developed a decision tool for the implementation of short sea shipping. It places weights and scores upon a list of critical decision factors that may support the initiation of a short sea shipping project. A comparison between the current and the future probability of success indicates the potential to improve towards a successful short sea initiative. The list of 20 critical success factors is provided in Table 1. A distinction between requirements on the one hand and advantages on the other hand is presented. Additionally, a distinction between the different types of requirements (infrastructure, administration, image and service) and different advantages (environmental, infrastructure and cost efficiency) that can be distinguished in the former sections is shown in Table 1. The decision tool calculates the probability of success of a short sea initiative by calculating the overall weighted average score on these critical success factors. 15 Table 1: critical success factors for initiating a successful short sea initiative Aspect Type Critical success factor 1. Integration into multimodal transport chains or networks; "just in time"; hinterland Infrastructure 2. Stimulation of new maritime transport technologies 3. Minimize restrictive labour regulations 4. Removal of administrative barriers 5. Integration of border crossing systems 6. Automation of customs / immigration security systems 7. Improvement of transparency in ports, related to tariffs and state Administration aids 8. Minimize administrative barriers because of rather complex documentation and procedures in ports and the veterinary checks Requirements 9. Minimize delays in ports 10. Improvement of image of short sea shipping, increase awareness Image of full range of service 11. Creation of reliable statistical market data on existing land transportation, both for commercial development and policy making 12. Improve the regularity of services because of trade imbalances Service 13. Improve performance by increasing speed, reliability, quality of service and cost-efficiency, improve competitive pricing 14. Decrease susceptibility to inclement weather conditions 15. Lower energy consumption and better environmental performance in terms of pollution and safety Environmental 16. Reduced road congestion 17. Increase of the transportation capacity Advantages 18. Potential contribution to development of peripheral regions Infrastructure 19. Positive effect on the development of the other sectors such as the port sector and the shipbuilding industry 20. Expansion of capacity with few infrastructure costs Cost efficiency 16 A short sea shipping methodology for comparing road and short sea transport of agri-food products Perez-Mesa et al. [17] discuss the benefits of implementing short sea shipping in intermodal transport for fruits and vegetables, with a specific analysis of horticultural exports from southeast Spain. A multi-criteria decision-making model is applied to determine the optimal allocation between land and intermodal transport, including environmental externalities. Currently land transport accounts for almost 100% of the export flow of fruits and vegetables from southeast Spain. The authors observe a trend that each retail chain will contract a few suppliers that guarantee volume, quality, range, uninterrupted service, food safety and traceability. This will provide a stability of sales, which creates an opportunity for short sea shipping. Ro-ro 4 maritime transport has been selected for implementing short sea shipping, as this system is flexible and better suited to the transport of perishable produce [17]. The most relevant result is that the cost of intermodal transport is 14% lower than land transport, but that the total transit time for intermodal transport is almost twice that for land transport. The decrease of the total cost does not compensate the increase of the total transit time. The number of quality claims is expected to increase, both due to real quality losses and due to demanded discounts for damaged shipments which actually are undamaged, when prices at the destination fall brusquely. Summary Yonge and Henesy [7] have developed a decision tool for supporting the initiation of a short sea shipping project. This decision tool was explained in section 2.3. A benefit of this tool is that the list of critical success factors is a rather generic list, that can be used for different short sea studies as well. However, some disadvantages exist as well. First, the decision tool calculates a simple weighed score, where the distinction between requirements and advantages is neglected. This seems strange because requirements represent necessary conditions, such as the success factor ‘demand’ is called a ‘must have’ for any project according to Yonge and Henesy [7]. Therefore, requirements seem to be more important than advantages, which are (only) ‘contributing conditions’. The decision tool does not make this distinction. Second, the tool was developed at a high level. Although the authors suggest that the tool can be adjusted and perfected to consider lower-level weightings and to apply it to other industry stakeholders in determining their considerations to initiate a short sea shipping project, this will demand elaboration in detail of indicators and evaluation. Hence also much additional information needs to be collected. 4 Roll-on-roll-off, loading and unloading on wheels 17 Third, the decision tool is not specifically focussed on agri-food products, for which shelf life is a critical element of the decision-making process. Agri-food products are expected to demand specific critical success factors. Fourth, the methodology is restricted to an absolute evaluation of some SSS design. It does not involve comparison to other modalities and hence does not support an investor in decision making in an optimal way. Another methodology on short sea shipping is presented in Perez-Mesa et al. [18]. They apply multi-criteria decision-making techniques to determine the optimal allocation between land and intermodal transport, including environmental externalities, with a specific analysis of horticultural exports from southeast Spain. This was explained in section 2.4. The multi-criteria decision-making model covers both costs (including externalities) and transit times, which are both key performance indicators when shipping agri-food products. However, such a multi-criteria decision-making model does not fully cover the need for a methodology that supports the shipper’s operational decisions whether to apply short sea shipping for transporting a specific agri-food cargo to a specific consignee: first, it starts from an available port infrastructure that is suitable for short sea shipping of perishables. This is understandable in Europe but not in countries with emerging economies in Africa or Asia. Port selection depending on the match between infrastructure and supply chain requirements is part of our envisioned methodology. Second, the envisioned methodology should be flexible in the context of market requirements that need to be met. In Perez-Mesa et al. [18] they are assumed to be static and high-end. Third, truck transport in Europe cannot be compared to truck driving in emerging economies in Africa or Asia. State border issues, roadblocks, bad roads, toll, etc., need to be addressed in the envisioned methodology. From Perez-Mesa et al. [18], the following elements, that are important when developing a short sea shipping methodology with respect to agri-food products, were identified: - a stability of sales between shipper and consignee increases the opportunity for short sea shipping; - ro-ro maritime transport is flexible and better suited to the transport of perishable produce; - the total transit time for intermodal transport is probably (much) higher than that for land transport; - an increased total transit time is expected to have negative consequences for product losses and quality or discount claims. 18 Both methodologies described do not incorporate product specific requirements or restrictions of fresh produce. The quality level required at market level has impact on the lead time options and/or necessary technology investment (costs) to guarantee product quality. Another issue are the investment costs. The two methodologies described include operational costs only, whereas some investments might be needed as well. Additionally, the need for regulatory reform might exist as a prerequisite to implement a short sea shipping solution. There are a variety of papers which describe the requirements and advantages of short sea shipping (see sections 2.1 and 2.2 respectively). The requirements are: a. Availability of infrastructure and equipment; b. Harmonizing administrative procedures and regulations; c. Overcoming the poor image of short sea shipping; d. Provision of short sea shipping service which meets shippers’ needs. The advantages in the literature review described are: e. Decreased environmental impact; f. Better utilization of infrastructure; g. Potential cost efficiencies. Since none of the described methodologies (or parts thereof) include a tool that facilitates a (potential) business (consortium) in selecting and evaluating a short sea shipping scenario and comparing this scenario with existing supply chains with regard to both business effectivity and the reduction of food losses, the development and subsequent piloting of such a methodology is carried out in this project. 19 3. Methodology development Auke Schripsema, Han Soethoudt, Seth Tromp This project aims to develop a methodology to evaluate short sea shipping scenarios in terms of business effectivity and reduction of food losses. In order to achieve this, the envisioned methodology consists of four distinctive phases: A. Selection of product-route combinations; B. Selection of farmer-market combinations; C. Supply chain design; D. Test implementation. Each distinctive phase follows a process that results in input for the next phase. An overview of the processes and results are shown in Figure 1 Figure 1: Evaluation processes & results of each phase in terms of business effectivity & reduction of food losses Selection of product-route combinations The first phase of the methodology is dedicated to select the product-route combinations (PRCs) which offer potential for business profitability and the reduction of food losses. A route is defined as a pair of two different CEZs. These CEZs determine the region of both origin and destination of the fresh product which will be transported by means of short sea shipping. The product-route selection consists of these aspects: 1. Route selection 2. Product selection 3. Initial feasibility check 20 The selection processes of routes and products are described in section 3.1.1 and 3.1.2 respectively. The initial feasibility check, considered from a cost point of view, is described in section 3.1.3. The end result of this phase of the methodology is a table including all potentially viable short sea shipping routes between CEZs, and a list and ranking of fruits and vegetable products which are promising, with regard to business effectivity and the reduction of food losses, and suitable to be transported on those routes by means of short sea shipping. In the output table, pairs of CEZs (origin and destination) are listed including per combination the products that have potential. This table is the basis for the second phase of the methodology, in which suitable farmer-market combinations will be selected. 3.1.1. Route selection A ‘route’ is defined as a pair of two different CEZs. These CEZs determine the region of both origin and destination of the products which will be transported by means of short sea shipping. The route selection is divided into four separate topics, namely: 1. Identification of CEZs 2. Determination of port connectivity between CEZs 3. Attractiveness of CEZs for multi-modal transport 4. Existing shipping line connectivity These topics, and the related criteria, are elaborated below. 3.1.1.1. Identification of CEZs The purpose of the identification of CEZs is to provide an overview of all existing potential regions of origin and destination for short sea shipping of products in the relevant country. For example in India, a coastal zone (for example one or more districts) is a CEZ if it fits the following three criteria, as defined by the Ministry of Shipping of the Government of India [19]: a. The region has a coastline with a length between 300 and 500 kilometres. b. The region includes an inland area between 200 and 300 kilometres. c. The region includes one, two or three seaports. In the next steps, this complete list of CEZs will be narrowed down to include only those CEZs which are viable for the implementation of short sea shipping of fruits and vegetable products. 3.1.1.2. Determination of port connectivity between CEZs After all potential CEZs have been identified, the next step of the methodology consists of selecting only those CEZs which have sufficient port connections to one another to ensure maximum flexibility on potential short sea shipping routes. This starts by identifying the ports which are part of the defined CEZs. 21 The following two criteria are added, which are based on the judgment of the authors of this report: d. The number of shipping lines servicing the CEZ is at least two. e. There is at least one shipping connection to other ports. These criteria ensure that there is at least minimal connectivity to other ports, and that a replacement connection can be arranged in case the connection of first choice can’t be utilized. The CEZs which don’t include ports that meet both criteria regarding port connectivity will be excluded from the remainder of the selection procedure. 3.1.1.3. Attractiveness of ports in CEZs regarding multi-modal transport of fruits and vegetables This list of potentially suitable ports for short sea shipping is narrowed down further by applying criteria regarding the attractiveness of the ports for multimodal transport. These criteria can refer to Ro-Ro (‘roll on, roll off’) or reefer transport. In the future, once the concept of short sea shipping is potentially rolled out, a large quantity of Ro-Ro’s and/or reefers need to be shipped. With regard to the nature of farming in emerging economies (usually many smallholder farmers 5) and in order to realize large volumes (multiple containers per week), it is expected that the short sea shipping supply chains will involve combining produce of multiple farmers at a collection point. Therefore, the criteria regarding the attractiveness of CEZs for multi-modal transport are based on these assumptions: f. The transport from farm to collection point might lead to mechanical product damage, hence sorting should be done at the collection centre and from there produce is transported to the port. The criterion is: The length of unpaved roads between collection centre and port is less than 5 km 6. g. We assume that along the coast most vessels transporting containers are feeder type and carry between 500 and 1500 containers 7. Based on container traffic statistics 8 the average number of feeder vessels per week in the port can be calculated. The number of feeder vessels per week in the port should be at least one. h. Availability of cranes, trailers, side loaders and carriers (in the case of container transport) or ships should have a ramp (in the case of Ro-Ro transport). i. Depth of the port and the waterway leading to the port of at least 10 meters 9. j. Availability of power supply units for refrigerated containers in the port or at the nearby the container freight station. Note that for Ro-Ro transport the port has no restricting characteristics. 5 Further discussed in paragrapgh 3.3.4 6 Note that the collection point might as well be close to the port, but anyhow we assume that produce not meeting the quality requirements is not shipped. 7 https://en.wikipedia.org/wiki/Container_ship 8 http://ipa.nic.in//showimg.cshtml?ID=217 9 https://people.hofstra.edu/geotrans/eng/ch3en/conc3en/containership_draft_size.html 22 3.1.1.4. Existing shipping line connectivity The final restriction for route selection is the presence of shipping routes. A route between a CEZ of origin and a CEZ of is destination is only considered a viable route in this methodology when it is currently served directly by at least one shipping line. 3.1.2. Product selection In order to select the types of fruits and vegetables which have the best chance of being successfully transported by short sea shipping, the following criteria are applied: a. Most produced products per selected CEZ; b. Sufficient shelf life; c. Oversupply in the CEZ of production; d. Undersupply in one of the other selected CEZs. These criteria are described below in more detail. The product selection phase ends with a shortlist of fruits and vegetable products, with sufficient shelf life and guaranteed demand, of which there is an abundance in one CEZ with a well- connected port and a shortage in another CEZ with a well-connected port. 3.1.2.1. Most produced products per selected CEZs The ten most produced types of fruits and vegetables should be identified (in terms of annual quantity in tons produced) for each of the selected CEZs from the route selection phase as described in the former section, including the months of the harvest season 10. As a production top 10, by default the fruits top 5 and vegetable top 5 is taken. To identify this top 10 in the CEZ, statistical data should be available on such a regional level that the CEZ can be built up by a collection of regions. For example, in India CEZs constitute of a collection of districts, and production data are available on district level. In case the CEZ consists of more than one district, the total production of all districts in the CEZ is calculated for all fruits and vegetables available. Subsequently, the ranking takes place only over the CEZ as a whole. Note that it’s possible that some products are produced in one district and not in another. In this case, zero can be put in the production table for this district before calculating the totals for all products. 10 For some districts less than five fruit or vegetable products might be produced. In these cases the list will be shorter. 23 Once the top 5 for fruits and for vegetables is determined, an extra column is added to the result table with the months of harvest. This is necessary since later on, when the products with oversupply are derived, wholesale prices will be compared on monthly level between the sourcing area and potential destinations and obviously this can only be done if the product is harvested. To avoid outliers (e.g. bad climatological conditions in some particular year) the average of the production data available over the most recent three years available is taken. If the data are available for two years, one takes the two-year average, but if only one year of data is available it is necessary to check whether this year is an outlier in the trend. To do so, the data for this particular product are considered over most recent three years on state level for the state the district belongs to. If this particular year where the district data are available is no outlier in the state-wise annual production, one can use this one data point for the analysis. However, if this year is an outlier, the product is deleted from the list and the next one in order is added. Note that at this moment different product varieties or labels (e.g. ecological) are not taken into account yet. During the match analyses (section 3.2.3), market players and farmers will be evaluated on product varieties and or labels though. 3.1.2.2. Sufficient shelf life The list of top 10 produce is further narrowed down based on shelf life. The shelf life should be sufficient given the expected transit time to transport the fruits and vegetables for every combination of CEZs resulting from the route selection. The shelf life of a product restricts the options in the sense that the logistic throughput time cannot be too long. In this step, the shelf life of all top 10 products will be listed for ambient as well as cold temperature conditions. In combination with throughput time calculations some opportunities will be deleted. When products arrive at the port for sea transport all kind of processes can take place like administrative procedures, phytosanitary check, waiting time for loading or unloading, etc. We call these ‘Processes and Documents on Departure’ (PDD) and ‘’Processes and Documents on Arrival’ (PDA). We assume that the lead time between farm and the logistic hub at the port is one day. Doing so the throughput time from farm to buyer is determined, and it is done in a product- independent way. The on-land throughput times are independent of the connection between two ports. 24 3.1.2.3. Oversupply in the CEZ of production The list of suitable products is further narrowed down based on oversupply (so risk of having food losses is mitigated) in the CEZ where the relevant product is produced. This is checked for each product on the list. Therefore, three kinds of data are required for every product: i. Population in every district within the CEZ (latest data available) ii. Average of production data available over the most recent three years available in these districts iii. Average monthly consumption on a national level (latest data available) To calculate i, the population of all the districts in the CEZ are added to the CEZ total. The latest data available are used, only one year. In order to support a sustainable short sea supply chain, in step ii the average production data available over the most recent three years available will be calculated, including the months of harvest. Data of the most recent three years are necessary to reduce the risk of considering an exceptional period (for example because of extreme weather conditions) that influences the production data significantly and might lead to incorrect (un)selection of the considered produce. These production data are available from 3.1.2.1. The production per capita per month in harvest time is compared to the average consumption per month on national level, to check if there is an oversupply. It is called a case of oversupply if the annual production per capita per month in harvest time is at least twice the average monthly consumption. 3.1.2.4. Undersupply in one of the destination CEZs The product list is further narrowed down based on undersupply in one or more of the selected destination CEZs. In this methodology, undersupply is related to the wholesale market prices. For the sourcing CEZ as well as the destination CEZ the average monthly wholesale prices available over the most recent three years are determined, based on a large wholesale market located in one big city (more than 1 million population) within 200 km to 300 km 11 from the production area (sourcing CEZ) and the port (destination CEZ). Three years of wholesale market prices are necessary based on the same reason as for the production data (3.1.2.3). For example, in India, ‘undersupply’ could be defined as cases with a monthly-average difference between sourcing CEZ and destination CEZ s of at least 1,000 INR / quintals 12. By taking such a high threshold value, it is assumed to obtain only one or two feasible cases. Remark: note that undersupply is not really the driver for business here. The price difference between the wholesale markets is key to the opportunity. Nevertheless, the assumption is undersupply and relatively higher prices are correlated. 11 Note that the city could be in another CEZ. 12 1 quintal = 100 kg 25 3.1.3. Initial feasibility check Finally, the product-route list is further narrowed down based on financial feasibility and competitiveness to road transport. Therefore, in this section, the feasibility of short sea shipping is considered from a cost point of view. In addition, the competitiveness in relation to trucking is carried out with respect to costs and lead time. 3.1.3.1. Financial feasibility The financial feasibility of short sea shipping should be determined by comparing the prices of the relevant product for both the wholesale market where it’s currently sold and the new wholesale market where the product is potentially sold instead. In order to make the change to a new wholesale market feasible in terms of costs, the additional revenue which is earned by selling at a new market must be larger than the increase in logistical costs of shipping the container with these products to this new market. Total logistics costs of short sea shipping are collection transport from farmers, all costs relating to shipping and documents, and inland transport from port to major city. This should be compared to the transport costs of directly trucking from the sourcing area to the current wholesale market (nearest big city). 3.1.3.2. Competitiveness Cost and lead time have been calculated for the supply chain involving short sea shipping. These two indicators should be researched for trucking as well and be compared. Eventually some indications on percentage-wise losses can be found in literature. It is expected that truck transport leads to more mechanical product damage than short sea shipping. Selection of farmer-market combinations The starting point for selecting suitable farmer-market combinations are the product-route combinations as the result from the product-route selection (see 3.1). A product-route combination is a combination of a product and a route, where a route is defined as a pair of different coastal economic zones (CEZs). These two CEZs determine the (foreseen) regions of origin and destination for the involved product. So, the product-route selection has shown that it is promising to apply short sea shipping to transport the identified product from the origin CEZ to the destination CEZ. A farmer-market combination (FMC) is defined as a combination of a farmer (group) with regard to the product under consideration (incl. variety and label) within the CEZ of origin, and a market player(s) (customers such as retailers, restaurant chains and hotel chains) within the CEZ of destination. Within a farmer-market combination, farmers and market players are mentioned by name or pseudonym. 26 The selection of suitable farmer-market combinations consists of three aspects: 1. Market research; 2. Farmer research; 3. Match analysis. The interdependency between market research, farmer research and match analysis is visualized in Figure 1. These interdependent aspects of the phase of selecting suitable farmer-market combinations are elaborated in section 3.2.1, 3.2.2 and 3.2.3 respectively. The end result of the farmer-market selection is a table with the potentially viable combinations of farmers and market players within the selected CEZs, with respect to the selected product. This table quantitatively scores both the market player attractiveness and the match between market player requirements and farmer status. The highest combination of these two scores is to be considered as the most feasible farmer-market combination, as the market player attractiveness is large, and the farmer status meets the market player requirements most. This output is the basis for the supply chain design. Selection of farmer-market combinations Market research Farmer research Identification of market players Identification of farmers Determination of market player attractiveness Determination of farmer status Determination of market player requirements Match analyses Identification of ‘best matches’ Discussion of qualitative / soft aspects Most feasible farmer-market combination Figure 2 Interdependency between market research, farmer research and match analysis. 27 To illustrate the expected output of the farmer-market selection, a hypothetical table showing a single product variety and label is shown below (Table 2). The figures shown are fictional. In this example, the farmer-market combination of Mr. Mango-Mango Juice Production Company Z is scored most feasible. This is the case because the Mango Juice Production Company scores a high market attractiveness, and there is a high match between this market player and the farmer Mr. Mango. The approach to calculate the figures will be described later in section 3.2. Table 2 A hypothetical output table presenting on the one hand the market players with a high market attractiveness and on the other hand the estimated matches between 4 farmers and these 3 markets Organic Keith Mango Market players → Online retailer Hotel Chain Y Mango Juice X production company Z Market attractiveness (%) 75 79 79 Farmers ↓ Match between farmer and market player (%) Mr. Mango 20 51 89 Mango farmers united B.V. 30 35 12 Mango Delicious Company 85 78 66 The Mango Start-up 12 22 18 3.2.1. Market research Market research is divided into three separate topics as shown in Figure 2, namely: Market research Identification of market players Determination of market player attractiveness Determination of market player requirements Figure 3 Market research The identification of market players (3.2.1.1) results in a list of potential customers (market players). Determination of market player attractiveness (3.2.1.2) results in a split up of this list: market players that are not attractive and will no longer be taken into account. Market players that have a sufficient attractiveness will be part of the study that results in market player requirement listing (3.2.1.3). 28 Both the market player attractiveness score and the listed market player requirements will be input for the match analysis. 3.2.1.1. Identification of market players The purpose of the identification of market players is to provide an overview of potential customers (retailers, fast-food chains, etc.) within the CEZ of destination. To realize the objective of a reduction of food losses, these customers should be willing to and capable of being a part of a long-term partnership in the supply chain using short sea shipping transportation. The potential of a partner (group) increases if their mind-set is on: - Buying sufficient volume for short sea shipping: minimal one 20-foot container per order; - Setting up long-term partnerships with the famer to jointly realize more benefits of added value (=market driven supply chain), instead of buying whatever is on the open market; - Selecting suppliers based on quality, reliability and price, instead of only on price; In order to identify such market players, two approaches exist: 1. Use the network of a local project partner for consulting market players for interest in additional suppliers (e.g. off-season demand) of product(s) bought elsewhere currently, or new suppliers of product(s) that are not in their buying portfolio yet. Additionally, the willingness to source through short sea shipping transportation needs to be present. 2. Consult wholesale markets in urban areas around metropoles: In the urban area of the wholesale markets, retailers, foodservice companies and other outlet organizations can be identified by interviewing wholesale market representatives. The gathered ‘leads’ can be used to contact market players for interest in additional or new suppliers that can deliver through short sea shipping transportation. In contrast to most earlier steps in this methodology, the market-player identification is not aiming at realizing an all comprehensive list of all existing market players. The aim is to find sufficient market players to be able to select promising farmer/markets during the match analysis (3.2.3). In the next step, this list of market players will be narrowed down to include only those market players which are attractive in general terms. 3.2.1.2. Determination of market player attractiveness in general After potential market players have been identified, the next step of the methodology consists of determining the attractiveness of these market players in general terms. In order to achieve this, these market players will be interviewed 13. The general market player attractiveness is assessed by relevant attractiveness criteria (example in Table 3), such as: 1. From what type of supplier does the market player usually source his products? 13 Chapter 4 refers to appendices showing both interview questions and results 29 2. Which are the current expected sales and purchase prices of the considered product (the product selected during the product-route selection)? 3. During which calendar period is the considered product currently sourced by the market player? 4. Which sales trend exists during the sourcing period? For example, significantly increasing, slowly increasing, stable, slowly declining or significantly declining. 5. If relevant, how is the market player’s attitude towards the considered product as a new product to its assortment? 6. What is the estimate of the volume of the considered product that the market player expects to sell in a specific time period (month/week)? 7. How does the market player want to pay the purchases price to his supplier(s)? 8. What type of logistical information is currently shared by this market player with his suppliers? To be able to score the overall market player attractiveness next, the following steps take place (example in Table 3): 1. Determine the range of possible answers to each criterion; 2. Arrange these possible answers with respect to attractiveness; 3. Assign a score to each possible answer; 4. Assign a weight to each criterion; 5. Perform the market player interviews 14 and collect the answers; 6. Score the answers from the interviews and determine the general attractiveness of each market player (weighed score sum). At step 5 the market players are interviewed, such that to each market-attractiveness criterion one of the possible answers is attributed. In order to determine the overall attractiveness of the interviewed market players, the weighted sum of all criterion scores is calculated at step 6. An example of the outcome of step 6 is the following table (Table 3), which is the hypothetical result of having interviewed three market players. Note that next to the overall market player attractiveness score, also the data completeness is relevant in selecting the most attractive market(s). Each row on the left represents one market player attractiveness criterion. The possible answers are arranged with respect to attractiveness, and to each possible answer a score is assigned. The range needs to be the same for all criteria (0-4 in the example of Table 3). To each criterion a weight factor is assigned. The three columns on the right cover three market players (Mr Mango, Mango Farmers United B.V., Mango Delicious Company) for which, where possible, the relevant criteria were scored. Finally, the total weighed score is calculated for each market player (‘total’). This total score is compared to the maximum possible score given the available answers (‘score’). Moreover, the percentage of assessed criteria is calculated for each market player (‘completeness’). 14 Chapter 4 refers to appendices showing both interview questions and results 30 Only those market players which are considered to be sufficiently attractive are selected for the next step: determination of the market player requirements. Table 3 Determination of the overall attractiveness of the interviewed market players Mango Farmers United B.V. Mango Delicious Company Market Characteristics Mr Mango weight local market online retailer trader APMC farmer sourcing stakeholder 1 3 3 4 0 1 2 3 4 < market price market price constant price average price > market price sales / purchase price 3 1 0 1 2 3 4 not applicable (n.a.) (year round supply) sourcing period n.a. n.a. n.a. n.a. n.a. decreasing fluctuating stable increasing trend 1 0 1,33 2,67 4 none one > one like to add product 1 0 2 4 < 100 kg 100-200 kg 200-500 kg 500-1000kg >1000 kg volume estimate/wk. 1 4 0 1 2 3 4 online - 7 days cash - 7 days online - direct cash - direct advanced process of payment 3 0 3 4 0 1 2 3 4 none rejects indent order planning information sharing 2 1 4 0 1 2 3 4 total 5 12 31 maximum 24 16 40 score 21% 75% 78% completeness 43% 29% 71% 31 3.2.1.3. Determination of market player requirements In order to determine the requirements of these market players towards their potential suppliers, the interviews with the market players (3.2.1.2) cover the assessment of relevant market player requirements as well. For example: 1) Product variety and label (e.g. organic) 2) Product requirements, such as: a) Firmness b) Shape c) Product packaging d) …. 3) Delivery acceptance 4) Delivery frequency 5) Product requirements such as: a) The way of transportation to the market b) Transport packaging An example of the outcome is the following table (Table 4), which is the hypothetical result of having interviewed one particular market player, who is interested in the considered product ‘organic vine tomato’: Table 4 Examples of market player requirements Issue Market player requirements 1. Product variety and label Organic vine tomato 2. Product requirements a. Firmness High b. Shape Round (not oval) 3. Delivery acceptance Rejects what is not ok 4. Delivery frequency 6 to 7 days a week 5. Logistic requirements a. Way of transport to the market player Open truck b. Transport packaging Crate Price check for specific product variety and label For the product involved, an initial financial feasibility check was done in section 3.1.3.1. However, this initial check took place on the general product level, not on the level of the product specific variety and/or label. Therefore, the financial feasibility of short sea shipping should be checked again by comparing the product prices and logistical cost of the relevant product variety and label in the same way as described in section 3.1.2.1 32 The market player requirements are input for the match analyses in section 3.2.3. 3.2.2. Farmer research The farmer analysis is divided into two separate topics as shown in Figure 1 and Figure 3. Farmer research Identification of farmers Determination of farmer status Figure 4 Farmer research These two topics, and the related criteria, are elaborated below. 3.2.2.1. Identification of farmers The purpose of the identification of farmers is to provide an overview of potential suppliers (farmers, farmer groups, etc.) within the coastal zone of origin. The potential of a farmer (group) increases if their mind-set is on: - Selling sufficient (consolidated) volume for short sea shipping: minimal one 20-foot container per customer order; - Selling directly to customers to jointly realize more benefits of added value (=market driven supply chain), instead of selling on the open market; - Selecting customers that focus on quality, reliability and price, instead of only on price; - Setting up long-term partnerships with customers. In order to identify suitable farmer (groups) two approaches exist: 1. Use the network of a local project partner for consulting farmers for interest in additional customers (e.g. retailers, hotel chains, etc.) of product(s) sold elsewhere currently. Additionally, the ambition to source through short sea shipping transportation needs to be present. 2. Consult product related association(s) and gather ‘leads’ that can be used to contact farmers for interest in additional customers that can be supplied through short sea shipping transportation. The aim is to find sufficient farmers to be able to select promising farmer-market combinations during the match analysis (3.2.3). 3.2.2.2. Determination of farmer status For each identified farmer (or farmer group), the current status is determined with respect to the market player requirements as identified in 3.2.1.3. The market player requirements cover criteria as mentioned earlier in paragraph 3.2.1.3 : 1) Product variety and label (e.g. organic) 33 2) Product requirements, such as: a) Firmness b) Shape c) Product packaging d) …. 3) Delivery acceptance requirements 4) Delivery frequency requirements 5) Product requirements such as: a) The way of transportation to the market b) Transport packaging In order to determine the farmer status, the following steps will take place: 1. Perform open interviews (questions below) with the farmers to achieve answers for each market player requirement; 2. If relevant, collect comments about each answer; 3. Fill a table with the answers and comments to each market player requirement. Possible interview questions related to each market player requirement are: 1. Product: Which product variety and label are produced? 2. Product requirements: What firmness and shape are produced? 3. Delivery acceptance: Which types of packaging are currently applied by the farmer? What are the farmer’s expectations when the market players reject (parts of) the delivery? 4. Delivery frequency: What delivery frequency is currently achieved by the farmer? 5. The way of transport to the market: Which way of transport is currently applied by the farmer to the farmer’s market/customers? Which transport packaging? An example of the outcome of these interviews is the following table (Table 5), which is the hypothetical result of having interviewed one particular farmer. 34 Table 5 Example of farmer status determination with respect to the market player requirements Issue Class Comment 1. Product variety and label Regular n.a. 2. Product requirements a. Firmness High b. shape Oval 3. Delivery acceptance Always Delivery to APMC 15 4. Delivery frequency 1 or 2 per week n.a. 5. Logistic requirements a. Way of transport to the market Open truck Pay for transporter b. Transport packaging loose Mostly packed loose, seldom crates Just like the market player requirements, these farmer characteristics are input for the match analysis in section 2.1.3. 3.2.3. Match analysis The match analysis is about determining the match between the market player requirements and the farmer status for each farmer-market combination. Therefore, the market player requirements and the farmer status are input for this third and final aspect of selecting suitable market-farmer combinations. The match analysis is divided into two separate topics as shown in Figure 4. Match analyses Identification of ‘best matches’ Discussion of qualitative / soft aspects Figure 5 Match analysis To illustrate: if the requirements of five market players and the status of five farmers are identified, then 25 (5 x 5) matches will be scored and weighed. The ‘best match’ is the market-farmer player combination with the highest weighed match score. Additionally, the match scores and/or weight factors can be adjusted due to qualitative / soft elements as is explained in the next sections 3.2.3.1. Identification of the ‘best match’ With respect to each market player, the following steps take place for each market player requirement: 1. Determine the range of possible answers for each requirement; 2. If possible, arrange these possible answers with respect to effort, investment etc.; 15 Agricultural Produce Market Committee: general name for a wholesale market in India 35 3. Derive classes of answers, that match with market classes; 4. Fill a table according to this classification. For example, with respect to the market requirement ‘delivery frequency’, this could be as follows: Table 6 The market requirement 'delivery frequency' is elaborated in this table Market requirement example: delivery frequency Action answer Step 1 Determine range of answers (e.g. times per week) 1,2,3,4,5,6,7 Step 2 Order these answers Is done Step 3 Eventually, derive classes of answers (to reduce (1-2), (3-5) and (6-7) times a week the amount of answers) Step 4 Fill table according to classification Fill in the class for delivery frequency Subsequently, the steps to realize a match score with a particular farmer are: 1) For each market requirement, calculate the quantitative difference between the market requirement and the farmer status (market score – farmer score = GAP score); 2) Weigh each market requirement and multiply with the GAP scores to gain the weighted GAP scores; 3) Sum both the weighted GAP scores and the maximum weighted GAP scores; 4) Calculate the total GAP score as a percentage (sum weighted GAP scores / sum max weighted GAP scores); 5) Calculate the match score (100%- GAP). As an example, Table 7 shows this process for FMC: Mango Delicious Company - Mango Juice production company Z: Table 7 Calculating the match scores Issue Market Score Farmer Score GAP Weight Total Max. player weighted weighted GAP GAP Product variety Other 4 Regular 2 2 3 6 12 and label variety Product requirements a. Firmness High 4 High 4 0 3 0 12 b. Shape Round 4 Oval 2 2 3 6 12 Delivery Always 4 Always 4 0 1 0 4 acceptance 36 Issue Market Score Farmer Score GAP Weight Total Max. player weighted weighted GAP GAP Delivery frequency 6 to 7 4 1 or 2 1 3 1 3 4 Logistical requirements a. Way of Open 2 Open 2 0 1 0 4 transport truck truck to market b. Transport Crate 4 Loose 2 2 2 4 8 packaging TOTAL 9 14 19 56 GAP (%) (total 34 weighted GAP / Max Weighted GAP) MATCH (%) 66 (100%-GAP) Finally, a table is composed of which each column represents a market player (and a quantitative estimation of its general attractiveness, as a result from section 3.2.1.2). Each table row represents a farmer. In each cell, the match percentage is provided. ‘Best matches’ are combinations of market players with high general attractiveness and farmers with high match percentages with respect to these market players. An example of the output is the following table (Table 8). Table 8 Example of the identification of the ‘best matches’. ‘Best matches’ are combinations of high attractive market players and farmers with high match scores with respect to the market player requirements of these market players. Organic Keith Mango Market players → Online retailer Hotel Chain Y Mango Juice X production company Z Market attractiveness (%) 75 79 79 Farmers ↓ Match between farmer and market player (%) Mr. Mango 20 51 89 Mango farmers united B.V. 30 35 12 Mango Delicious Company 85 78 66 The Mango Start-up 12 22 18 37 To summarize, first market players are analysed on market attractiveness, and hereafter these market players are matched with farmers. These two scores together (on market attractiveness and on match) determine which farmer-market combinations are considered to be promising, probably together with qualitative aspects (soft elements) as is described in the next section. 3.2.3.2. Discussion of qualitative aspects (soft elements) The farmer, the market player or the project partner that executes this methodology might have some qualitative preferences. These could lead to an adjustment of the ‘best’ matches as occur from the match analysis. Examples of qualitative aspects are: - Trust. Example: in general, a market driven chain requires more volume than one farmer can offer. If so, several farmers should cooperate in order to satisfy the market player. Such a cooperation is based on trust, which is for example violated if one or more farmers decide to sell elsewhere for a higher spot price. - Risk attitude. Example: in most cases investments are required. Sometimes small (like buying plastic crates) and sometimes big (like a storage facility) investments. Farmers act differently at this point and like-minded ones should cooperate. - Ownership/responsibility. Example: what happens if the produce is rejected at the distribution centre of the market player, because of bad product quality? Who is responsible? The truck driver (organization), the shipping line or the farmer? And who owns the product at that time? - Short term vs. long term orientation. Example: in most cases short and long term are related to the return-on-investment (ROI). - Niche-oriented vs. competition. Example: a niche market has a threshold risk when entering the market, but once successful it has in general not much competition in the first part of the life cycle. The growth is slow. In an existing competitive market, volumes tend to be much bigger, but as a consequence the market player requirements are strict, since the market player is depending on it. Based on the match analysis between farmers and market players, and a discussion of the qualitative aspects, the most promising farmer-market combinations are finally selected. So, the end result of the farmer-market selection is a table including all potentially viable combinations of farmers and market players (both by name) within the selected CEZs, with respect to the selected product. This table will be input for methodology phase C: Supply chain design. Supply chain design Starting point of supply chain design are the promising farmer-market combinations. Each promising farmer-market combination is a ‘best match’ between a farmer (group) and a market player, including the consideration of qualitative aspects. For each farmer-market combination a short sea shipping supply chain will be designed, which is feasible with respect to cost and product quality. 38 The supply chain design is about the logistical concept of the future supply chain. It points out logistical aspects to consider during the design of the future short sea shipping supply chain. These aspects are relevant for: 1. Emerging economies in which it is common practice that the majority of farmers are smallholders that are not able to individually ship multiple containers of produce annually; 2. Supply chain actors that have, in contrast to Western economies, none or limited experience with market driven supply chains. Most actors in emerging economies are linked to a wholesale market nearby: farmers push their produce to the wholesale market, buyers buy here as well. The implementation of any short sea shipping supply chain solution will probably be one of the first times that most supply chain actors will face a market driven supply chain. One or multiple supply chain designs (scenarios of the future short sea shipping supply chain) can be developed. Each supply chain scenario will be described according to four levels: the physical level, the logistical control level, the information level and the organizational level. The different scenario(s) will be evaluated afterwards on cost and product quality. To illustrate the supply chain design, the example below is introduced (Table 9). Table 9 Example of a promising market-farmer combination Short sea shipping from Mango Farmers United to Hotel Group The Fresh Experience Hotel Group The Fresh Experience is profiling themselves on the exclusive fresh breakfast services they offer to their premium clients. Unfortunately, the availability of fresh mango of sufficient quality is very scarce during the winter. The current source of fresh mango is from local sources that sell their produce to the local wholesale market. A representative of the hotel group visits the market twice a week to buy the highest quality available. During the local harvest season, the quality is high, and the prices are low. It is the other way around during off season periods (winter). Now, a new opportunity has arisen: a new source, Mango Farmers United, 4500 km away from the hotels, can deliver high quality mango during the winter. Mango Farmers United has a considerable oversupply during winters, with low prices and even the risk of food losses as a consequence. It seems promising to transport these mangos from Mango Farmers United to Hotel Group The Fresh Experience via short sea shipping. Conventional road transport will take three weeks of transit times due to border passing, with serious consequences for product quality. Short sea shipping demands a 1.5-week cold supply chain, as part of which the mangos are to be transported by a 20ft or 40ft reefer container or by a temperature-controlled truck (roll on, roll 39 off). However, it is even unclear whether the mangos will survive a 1.5-week journey in a reefer container. The design aspects at the physical level, the logistical control level, the information level and the organizational level are described in sections 3.3.1, 3.3.2, 3.3.3 and 3.3.4 respectively. After a supply chain is (re-)designed, it will be evaluated by interviews and desk study with respect to cost and quality loss (3.3.5). The end result of the supply chain design is one or more evaluated supply chain scenarios, where each scenario consists of a logistical concept of a supply chain making use of short sea shipping. 3.3.1. Physical design Physical design is about the managed system, so about the supply chain partners and the primary transformation processes they are to perform. The physical design is visualised by the sequence in time of these primary transformation processes, and the actors allocated to these processes. An example is visualized in Figure 5. Figure 6 Physical design: the primary transformation processes that take place within the managed system, and the actors who perform these processes. 3.3.2. Logistical control Logistical control is about the managing system, which aims at realising a certain system output (end products delivered to the supply chain’s customers) by adjusting certain control variables, whilst dealing with non-manageable inputs such as demand. The managing system is about how to control the product flow within the supply chain in an efficient and effective way. Examples of logistical control concepts are: 1. The position of the Customer Order Decoupling Point (COPD): see below for an explanation; 2. Cross docking: Eliminating the storage of products at the market player’s distribution centre by transferring, re-consolidating and distributing products within 24 hours to the market player’s outlets; 3. Continuous replenishment: The inventory of the market player is managed by more frequent and smaller deliveries from the supplier, based on actual sales and forecasted demand. 40 Customer Order Decoupling Point An important characteristic of the logistical control within a supply chain is the extent to which customer orders penetrate the supply chain. The Customer Order Decoupling Point (CODP) separates the part of the supply chain oriented towards customer orders from the part of the supply chain based on planning. Downstream of the CODP the material flow is controlled by customer orders and the focus is on customer lead time and flexibility. Upstream towards suppliers, the material flow is controlled by forecasting and planning, and the focus is on efficiency (usually employing large batch sizes). In our example, the CODP is located at the central storage of the Hotel Group The Group (Figure 6). Here customer orders (orders from the individual hotels) arrive and order picking takes place. The central storage of the Hotel Group places its replenishment orders at the storage of Mango Farmers United (Figure 6). Figure 7 Logistical control: the location of the Customer Order Decoupling Point as part of the managing system. Customer orders for new mangos are send from the individual hotels to the central storage of the Hotel Group. Here order picking takes place. Fresh food supply chains might benefit from the shifting of the CODP upstream. Because of the detection of inefficiencies due to repackaging of products in the supply chain, the information exchange of customer wishes to the farmer group might improve performance. For example, by connecting farmers to customers so that products can be packed directly according to final customer wishes. This might reduce food losses at the customer as well. However, it increases the lead time of the customer order (Figure 7). Figure 8 By shifting the CODP upstream, products can be packed directly according to final customer wishes. 41 3.3.3. Information The managing system takes decisions on the basis of available information. The information system’s task is to register the relevant internal data, partner data and external data and to convert it to control information (Figure 8). Figure 9: Information: Information is collected and shared within the supply chain At the information level, the following design issues are relevant: - Which (transactional data) is relevant to whom? (day, hour, shipment, type, #, etc.) - Who is the owner of this data? o Which system registers this data? o Can external partners access this data? - Does the available data need any processing? - Which decisions need to be made after obtaining the (optionally processed) data? - Can the collection of this data lead in the long term to supply chain optimization, making use of analytical systems (e.g. forecasting, location/allocation decisions)? For example, in case of our example the information design is as follows: 1. Ordering from hotel at the central storage is done by phone. Such an order consists of day, product, and quantity. 2. Ordering from the central storage of the Hotel Group at the farmer group is done by phone as well. Such an order consists of day, product, and quantity as well. 3. The order data are processed and transformed into order picking data, dependent on product availability and priority rules. 4. Based on the order picking data, order picking and distribution take place. 5. Point-of-sale data are collected both at storage of the farmer group and at the central storage of the Hotel Group. Based on these historical point-of-sale data, growing and harvest planning at the farmer group is supported. 42 3.3.4. Organization Organization of fresh food value chains is about how to connect farmers to market players, in order to achieve a market driven supply chain. Basically, one can distinguish between three different agricultural farming systems: 1. Smallholder farming system 16; 2. Medium scale/entrepreneurial farmers; 3. Industrial farmers. This distinction is partly based on the availability of land size for farming, but moreover on the business orientation of farmers. There are 500 million smallholders worldwide, and smallholders represent the majority of farmers in the world, especially in non-western economies including emerging economies. Therefore, the organization of the value chain often falls or stands with organizing multiple smallholder farmers in such a way that a market player can be served effectively. This paragraph describes options and topics to consider during the organizational design of the fresh food value chain. There are very important and distinct differences between smallholders which can be categorized as follows[20]: - Smallholders with less than 1 hectare and an income below the poverty line of $1.85 a day. This is the biggest group of smallholders. They are net consumers of food instead of producers. For them agriculture is their fate rather than a profession by choice. It is the most vulnerable group; - Smallholders with 1-2 hectares, the second biggest group. They are commercially active in loose value chains and have some production surpluses; - Smallholders with more than 2 hectares, only about 7 % of the smallholders. Those farmers are commercially viable with reliable production surpluses, in some cases they are even entrepreneurial farmers. These farmers are active in tight value chains. Table 10 Types of smallholder farmers within emerging economies (adopted from Nico Roozen, Solidaridad, March 2016[20]) Traditional Smallholders Potential to become entrepreneurial farmers a. Subsistence/non- b. Commercial in c. Commercial in tight commercial farmers loose value value chains chains 16 There is no unique and unambiguous definition of a smallholder. Often scale, measured in terms of farm size is used to classify farmers. As a result of this households with less than a threshold size of 2 hectares are often stipulated as smallholders. However, across countries, the distribution of farm sizes depends on a number of agro-ecological and other conditions. (FAO 2010, Policies and institutions to support smallholder agriculture. Committee on Agriculture, 22nd session). 43 Strategy Farming for survival rather Looking for Business orientation than choice diversification Land Size < 1 ha 1-2 Ha > 2 ha Engagement Buyers of food Some surplus of Reliable surplus of food with markets food + cash crops Number of 300 165 35 families (in mn) Business orientation Multiple options exist to organize the connection between smallholder farmers and market players in emerging economies. Four options are described below. In practice, these options are implemented solely or as a hybrid connecting the best characteristics of multiple options. 3.3.4.1. Bilateral relations between individual farmer and market player As one smallholder is in general not able to sell large quantities, this option is not applied often. In case it is applied, this usually involves smallholders with potential to become entrepreneurial farmers or to smallholder farmers that sell a relatively unique crop 17. Advantages - Relatively simple to organize; Disadvantages - Large market players need to setup many bilateral relations with smallholder farmers to obtain the right volume at the right time. 3.3.4.2. Contract farming In contrast to bilateral relations, contract farming formalizes (contracts) the agreements agreed upon. Usually, these agreements relate to a prefixed price, volumes / period, delivery dates and quality. Even financial commitment may be involved of the market player to the farmer before growing. Advantages - Fewer food losses as no crop is planted before financial commitment of the market player; - Financial security for smallholder farmer. 17 For example: specific berries considered as superfoods in Western Economies that are only grown in the himalayas by smallholders due to environmental factors. 44 Disadvantages - Price fluctuation will disadvantage the farmer when prices rise and the market player when prices drop; - Large market players need to setup many bilateral relations with smallholder farmers to obtain the right volume at the right time 18. 3.3.4.3. Lead farmer This option can be applied once a market player has relatively challenging market requirements like large demand and high-quality restrictions (e.g. an overseas export market delivered to by reefer transport). Multiple smallholder farmers deliver directly after their harvest to a (much) larger farmer that is already connected to the market player. In addition to market connectivity, the lead farmer’s role in this value chain is to evaluate whether the quality of the just harvested produce is equal or higher than the market requirements. If so, the smallholder farmer will receive a (contracted) price from the lead farmer that is usually higher than the local price. Hereafter, the lead farmer will sell the produce to the market player. If not, the smallholder needs to sell his produce to any local market player or the lead farmer will buy it for a relatively low price. Once the lead farmer buys the produce, this farmer will sell it to a local market player like for example a juice processor. Additionally, the lead farmer is usually involved in education of the smallholder farmers to enable them to reach the relevant market requirements. This way of working is often initiated by NGOs. Advantages - Enables smallholder farmers to reach lucrative markets offering relatively high prices; - Easy knowledge transfer from lead farmer to smallholder farmers Disadvantages - It might still be difficult to meet market requirements, because a lack of product quality; - Opportunity for a lead farmer to squeeze the smallholder farmers (price-wise); - In the initial phase, preferably a lead farmer with a mind-set on social responsibility needs to be found. 3.3.4.4. Cooperation 19 This option organises smallholder farmers in farmer groups (cooperations). Jointly, one or multiple market players are served. Often, a board of representatives of the farmers is appointed that organizes negotiations with market players, internal farmer education, joint purchase of farmer inputs and joint transport to market players. By uniting, usually power balance is realized between multiple smallholder farmers and relatively large market players. 18 Note that this option is referring to an individual farmer. In practice, also a farmer cooperation can apply contract farming. 19 Cooperations were successful in Westerm economies, but now seem to lose effectivity. In Western economies a shift from cooperations to contract farming can be observed. This leads to relatively lower prices for farmers and more profits for retailers. Consortia that are considering to implement this option are advised to make a more profound study on this matter. 45 Additionally, it provides opportunities for smallholder farmers to gain additional added value by selling to market players instead of middle men who may require provisions. Advantages - Power balance leading to a robust and future proof value chain; - Enables smallholder farmers to reach lucrative markets offering relatively high prices; - Smallholder farmers can focus on their core competence: farming. Disadvantages - Often, consolidated produce of multiple smallholder farmers leads to a batch that has no uniform quality (which often is a market requirement); - Smallholder farmers will face the temptation of selling to a middleman instead of via the cooperation (which might have obligations to market players). To illustrate, with regard to the mango case (Table 9), Mango Farmers United (a cooperative) was founded to be able to jointly deliver mangoes to reach lucrative markets offering relatively high prices. Hotel Group The Fresh Experience (4500 km away) has shown interest in buying mangos from the cooperative, because they face low quality and high prices during their winter. In the farming area, during this period of the year, it is harvest season resulting in oversupply and a drop in prices. The cooperative (one of the described options of organizational design) is enabling Mango Farmers United and Hotel Group The Fresh Experience to set up a lucrative supply chain leading to high benefits for both. Additionally, it leads to reduction of food losses as a solution is found to consume the oversupply in the farmer area. 3.3.5. Supply chain evaluation After a supply chain is (re-)designed, it will be evaluated (by interviews and desk study) with respect to cost and quality loss. This is done in order to check which supply chain scenario is (if at all) feasible with regard to cost and product quality. The two key performance indicators are defined as follows: - Cost: these are defined as the sum of operational cost prices of the chain of post-harvest physical activities (the managed system as defined in 3.3.1) of supplying 1 kg product from the farmer to the market player; - Quality loss: defined as the loss of product quality during the total time between receiving the order at the farmer and product replenishment at the market player (order lead time). This quality loss can be translated into both economic loss due to downgrading and economic loss due to product loss (waste). 46 Please note here that the supply chain evaluation cannot (always) be a quantitative analysis. It might be based on (an) estimation(s) by (product or supply chain) expert(s). Therefore, it might occur that crucial information is lacking (input for the test implementation, chapter 4) or that interview results show a range of expected quality loss instead of exact figures. 3.3.5.1. Cost Operational cost calculation is performed according to activity based costing: 1. Estimate the average order size (kg); 2. List all post-harvest physical activities in the right sequence; 3. Calculate the cost for each activity, taking into account the average order size; 4. Add it all up and divide it by the average order size. 5. Evaluate total costs with regard to business profitability This results in the total cost per kg produce. 3.3.5.2. Quality loss Quality loss can be estimated making use of an estimation of the lead time of each activity and the storage and transport conditions (mainly temperatures). Lead time is calculated according to the following steps: 1. Give the average order size (kg); 2. List all post-harvest physical activities in the right sequence; 3. Calculate the lead time for each activity, taking into account the order size, including waiting time between and during activities; 4. Estimate the storage temperature for each activity; 5. Estimate the quality loss for each activity; 6. Add it all up. The total lead time and the storage conditions of the short sea shipping supply chain need to be such that a minimum acceptable product quality can be guaranteed to the market player. If available, one can work with variations (in lead time, temperature, initial product quality and/or quality-decay model) in order to calculate the probability/fraction of product loss. This process is shown in Figure 9 in which these variations lead to different points in time when the (un)acceptance limit is reached. 47 Figure 10: quality decay model In evaluating food losses in (short sea shipping) supply chains, two aspects need to be taken into account: 1) Market acceptance: does the market player accept arriving deliveries? In other words: does a delivery meet the product-quality requirements that were agreed upon? 2) Economic value decrease: Has an economic value decrease taken place because of product- quality loss? For example: a wholesaler might accept a shipment of mango with some little brown spots on it, knowing that this batch will be sold for a relatively low price or knowing that there is not a single mango available out on the market, so he will be able to make a proper margin. Note here that in successfully implementing a short sea shipping supply chain as a totally new concept in the country to which this methodology is applied, the ‘market acceptance’ is critical. Non-acceptance of a significant part of shipments will lead to a failure of the implementation. A ‘decrease in economic value’ might be accepted but will indicate opportunities for improving the short sea shipping supply chain. The most attractive supply chain scenario in terms of cost and quality will be the input for the test implementation (section 3.4). Test implementation This section is about conducting a test transportation by short sea shipping. Starting point is the supply chain design. A direct implementation of this supply chain design usually comes along with a lot of risks and uncertainty, especially when a new concept, like short sea shipping, is implemented for the first time. The supply chain actors may be located at long distances from each other, which might lead to a barrier with regard to the build-up of trust. Without trust, direct investments by the supply chain actors might be considered a bridge too far. 48 If the concept of short sea shipping is experienced as risky, goes along with uncertainty and needs trust among supply chain actors (which is expected during any first implementation in any country), a test implantation can be organized to validate the supply chain design in practice. This section covers the steps to be taken when organizing such a test implementation: 1. Scope; 2. Test implementation team; 3. Measurement protocol. These steps are described in section 3.4.1, 3.4.2 and 3.4.3 respectively. 3.4.1. Scope The test implementation will demonstrate the possibility of physical distribution (against acceptable costs and quality loss) from farmer to market player, making use of short sea shipping. It focusses on a demonstration of the concept, to show its potential to the supply chain actors. Additionally, as part of the methodology, the test implementation focusses on needs for test implementations as concluded from the supply chain evaluation (3.3.5) which was done in order to conclude which scenario is most feasible for implementation. During the supply chain evaluation, data is collected, assumptions are made, and analyses are performed considering the scenarios set in the supply chain design. However, the supply chain actors (farmer group, market player, logistic service provider) that will finally implement the short sea shipping supply chain might feel uncertain on specific aspects. Therefore, it can be decided that these aspects are tested during a test implementation. 3.4.2. Test implementation team The ambitioned supply chain actors of the supply chain design are not necessarily the actors that need to execute the test implementation. In practice, a consortium can agree to limit the amount of supply chain actors. For example, with regard to the Mango example (Table 9), the ambitioned final implementation after a successful test implementation might involve reefer transport of 4500 km, while the test implementation is a test in a laboratory simulation of this supply chain. This means that the test implementation does not require the involvement of the logistical service provider. Additionally, as usually multiple stakeholders might be cooperating for the first time, it is advised to document the roles and responsibilities. 3.4.3. Measurement protocol The test implementation is to demonstrate specific aspects of the supply chain design as agreed upon by the supply chain actors involved. To execute the test implementation, it is advised to define Key Performance Indicators (KPIs) first, describe the way of measuring these KPIs in detail second and plan measurements last. Combined, this forms the measurement protocol. 49 With regard to physical distribution, KPIs relate typically to costs and quality loss, the amount of food losses (e.g. in kg), market player acceptance (e.g. yes or no), and consumer experience (e.g. excellent, average, low, unacceptable). The measurement planning is usually by day during the entire test implementation. Day Measurement 1 1 2 2 3 none 4 1,2 50 4. Methodology application Fruits and vegetables comprise a substantial share of the total Indian fresh food production. A large number of small Indian farmers are engaged in this production, and it has been noted that their corresponding food losses are estimated to be upward of 50 per cent [1]. This study explores short sea shipping opportunities for urban food supply chains in India to help identify possible obstructions in its agro-food logistics system. Utilising more efficient urban short sea food-supply chains is expected to: i. significantly increased profitability of farm produce for the farmer; ii. reduce food prices in urban coastal areas for the consumer; iii. reduce food losses in post-harvest supply chains (currently estimated to be over 50 per cent) in the country. The Government of India has shown strong intent towards exploiting its underutilised coastline for domestic transport of cargo through the Sagarmala initiative under the Ministry of Shipping [21]. The comprehensive programme aims to tap the potential of India’s coastline and inland waterways to create opportunities for port-led development in India. This project will therefore focus on using short-sea shipping for transport of fruit and vegetable produce in India. Results in this section reflect the distinct phases of the proposed methodology for short sea shipping as elucidated in Chapter 3 applied to the specific case of fruit and vegetables in India. The results helped deduce the selection of possible product route combinations (PRC) and the cost benefit analysis of each of the PRCs to arrive at a selection of one or two PRCs for implementation going forward. A market-based approach has been taken to determine the optimal allocation between land and intermodal transport. Environmental externalities were also observed during the application of the methodology. The results are shared in the following sections: - 4.1: Selection of product route combinations - 4.2: Selection of farmer-market combinations - 4.3: Supply chain redesign - 4.4: Test implementation 51 Selection of product-route combinations 4.1.1. Route selection In order to arrive at possible ‘routes’ (as defined under 3.1.1 of Methodology development) for the transportation of products by short sea shipping, CEZs with shipping line connectivity and requisite logistical infrastructure were determined in this section. Additionally, total turnaround time between origin and destination CEZs for farm-to-retailer supply chain was also deduced. 4.1.1.1. Identification of CEZs The first step to route selection was to identify all Indian Coastal Economic Zones (CEZs), the ports that fall within each CEZ, and the states in/across which they are located as per the country’s current sea route possibilities (Table 11). Below is the list of all existing ports in each of the CEZs and corresponding states where they are located. Table 11: Identification of 14 Coastal Economic Zones with States and Ports 20 Sr.no. CEZ State Ports 1 Kutch Gujarat Kandla, Mundra 2 Saurashtra Gujarat Pipavav, Sikka 3 Suryapur Gujarat Dahej, Hazira 4 North Konkan Maharashtra JNPT, Mumbai Dighi, Jaigarh, 5 South Konkan Maharashtra, Goa Mormugao 6 Dakshin Karnataka Mangalore 7 Malabar Kerala Kochi 8 Mannar Tamil Nadu Tuticorin 9 Poompuhar Tamil Nadu Cuddalore Chennai, Ennore, 10 VCIC South Tamil Nadu Kattupalli 11 VCIC Central Andhra Pradesh Krishnapatnam Visakhapatnam, 12 VCIC North Andhra Pradesh Kakinada 13 Kalinga Odisha Paradip, Dhamra 14 Gaud West Bengal Kolkata, Haldia 20 Ministry of shipping government of India, National perspective plan April 2016 (shipping.nic.in) http://www.bestcurrentaffairs.com/list-coastal-economic-zones-india Ministry of shipping government of India, Sagarmala Review: Port led Industrialization Dec 16 2016 (shipping.nic.in) 52 Figure 10 shows a geographical map of India’s coastline displaying all 14 CEZs. CEZs Kutch, Saurashtra, and Suryapur fall in the state of Gujarat. The state of Maharashtra has a long coastline that constitutes of CEZ North Konkan as well as part of CEZ South Konkan. CEZ South Konkan also includes the state of Goa. CEZ Dakshin is located in the state of Karnataka, and CEZ Malabar in the state of Kerala. These are the CEZs on the west coast of India. On the east coast, Tamil Nadu state has CEZs Mannar, Poompuhar, and VCIC South, CEZs VCIC Central and VCIC North are located in the state of Andhra Pradesh, CEZ Kalinga is in the state of Odisha and CEZ Gaud is in the state of West Bengal. Figure 11: Map of Indi’s coastline displaying all 14 CEZs comprising all coastal districts and relevant ports. In India, CEZs have already been defined as per the notification issued by the Government of India: A set of ports and districts near the coast constitute a CEZ and collectively meet the following three criteria, as defined by the Ministry of Shipping, Government of India [19]: a. The region has a coastline with a length between 300 and 500 kilometres; b. The region has an inland area between 200 and 300 kilometres; c. The region includes one, two or three seaports. 53 Since the selection criteria correspond to Indian definitions, all 14 existing CEZs meet the first three criteria a, b, and c (Table 12). Table 12: Output of first step of the methodology, i.e. application of criteria a, b, c [19]. Criteria Sr.no. CEZ State Ports a b c 1 Kutch Gujarat Kandla, Mundra x x x 2 Saurashtra Gujarat Pipavav, Sikka x x x 3 Suryapur Gujarat Dahej, Hazira x x x 4 North Konkan Maharashtra JNPT, Mumbai x x x 5 South Konkan Maharashtra, Goa Dighi, Jaigarh, Mormugao x x x 6 Dakshin Karnataka Mangalore x x x 7 Malabar Kerala Kochi x x x 8 Mannar Tamil Nadu Tuticorin x x x 9 Poompuhar Tamil Nadu Cuddalore x x x 10 VCIC South Tamil Nadu Chennai, Ennore, Kattupalli x x x 11 VCIC Central Andhra Pradesh Krishnapatnam x x x 12 VCIC North Andhra Pradesh Visakhapatnam, Kakinada x x x 13 Kalinga Odisha Paradip, Dhamra x x x 14 Gaud West Bengal Kolkata, Haldia x x x Key: ‘x’ CEZ meets criteria In the next step, this complete list of CEZs will be narrowed down to include only those CEZs which have the requisite shipping line connectivity. For the next step and steps the results of one step is an input to the next. Therefore the following process is applied to the results and inputs of every step: 1. If a port does not meet even one of the criteria in every step, then the port is excluded from further consideration. 2. If the CEZ under consideration has one port that does not meet the criteria but others do, then that particular port is excluded and other ports of the CEZ and the CEZ continue to be under consideration. 4.1.1.2. Determination of port connectivity between CEZs In the next phase, CEZs with adequate inter-port connectivity were identified out of all the potential short sea shipping routes. Ports and respective CEZs (Table 12) were further narrowed down to include only those with viable shipping connections (Table 13). For this, the following two criteria about port-to-port cargo shipping connectivity were applied: d. The number of shipping lines servicing the CEZ is at least two; e. There is at least one shipping connection to other ports. 54 Indian Coastal Logistics policies and laws are still being modified to fully realise the potential of the Sagarmala Coastal Initiative of the Ministry of Shipping, Govt. of India 21. Under existing laws only ships registered in India, i.e. the ships that carry the Indian flag, are permitted to carry domestic cargo along the Indian coast 22. Ships bearing flags of other nations, i.e. ships registered in other countries, are permitted to move only international cargo or empty containers along the coast. Having said that, foreign shipping companies can register vessels in India. Even if the parent shipping company isn’t Indian, if its vessel is registered in India, it is permitted to transport domestic cargo along the Indian coast. Shipping lines change their schedules from time to time based on demand. Also, some shipping lines – whether domestic or international - may service certain ports, however their vessels only carry bulk and do not carry containerized cargo. Moreover, the methodology also takes into consideration that the shipping line should carry reefer cargo containers. Therefore, shipping line connectivity is evaluated by only considering those vessels and respective shipping lines that can carry reefer containers. It would be ideal for Indian Coastal Logistics if more options of connectivity were available between the ports along the Indian coast for ships registered in India and other countries alike, but since that decision is under review, in applying the methodology two scenarios were considered: 1. International flag-bearing vessels along with Indian flag-bearing vessels are allowed to carry domestic cargo along the coast; 2. Only Indian flag-bearing vessels are allowed to carry domestic cargo along the coast. The set of criteria was applied to both scenarios, and results to arrive at the most viable short sea shipping routes were subsequently tabulated. Scenario 1- International flag-bearing vessels along with Indian flag-bearing vessels are allowed to carry domestic cargo along the coast Several ports are serviced by Indian flag-bearing vessels as well as one or more foreign flag-bearing vessels. These cumulatively help various ports meet criterion d, that of at least two shipping lines servicing the CEZ. The results from criteria d and e are found in Table 13 below: 21 http://shipping.gov.in/showfile.php?lid=2422 http://pib.nic.in/newsite/PrintRelease.aspx?relid=159037 22 http://shipping.gov.in/showfile.php?lid=2229 (pg 1) http://lawmin.nic.in/ld/P-ACT/1958/A1958-44.pdf (pg 178) The Merchant Shipping Act 1958, 2011, Universal Law Publishing Pvt Ltd (pg 187) 55 Table 13: Scenario 1- Identifying CEZs with at least two shipping lines and at least one inter-port shipping connection23 Criteria Sr.no. CEZ State Port d e Kandla x x 1 Kutch Gujarat Mundra x x Pipavav x x 2 Saurashtra Gujarat Sikka x Dahej x 3 Suryapur Gujarat Hazira x x JNPT x x 4 North Konkan Maharashtra Mumbai x Dighi x 5 South Konkan Maharashtra, Goa Jaigarh x Mormugao x 6 Dakshin Karnataka Mangalore x x 7 Malabar Kerala Kochi x x 8 Mannar Tamil Nadu Tuticorin x x 9 Poompuhar Tamil Nadu Cuddalore x Chennai x x 10 VCIC South Tamil Nadu Ennore x Kattupalli x x 11 VCIC Central Andhra Pradesh Krishnapatnam x Visakhapatnam x x 12 VCIC North Andhra Pradesh Kakinada x x Paradip x 13 Kalinga Odisha Dhamra x Kolkata x x 14 Gaud West Bengal Haldia x x Key: ‘x’ CEZ meets criterion | __ Criterion not met Among the results in Scenario 1 (Table 13), the ports of Sikka, Dahej, Mumbai, Dighi, Jaigarh, Mormugao, Cuddalore, Ennore, Krishnapatnam, Paradip and Dhamra did not meet criterion d. The criterion was applied to the entire CEZ, so even if only one of the ports in the CEZ met the criteria, the CEZ continued to be included in the final result. Thus, CEZs South Konkan, Poompuhar, VCIC Central, and Kalinga did not meet criterion d. Even though Sikka port did not meet criterion d, Pipavav port did, thus CEZ Saurashtra met the criterion with two shipping lines 23 Appendix 1 56 serving it. Similarly, even though Dahej port in Suryapur did not meet criterion d, Hazira port did; JNPT in North Konkan was serviced by shipping lines carrying containerized cargo; and both Chennai and Kattupalli ports compensate for Ennore port’s lack of shipping line serviceability. Ports such as Mumbai, Mormugao and Paradip have shipping line connectivity. However, the shipping lines that service these ports only carry bulk vessels, and not containerized cargo. All ports were observed to have connectivity to at least one other port and therefore all CEZs met criterion e 24. Scenario 2- Only Indian flag-bearing vessels are allowed to carry domestic cargo along the coast Several ports are serviced by Indian flag-bearing vessels by the state-owned Shipping Corporation of India as well as one or more private domestic ship operators. Therefore, various ports met criterion d, that of at least two shipping lines servicing the CEZ. The result of application of criteria d and e is in the Table 14 below: Table 14: Scenario 2- Identifying CEZs with at least two shipping lines and at least one inter-port shipping connection2526 Criteria Sr.no. CEZ State Port d e Kandla x 1 Kutch Gujarat Mundra x x Pipavav x x 2 Saurashtra Gujarat Sikka x Dahej x 3 Suryapur Gujarat Hazira x x JNPT x x 4 North Konkan Maharashtra Mumbai x Dighi x 5 South Konkan Maharashtra, Goa Jaigarh x Mormugao x 6 Dakshin Karnataka Mangalore x x 7 Malabar Kerala Kochi x x 8 Mannar Tamil Nadu Tuticorin x x 9 Poompuhar Tamil Nadu Cuddalore x Chennai x 10 VCIC South Tamil Nadu Ennore x Kattupalli x x 11 VCIC Central Andhra Pradesh Krishnapatnam x 24 Appendix 2 25 Appendix 1 26 Appendix 2 57 Criteria Sr.no. CEZ State Port d e Visakhapatnam x x 12 VCIC North Andhra Pradesh Kakinada x x Paradip x 13 Kalinga Odisha Dhamra x Kolkata x x 14 Gaud West Bengal Haldia x x Key: ‘x’ CEZ meets criteria | __ Criteria not met When criteria d and e are applied to the results in Table 14, it is evident that criterion d matched most CEZs because apart from Shipping Corporation of India (SCI), other private shipping lines also operate between several ports subject to availability of cargo. However, the ports within the CEZs which were not connected by at least two shipping lines included Kandla port in CEZ Kutch, Sikka port in CEZ Saurashtra, Mumbai port in CEZ North Konkan, entire CEZs South Konkan; Poompuhar, & Kalinga, Chennai and Ennore ports in CEZ VCIC South, and the Krishnapatnam port in CEZ VCIC Central (Table 14). All ports have connectivity to at least one other port and therefore all CEZs meet criterion e 27. At this stage, the list of CEZs with good connectivity was sifted out. Remainder CEZs, after the application of these criteria, were to be the input for the next step of the methodology. The next phase of CEZ selections refereed to multimodal transport infrastructure and capability for each port and the respective CEZ to which it belongs. 4.1.1.3. Attractiveness of ports in CEZs regarding multi-modal transport of fruits and vegetables A further deep-dive was carried out into the list of CEZs with good connectivity by applying criteria for attractiveness of multimodal transport and availability of relevant infrastructure. These criteria further restrict the list of potential ports for short sea shipping. Below are the five additional criteria to which the list in Table 13 and Table 14 is subjected: f. The length of unpaved roads between collection centre and port is less than 5 kilometres; g. The number of feeder vessels per week in the port should be at least one; h. Availability of cranes, trailers, side loaders and carriers (in the case of container transport), or ships should have a ramp (in the case of Ro-Ro transport); i. Above 10 meters draft at all ports; 27 Appendix 2 58 j. Availability of charging point for the reefer container at the Container Freight Station (CFS). It should be noted here that shipping infrastructure in India is being developed rapidly. The approach of evaluating CEZs based on two different scenarios will continue for the above criteria as well. Scenario 1- International flag-bearing vessels along with Indian flag-bearing vessels are allowed to carry domestic cargo along the coast Table 15: Scenario 1- results of CEZs with shipping connectivity, to which criteria f to j regarding multimodal transport is applied Criteria Sr.no. CEZ State Port f g h i j Kandla x x x x x 1 Kutch Gujarat Mundra x x x x x 2 Saurashtra Gujarat Pipavav x x x x 3 Suryapur Gujarat Hazira x x x x x 4 North Konkan Maharashtra JNPT x x x x x 6 Dakshin Karnataka Mangalore x x x x 7 Malabar Kerala Kochi x x x x x 8 Mannar Tamil Nadu Tuticorin x x x x x Chennai x x x x x 10 VCIC South Tamil Nadu Kattupalli x x x x x Visakhapatnam x x x x x 12 VCIC North Andhra Pradesh Kakinada x x x x Kolkata x x x 14 Gaud West Bengal Haldia x x Key: ‘x’ CEZ meets criteria | __ Criteria not met With reference to scenario 1, criteria f to j is applied to the list of CEZs with good connectivity. It is observed: f. To prevent mechanical product damage, it is assumed that sorting should be completed at the collection centre and from there produce is transported to the port. As a swiftly developing country where close to 110 km paved roads are constructed per day (400000 km district level roads constructed between years 2001 and 2011 alone)28, data on road density is constantly changing. This would also mean that there is high likelihood of ports being connected to major districts inland via roadways. For the purpose of this study, therefore, it was assumed that all roads between 28 http://rural.nic.in/sites/presentations.asp 59 the farm and port are paved or contain less than 5 kilometres of unpaved roads (usually from the farm to the nearest paved road). Therefore, we note that all ports and respective CEZs meet criteria f in Table 15 above. g. This criterion is applied on the assumption that most vessels transporting containers along the Indian coast are feeder type and carry between 500 and 1500 containers 29. The average number of feeder vessels per week at the port were calculated based on container traffic statistics 30. Several shipping lines connecting the ports in these CEZs do run weekly feeder vessels, which is, in essence, criterion g. It is advantageous if feeder vessels are used as they can carry less number of Twenty-Foot Equivalent Unit (TEUs) more frequently and faster. Many feeder vessels that run between the ports also carry bulk cargo exclusively and therefore may not be suitable for this methodology which needs container movement of cargo. On a weekly basis, Kandla port is serviced by 3 feeder vessels, Mundra port is serviced by 4 feeder vessels, both Pipavav port and Hazira port are serviced by 3 feeder vessels, JNPT port has 2 feeder vessels, Kochi port has 3 feeder vessels, Tuticorin port has 3 feeder vessel services, Visakhapatnam port is serviced by two feeder vessels, and Chennai & Kattupalli ports each have 2 feeder vessel services. While Kolkata & Haldia port were also served by 2 feeder vessels, but the frequency is once every 15 days, therefore CEZ Gaud does not meet criterion g 31. Kakinada port in VCIC North also does not meet this criterion. h. As all ports on the Indian coast considered for CEZ development are modernized, it can be safely assumed for the purpose of this study that the first half of criterion h, i.e. availability of cranes, trailers, side loaders and carriers (in the case of container transport) is met by all ports 32. There are no scheduled Ro-Ro services at any of the ports on the Indian seacoast 33. The criterion implies either 1. availability of cranes, trailers, side loaders and carriers (in the case of container transport), or 2. ships should have a ramp (in the case of Ro-Ro transport) should be fulfilled. All ports therefore can be said to be meeting criterion h. i. In the case of criteria i, which is that the depth of draft at the port should be more than 10 metres, both ports (Kolkata and Haldia) in CEZ Gaud do not meet the requirement. Kolkata port has a draft of 5.5 metres and the draft at Haldia is just about 10 metres 34. While Kandla port is shallower in terms of draft among modern Indian ports, it still stands at 11.2 metres and qualifies as per the criteria. 29 https://en.wikipedia.org/wiki/Container_ship 30 http://ipa.nic.in//showimg.cshtml?ID=217 31 Appendix 3 32 Appendix 4 33 Appendix 4 34 Appendix 5, https://www.searates.com/port/haldia_in.htm/ 60 j. Criteria j refers to the availability of reefer charging points. Container freight stations (CFS hereon) are established outside all ports for the storage of cargo awaiting customs inspection and other documentation 35. Charging points for reefers (REFs) can be found at most CFSes. However, the CFS at Mangalore port at CEZ Dakshin, and Haldia port at CEZ Gaud (which does not meet criteria g, h and i altogether) do not offer the facility of charging points for REFs 36. Scenario 2- Only Indian flag-bearing vessels are allowed to carry domestic cargo along the coast Table 16: CEZs after the application of multimodal criteria f, g, h, i, and j. Sr. CEZ State Port Criteria no. f g h i j 1 Kutch Mundra x x x x x 2 Saurashtra Gujarat Pipavav x x x x 3 Suryapur Hazira x x x x x 4 North Maharashtra JNPT x x x x x Konkan 6 Dakshin Karnataka Mangalore x x x x 7 Malabar Kerala Kochi x x x x x 8 Mannar Tuticorin x x x x x Tamil Nadu 10 VCIC South Kattupalli x x x x x 12 VCIC Andhra Visakhapatnam x x x x x North Pradesh Kakinada x x x x x 14 Gaud West Bengal Kolkata x x x x Haldia x x x Key: ‘x’ CEZ meets criteria | __ Criteria not met On application of criteria f to j to Scenario 2, it is found: 35Appendix 6 36Appendix 7 Kandla port - www.kandlaport.gov.in Mundra port - http://hindterminals.com/CFS_Mundra Hazira port - http://www.parekhgroup.in JNPT port - http://www.navkarcfs.com Kochi port - http://www.triway.in Tuticorin port - http://www.concorindia.com Chennai port - http://www.triway.in Kattupalli port - http://tgterminals.com Visakhapatnam port - http://www.allianceshipping.in 61 f. As discussed in the previous Scenario, 1, product sorting is assumed to have been completed at the collection centre to prevent product damage before being transported to the port. Also, as stated in the previous scenario, for this study, it was assumed that all roads between the farm and port are paved or contain less than 5 km of unpaved roads (usually from the farm to the nearest paved road) 37. g. While feeder vessels would be the ideal method of transporting between ports by the short sea routes, in the case of Scenario 2 all shipping lines run cargo vessels between all the ports and not scheduled feeder vessels. But feeder vessels run based on how much cargo is available. It is important to note, however, that there is also significant connectivity of non-feeder vessels between the Indian ports which is not evaluated in the g criteria. It is advantageous if feeder vessels are used as they can carry less number of TEUs more frequently and faster. But we cannot ignore the fact that Indian ports are also connected with non-feeder services along the coast. Therefore, all Indian ports meet this criteria in this scenario on a need-basis. 38. h. As all ports on the Indian coast considered for CEZ development are modernized, it can be safely assumed for the purpose of this study that the first half of criterion h, i.e. availability of cranes, trailers, side loaders and carriers (in the case of container transport) is met by all ports 39. There are no scheduled Ro-Ro services at any of the ports on the Indian seacoast 40. The criterion implies either 1. availability of cranes, trailers, side loaders and carriers (in the case of container transport), or 2. ships should have a ramp (in the case of Ro-Ro transport) should be fulfilled. All ports therefore can be said to be meeting criterion h. i. In the case of criteria i, which is that the depth of draft at the port should be more than 10 metres, both ports (Kolkata and Haldia) in CEZ Gaud do not meet the requirement. Kolkata port has a draft of 5.5 metres and the draft at Haldia is just about 10 metres. While Kandla port is shallower in terms of draft among modern Indian ports, it still stands at 11.2 metres and qualifies as per the criteria 41. j. Criteria j refers to the availability of reefer charging points. Container freight stations are established outside all ports for the storage of cargo awaiting customs inspection and other documentation 42. Usually, charging points for reefers (REFs) can be found at these CFSes. The CFS at Mangalore port at CEZ Dakshin, and Haldia port at CEZ Gaud (which does not meet criteria g, h and i altogether) do not offer the facility of charging points for REFs currently 43. 37 http://rural.nic.in/sites/presentations.asp 38 Appendix 3 39 Appendix 4 40 Appendix 4 41 Appendix 5, https://www.searates.com/port/haldia_in.htm/ 42 Appendix 6 43 Appendix 7 62 4.1.1.4. Existing shipping line connectivity For the list of CEZs, after the application of criteria pertaining to multimodal transport and associated infrastructure, the existing presence of shipping routes between CEZs of origin and CEZ destinations is considered. Only if the route is served by at least one existing shipping line directly, is it considered a viable route. This methodology step continues to evaluate CEZs under both scenarios for existing connectivity. Scenario 1 In the case of Scenario 1, potentially viable short sea shipping routes between the Indian CEZ port pairs are shortlisted below (Table 17) 44. Table 17: Scenario 1- Results of origin and destination CEZ-pairs in which shipping routes are already present 45 ORIGIN DESTINATION - - - Kattupalli - – - Tuticorin - Chennai - Visakhapat Suryapur Konkan Malabar Mundra Mannar Sr.no. Hazira- Kandla CEZ Kutch Kutch North North Kochi VCIC VCIC VCIC JNPT S h South Port nam Kandla x x x 1 Kutch Mundra x x x x x x x 3 Suryapur Hazira x x 4 North Konkan JNPT x x 7 Malabar Kochi x x 8 Mannar Tuticorin x x x x Chennai x 10 VCIC South Kattupalli x x x x x 12 VCIC North Visakhapatnam x x x x Key: ‘x’ CEZ meets criteria | __ Criteria not met Of the CEZ-pairs presented above in Table 17, Kandla port from CEZ Kutch is connected to Mundra and JNPT ports. Mundra (observed to be the best connected to other ports in the CEZ list) has existing shipping routes with Kandla, JNPT (its only port connection from the list), Kochi, Tuticorin, and Kattupalli ports with the exception of Chennai port. In CEZ Suryapur, Hazira port has existing feeder connectivity with Mundra port in CEZ Kutch and JNPT port in CEZ North Konkan. In CEZ Malabar, Kochi port additionally also has a route to Tuticorin port, which in turn also has a route connectivity to Kattupalli. There is also a route from Kochi and Tuticorin ports to Mundra port. In CEZ VCIC South, Chennai port is only connected to Kattupalli. In VCIC North, Visakhapatnam port has connectivity with Mundra port in CEZ Kutch, Kochi port in CEZ Malabar, Tuticorin port in CEZ Mannar, and Kattupalli port in CEZ VCIC South. 44 Appendix 8 45 Appendix 8 63 Scenario 2 For Scenario 2 we have to consider only the Indian flag carrying vessels and therefore the results may be different for some ports. In the case of Scenario 2, potentially viable short sea shipping routes between the Indian CEZ port pairs are shortlisted below (Table 18) 46. Table 18: Scenario 2- Results of origin and destination CEZ-pairs in which shipping routes are already present ORIGIN DESTINATION Kattupalli - VCIC South Kakinada - VCIC North JNPT - North Konkan Visakhapatnam -VCIC Tuticorin - Mannar Hazira - Suryapur Kochi - Malabar Sr.no. CEZ Mundra -Kutch North Port 1 Kutch Mundra x x x x x x x 3 Suryapur Hazira x x 4 JNPT North Konkan x 7 Malabar Kochi x x x 8 Mannar Tuticorin x x x x x 10 VCIC South Kattupalli x x x Visakhapatnam x x x x x 12 VCIC NORTH Kakinada x Key: ‘x’ CEZ meets criteria | __ Criteria not met As observed under the results in Table 18 pertaining to connectivity in Scenario 2, all ports with the exception of Kakinada have direct sea route connectivity from Mundra port in CEZ Kutch. In addition, in CEZ Malabar, Kochi port has a route to Tuticorin port, which in turn also has a route to Kattupalli in CEZ VCIC South and Visakhapatnam in CEZ VCIC North. The connectivity of Kattupalli port to Kochi port by sea route is also present. Visakhapatnam port in VCIC North is connected to all ports with the exception of Hazira and JNPT ports, and Kakinada port is connected only to Visakhapatnam. Table 17 showcases shipping route connectivity for Scenario 1. Below (Table 19) is the summary of origin and destination CEZs based on existing shipping line connectivity between them. 46 Appendix 9, Ministry of Shipping, Government of India, Vision for Coastal Shipping. (shipping.nic.in) 64 Table 19: Scenario 1- Summary of results of shipping routes between origin and destination CEZ-pairs ORIGIN DESTINATION Suryapur North VCIC VCIC Sr.no. CEZ Kutch Malabar Mannar Konkan South North 1 Kutch + + + + + + 3 Suryapur + + - - - - North + - 4 Konkan + - - - 7 Malabar + - - + - + 8 Mannar + - - + + + VCIC - + 10 South + - + + VCIC - 12 North + - + + + Key: + Connectivity from origin to destination CEZ | - Absence of connectivity from origin to destination CEZ. Connectivity is measured from origin to destination CEZ based on at least one port from both CEZs having connectivity. CEZ Kutch has connectivity to CEZ North Konkan, CEZ Malabar, CEZ Mannar, and CEZ VCIC South. CEZ Suryapur has connectivity to CEZ Kutch and to CEZ North Konkan. CEZ North Konkan has route connectivity only to CEZ Kutch. CEZ Malabar enjoys route connectivity with CEZ Mannar and CEZ Kutch. CEZ Mannar has shipping route connectivity with CEZ VCIC South, apart from CEZ Malabar and CEZ Kutch. CEZ VCIC South has additional (apart from CEZ Kutch) route connectivity with CEZ Malabar and CEZ Mannar. CEZ VCIC North has connectivity to CEZ Kutch, CEZ Malabar, CEZ Mannar and VCIC South. For Scenario 2, only Indian flag-bearing vessels shall be considered, and therefore the results may vary for some ports. Table 18 showcases shipping route connectivity for Scenario 2. Below (Table 20) is the summary of origin and destination CEZs based on existing shipping line connectivity between them. 65 Table 20: Scenario 2- Summary of results of shipping routes between origin and destination CEZ-pairs ORIGIN DESTINATION North VCIC VCIC Sr.no. CEZ Kutch Malabar Mannar Suryapur Konkan South North 1 Kutch + + + + + + 3 Suryapur + + - - - - 4 North Konkan + - - - - - 7 Malabar + + - + - - 8 Mannar + + - + + + 10 VCIC South + - - + + - 12 VCIC North + - - + + + Key: + Connectivity from origin to destination CEZ | - Absence of connectivity from origin to destination CEZ. Connectivity is measured from origin to destination CEZ based on at least one port from both CEZs being connected. While several CEZs are observed to be reciprocally connected, that was not the case with CEZ pairs extrapolated below: Similar to Scenario 1, CEZ Kutch enjoys connectivity to all destination CEZs, i.e. CEZ Suryapur, CEZ North Konkan, CEZ Malabar, CEZ Mannar, CEZ VCIC South, CEZ VCIC North. CEZ Suryapur has connectivity with CEZ Kutch and CEZ North Konkan. Also, identical to Scenario 1, CEZ North Konkan has connectivity only to CEZ Kutch. CEZ Malabar enjoys connectivity to CEZ Kutch, CEZ Suryapur and CEZ Mannar. CEZ Mannar has connectivity to CEZ Kutch, CEZ Suryapur, CEZ Malabar, CEZ VCIC South, CEZ VCIC North. CEZ VCIC South is connected via shipping routes to CEZ Kutch, CEZ Malabar and CEZ Mannar. CEZ VCIC North has shipping route connectivity to CEZ Kutch, CEZ Malabar, CEZ Mannar and CEZ VCIC South. In the next step, the turnaround time for each CEZ pair shall be explored, not only in terms of port-to-port, but also for the entire supply chain process of farm to consumption. Throughput time of sea – part of the supply chain Having arrived at the most viable CEZ- routes, the end-to-end supply chain turnaround time (TAT) for all CEZs is calculated next. Since the resultant sets of CEZs in Scenario 1 and Scenario 2 differ only by two CEZs, namely the addition of CEZ Suryapur and CEZ VCIC North, the origin and destination CEZ results in Table 19 and Table 20 are combined. Total CEZs considered here onwards were therefore seven. 66 Table 21: CEZ pairs combined from results of Scenario 1 and 2 after deducing connectivity between each pair in days ORIGIN DESTINATION North Visakhapatnam - VCIC Kattupalli - VCIC South Kakinada - VCIC North JNPT - North Konkan Chennai - VCIC South Tuticorin - Mannar Hazira - Suryapur Kochi - Malabar Mundra - Kutch Kandla - Kutch Sr.no. CEZ Port Kandla 1 1 1 3 3 4 4 5 5 1 Kutch Mundra 1 1 1 3 3 4 4 5 5 3 Suryapur Hazira 1 1 1 2 3 4 4 4 4 4 North Konkan JNPT 1 1 1 2 2 3 3 4 4 7 Malabar Kochi 3 3 2 2 1 2 2 3 3 8 Mannar Tuticorin 3 3 3 2 1 1 1 2 2 Chennai 4 4 4 3 2 1 1 1 1 10 VCIC South Kattupalli 4 4 4 3 2 1 1 1 1 Kakinada 5 5 4 4 3 2 1 1 0 1 12 VCIC NORTH Visakhapatnam 5 5 4 4 3 2 1 1 1 The above table shows the number of days at sea between two ports and thus gives port to port transit time. However, the turnaround time (TAT) for each CEZ pair shall be explored, not only in terms of port-to-port connectivity time, but also for the entire supply chain process of farm to buyer. While the results for these calculations were independent of products selected, they were vital to the process of product selection as they were compared to the shelf-life or perishability of the products. Calculations of Transport at Sea were carried out in three stages. First, the nautical miles between each origin and destination port is tabulated. Then, time taken between the two ports is tabulated in terms of hours. 47 The knot (/nɒt/) is a unit of speed equal to one nautical mile (1.852 km) per hour, approximately 1.151 mph. The ISO Standard symbol for the knot is kn. The vessel travels at speeds of 18-24 kn, thus its speed is calculated at 20 km per hour for our calculation purposes here. If the TAT is less than 24 hours we have taken it as 1 day and if the TAT is more than 24 hours, then 2 days, and so on. 47 http://ports.com/ 67 Therefore, the number of hours taken for transport at sea from port to port were converted into, and presented as, the number of days finally. The addition of hours above each 24-hour cycle was rounded off to the next higher integer as an additional day. This was done since sea winds and other poor weather conditions often cause delays to absolute time schedules in shipping and it was better to assume slightly higher turnaround 48 It should be noted here that the data for the distance between other ports and Hazira port was not available online or from an official source. The source of information is therefore in the form of an interview 49. Moreover, data for the distance (and therefore Transport time at sea) between the other ports in the selected CEZs and Kattupalli port was not available. The calculation applied therefore is: difference between Kattupalli and Chennai port, 10.8 nautical miles, added or subtracted (depending upon direction from Kattupalli port) from its distance to Chennai to arrive at the distance from each port to Kattupalli port. While the turnaround time for each CEZ pair is explored in terms of port-to-port connectivity in the preceding table, the entire supply chain process of farm to consumption is much longer, and merits documentation while factoring in logistics. This is represented as a process flow below (Figure 11). Documentation and processes such as pre-cooling the reefer container, sending it to the farm, at farm sorting, dispatching the container are carried out even before the container arrives at the port. In addition to the port to port shipment, once the products arrive for transport to another port (‘Processes and Documents on Departure’ - PDD) such as survey inspections, loading container on the vessel, etc. is completed and the same process takes place when products arrive at destination port (‘Processes and Documents on Arrival’ - PDA). All ports take a procedural one day at the CFS and another day for the carting-loading process –the PDD and PDA was therefore considered to be a standard process of two days each. Turnaround time for on-land transport was taken as one day each, both, at the port of origin and destination. Considering that the farm and buyer both may be located within 200 km to 300 km of the port, given the criteria that has applied to the CEZ selection, it is determined that a container would take a day to travel this distance. 48 Appendix 11 49 Appendix 10 68 PRE-COOLING OF TRANSPORT AT SEA PROCESSES AND EMPTY REEFER • VARIABLE DOCUMENTATION AT CONTAINER (REF.: TABLE 21) ARRIVAL (PDA) • 1 DAY • 2 DAYS CONTAINER PROCESSES AND TRANSPORT FROM TRANSPORTED TO DOCUMENTATION AT PORT TO BUYER FARM & HARVESTING DEPARTURE (PDD) • 1 DAY AT THE FARM • 2 DAYS • 1 DAY SORTING OF PRODUCE CONTAINER DISPATCH AT FARM & LOADING & TO PORT CARTING THE • 1 DAY CONTAINER • HALF - ONE DAY Figure 12: Process flow from farm to buyer. Despite there being an additional step in the logistics of pre-cooling the container, it does not impact the perishability of the product. The 1 day that this step takes is, therefore, not considered in the total throughput time between each origin and destination CEZ for assessing perishability. While the container travels for a day from the port to the farm, the activity of harvesting and sorting is assumed to have already begun, and therefore it is the harvest stage, from when perishability has an impact on the produce. As the container reaches the farm, the time taken to load the container impacts the perishability of the produce as well. Port-to-farm, farm-to-port, time taken for documentation at the origin and destination ports, and port-to-buyer transit time were considered constant therefore remain identical for all ports, across all port-to-port results. A product-independent calculation of turnaround time (TAT) from farm to buyer was thus made (Table 22) to further compare it with produce perishability in the next section of this report. Each tabulation in this table corresponds to the total TAT between a farm located in one CEZ and a buyer located in another CEZ. Represented below in the table through ports that are 200-300 km from the buyer or the farm. The total TAT is a calculation of the total number of days from port to farm and back, the time taken at the origin port, transport at sea, time taken at the destination port, and time taken for the container to reach the buyer. 69 Table 22: Turnaround Time (TAT) for selected CEZs (in days). ORIGIN DESTINATION Kattupalli - VCIC South Kakinada - VCIC North Visakhapatnam - VCIC JNPT - North Konkan Chennai - VCIC South Tuticorin - Mannar Hazira - Suryapur Mundra - Kutch Kochi - Malabar Kandla - Kutch Sr.no. CEZ North Port Kandla 9 9 9 11 11 12 12 13 13 1 Kutch Mundra 9 9 9 11 11 12 12 13 13 3 Suryapur Hazira 9 9 9 10 11 12 12 12 12 4 North Konkan JNPT 9 9 9 10 10 11 11 12 12 7 Malabar Kochi 11 11 10 10 9 10 10 11 11 8 Mannar Tuticorin 11 11 11 10 9 9 9 10 10 Chennai 12 12 12 11 10 9 9 9 9 10 VCIC South Kattupalli 12 12 12 11 10 9 9 9 9 Kakinada 13 13 12 12 11 10 9 9 9 12 VCIC North Visakhapatnam 13 13 12 12 11 10 9 9 9 As reiterated earlier, pre-cooling of container was not calculated in the CEZ to CEZ TAT since it does not affect perishability. However, it is an integral part of the logistical supply chain and therefore included as part of the process flow in the Figure 11. This CEZ-level summary concluded the process of route selection. In the next section, as per the prescribed methodology, the process of product selection shall be undertaken, beginning with the identification of products in each selected CEZ along the possible routes determined in this section. 4.1.2. Product Selection In this section, ‘products’ (specifically fruits and vegetables) underwent a selection process to determine the fruits and vegetable(s) with the best chance for successful transportation by short sea shipping. Below are the set of criteria employed for selecting one or two products for consideration: a) Most produced products per selected CEZ; b) Sufficient shelf life; c) Oversupply in the CEZ of production; d) Undersupply in one of the other selected CEZs. 70 4.1.2.1. Most produced products per selected CEZs The product selection process began with identification of top five fruits and top five vegetables produced in each of the CEZs shortlisted in section 4.1.2. In India CEZs constitute of a collection of districts, and production data was available at district level. Therefore, fruits and vegetables were identified on the basis of annual production in each district. First, the district-wise production data of fruits and vegetables in each CEZ for the three most recent consecutive years was considered. This data was taken for the most recent three years to rule out any ‘bad year’ in terms of production lows caused due to climatic anomalies such as drought or flood or even pest attack. An average of the three-year production data was then derived for each fruit and vegetable per district, and the corresponding seasons/months of their harvest. As some three-year data was not as recent, data from the most recent year, 2015, was checked to ensure that these products are still cultivated in the respective CEZs. This average is considered to be the annual production of the specific fruits and vegetables in the district. Post this, the average production data of top five fruits and vegetables for each district was collated in a table at the CEZ level, to arrive at the total CEZ production of the produce. From the total values of production at the CEZ level, the products were ranked in descending order of production, i.e. 1 to the product with the highest average production quantity, and 5 to the product with the lowest average production quantity. The top five vegetables and fruits across CEZ Kutch is represented in Table 23. 50 Table 23: Product ranking for fruits and vegetables in CEZ Kutch 51 Production Vegetables Rank (MT) Onion 1 61233 Tomato 2 25692 Eggplant 3 26275 Cabbage 4 8715 Okra 5 5443 50 Appendix 12, https://data.gov.in/catalog/district-wise-season-wise-crop-production-statistics 51 http://dcmsme.gov.in/dips/2016-17/kutch.pdf 71 Production Fruits Rank (MT) Papaya 1 174601 Date 2 102778 Banana 3 70767 Mango 4 50998 Pomegranate 5 24059 It should be noted here, that only one district falls under the CEZ in the case of Kutch. Therefore, production of each product was simply ranked according to its production quantities. However, products are not ranked at district level, but at CEZ level for their average production. In the rest of the selected CEZs, several districts fall under each of them. Each product was listed with its average production quantity for each district and added within the product group to arrive at the total annual production quantity (in MT) of the product in the CEZ. The top five vegetables and fruits across CEZ Suryapur is represented in Table 24. 52 Table 24: Product ranking for fruits and vegetables in CEZ Suryapur Production Vegetables Rank (MT) Banana 1 1710119 Mango 2 299568 Sapota 3 118405 Papaya 4 101240 Indian Jujube 5 6923 Production Fruits Rank (MT) Cucurbits 1 212382 Okra 2 205146 Eggplant 3 151602 Tomato 4 61629 Cauliflower 5 22917 52 Appendix 12, https://data.gov.in/catalog/district-wise-season-wise-crop-production-statistics 72 As an example for the total volume calculation: In CEZ Suryapur, there are three districts: in Bharuch, 1078330 MT of banana is produced on average. In Surat and Navsari, 527789 MT and 104000 MT of banana is produced respectively. Production quantities of banana from all three districts were added to arrive at a total annual production of 1710119 MT/annum to be considered in the CEZ-wise ranking of banana for CEZ Suryapur. The top vegetables and fruits across CEZ North Konkan is represented in Table 25. Table 25: Product ranking for fruits and vegetables in CEZ North Konkan 53 Production Vegetables Rank (MT) Onion 1 2111549 Tomato 2 219718 Production Fruits Rank (MT) Grapes 1 449061 Banana 2 75223 Mango 3 47053 In the case of CEZ North Konkan, data for annual production is only available for Onion and Tomato among vegetables and Grapes, Banana and Mango among fruits. The product rankings for vegetables and fruits in CEZ North Konkan are therefore limited to 2 and 3 respectively. The top five vegetables and fruits across CEZ Malabar is represented in Table 26. 54 Table 26: Product ranking for fruits and vegetables in CEZ Malabar Production Vegetables Rank (MT) Tapioca 1 1366279 Drumstick 2 6053 Ginger 3 1543 Sweet Potato 4 220 Production Fruits Rank (MT) Coconut 1 1438666667 Banana 2 541406 Mango 3 121886 Pineapple 4 60578 Papaya 5 27916 53 Appendix 12, https://data.gov.in/catalog/district-wise-season-wise-crop-production-statistics 54 Appendix 12, https://data.gov.in/catalog/district-wise-season-wise-crop-production-statistics 73 The top five vegetables and fruits across CEZ Mannar are represented in Table 27. 55 Table 27: Product ranking for fruits and vegetables in CEZ Mannar 56 Production Vegetables Rank (MT) Tapioca 1 119509 Onion 2 20155 Tomato 3 6931 Eggplant 4 3271 Okra 5 2565 Production Fruits Rank (MT) Coconut 1 243798567 Banana 2 413495 Mango 3 23150 Jackfruit 4 8144 Pomefruit 5 2287 The top five vegetables and fruits across CEZ VCIC South is represented in Table 28. 57 Table 28: Product ranking for fruits and vegetables in CEZ VCIC South58 Production Vegetables Rank (MT) Eggplant 1 12967 Tapioca 2 5383 Okra 3 4905 Tomato 4 513 Sweet potato 5 393 Production Fruits Rank (MT) Coconut 1 25849400 Mango 2 93319 Banana 3 64116 Guava 4 800 Watermelon 5 Of all the data for produce available, production data for watermelon in the corresponding CEZ VCIC South, specifically, is unavailable. Therefore, while it is listed here, there is no production data available for ranking it. 55 Appendix 12, https://data.gov.in/catalog/district-wise-season-wise-crop-production-statistics 56 http://nhm.nic.in/JIT_Reports/13092015.pdf 57 Appendix 12, https://data.gov.in/catalog/district-wise-season-wise-crop-production-statistics 58 https://www.researchgate.net/publication/305766315_Horticulture_Statistics_at_a_Glance_2015 74 The top five vegetables and fruits across CEZ VCIC North is represented in Table 29. 59 Table 29: Product ranking for fruits and vegetables in CEZ VCIC North60 Production Vegetables Rank (MT) Tapioca 1 178077 Tomato 2 100626 Eggplant 3 82754 Onion 4 38222 Okra 5 37376 Production Fruits Rank (MT) Coconut 1 2051347333 Mango 2 1778262 Banana 3 960437 Papaya 4 144375 Lemon 5 65392 From all of these CEZ-wise production lists, a consolidated list of fruits and vegetables (Table 30) is derived for consideration of perishability threshold in number of days in step b. This consolidated list does not take into consideration the production ranks of the fruits and vegetables in each CEZ-wise list. Table 30: Consolidated list of all fruits and vegetables for consideration of perishability in criterion b. S.NO. FRUITS VEGETABLES 1 Papaya Onion 2 Date Tomato 3 Mango Eggplant 4 Pomegranate Cabbage 5 Banana Okra 6 Sapota Cucurbits 7 Indian Jujube Cauliflower 8 Grapes Tapioca 9 Coconut Drumstick 10 Pineapple Ginger 11 Jackfruit Sweet potato 12 Pomefruit 59 Appendix 12, https://data.gov.in/catalog/district-wise-season-wise-crop-production-statistics 60 nhb.gov.in/area-pro/NHB_Database_2015.pdf , midh.gov.in/technology/State-Wise-Horticulture-Status.pdf 75 S.NO. FRUITS VEGETABLES 13 Guava 14 Watermelon 15 Lemon Transit times from port to port by short sea shipping routes reveal connectivity of routes and the speed at which products can be transported. It is as important to know the perishability threshold of a product or its shelf life in terms of number of days. The latter determines which products would be most suitable to be transported via short sea shipping while impacting perishability of the product significantly less than existing modes of transport, help contain food wastage and losses and also earning the farmer a better price for his produce. In step b, therefore, for each product resulting from criterion a., the shelf life of the types of fruits and vegetables is compared to the total turnaround time taken from farm to buyer from one CEZ to another within the selected combination of CEZs in the next step. 4.1.2.2. Sufficient shelf life In this step, perishability of all products (fruits and vegetables) listed in Table 31 and Table 32 were listed with maximum & minimum days that each product will survive in cold chain. Perishability of the same products under ambient temperatures was also listed (Table 31). India runs warmer temperatures even by night along its coast owing to its geographical proximity to the tropic of Cancer, therefore ambient temperatures for perishability were considered at 30˚ Celsius. Average shelf life of fruits was calculated based on the corresponding minimum and maximum value in the third and fourth columns in the tables below in Table 31. Table 31: Product perishability data of fruits results of step a 61 S.NO. FRUITS REEFER/COLD CHAIN AMBIENT minimum maximum (days) (days) opt. T (˚ C) 30˚ C 1 Papaya 7 21 7-13 not viable 2 Date 180 360 0 OK, keep dry 3 Mango 14 21 13 not viable 4 Pomegranate 60 90 5-7.2 not viable 5 Banana 7 28 13-15 not viable 61 http://postharvest.ucdavis.edu/Commodity_Resources/Fact_Sheets/ 76 S.NO. FRUITS REEFER/COLD CHAIN AMBIENT not viable 6 Sapota 14 14 15-20 not viable 7 Indian Jujube unknown 62 28 2.5-10 not viable 8 Grapes 30 180 0 not viable 9 Coconut 30 60 0-2 not viable 10 Pineapple 14 28 7-13 not viable 11 Jackfruit 14 28 13 not viable 12 Pomefruit (apple/pear) 30 60 0-4 not viable 13 Guava 14 21 5-10 not viable 14 Watermelon 14 21 10-15 15 Lemon 14 14 9-10 not viable Table 31 reveals that among fruits, only dates survive in both cold chain as well as ambient conditions. In the latter case, the product must be kept dry, and humidity must be observed. 62 No data found in literature 77 Table 32: Product perishability data of vegetable results of step a. S.NO. VEGETABLES REEFER/COLD CHAIN AMBIENT minimum maximum (days) (days) opt. T (˚ C) 30˚ C 1 Onion 30 240 0 OK, keep dry 2 Tomato 14 35 8-13 not viable 3 Eggplant 7 14 10-12 not viable 4 Cabbage 21 42 0 not viable 5 Okra 7 10 7-10 not viable 6 Cucurbits 10 14 10-12 not viable 7 Cauliflower 21 28 0 not viable 8 Tapioca Data not available 9 Drumstick Data not available 10 Ginger 180 180 13 OK, keep dry 11 Sweet potato 120 210 13-15 OK, keep dry In Table 32, only onion, ginger and sweet potato among vegetables survive without cold chain. These products too however, must be kept dry, and humidity must be observed constantly. Data for tapioca and drumstick was not available, therefore the two products were not considered here onwards. Averages of the maximum and minimum days of perishability threshold were deduced. It was also mandated that products should be of sufficient quality for sale for five days after reaching the market. For instance, if the maximum perishability threshold for mango in cold chain was 21 days and minimum was 14 days, then its average perishability threshold in number of days was 17. In the case of cold chain, the number of days available for logistics would have been 17-5=12 in presence of cold chain. Since most fruits and vegetables in Table 31and Table 32 above were observed to not survive in ambient temperature, consideration for perishability thresholds for the products was restricted to cold chain. The maximum perishability threshold in number of days available for fruits after removing the five market days are given in Table 33 63. 63 Appendix 13 78 Table 33: Perishability threshold of fruits. FRUITS Maximum (days) Papaya 9 Date 265 Mango 12 Pomegranate 70 Banana 12 Sapota 9 Indian Jujube 9 Grapes 100 Coconut 40 Pineapple 16 Jackfruit 16 Pomefruit (apple/pear) 40 Guava 12 Watermelon 12 Lemon 9 The maximum perishability threshold in number of days available for vegetables after removing the five market days are given below in Table 34 64. Table 34: Perishability threshold of vegetable VEGETABLES Maximum (days) Onion 130 Tomato 19 Eggplant 5 Cabbage 26 Okra 3 Cucurbits 7 Cauliflower 19 Tapioca Data not available Drumstick Data not available Ginger 175 Sweet potato 160 As mentioned earlier, apart from onion, ginger and sweet potato, none of the products will survive in dry ambient temperatures, and therefore perishability thresholds for all products were considered with respect to cold chain only. 64 Appendix 13 79 These results were compared with the logistical TATs between CEZs (Table 22) in order to eliminate products that would not meet the perishability threshold. Since the product must be transported from a specific port in the CEZ, product-TAT combinations are listed port-wise for each CEZ below in Table 35 to Table 44. Table 35: Product-TAT comparison of fruits and vegetables produced near Kandla port (CEZ Kutch). Maximum Vegetables Rank Production Days Destination Ports where the produce can be shipped Mundra, Hazira, JNPT, Kochi, Tuticorin, Chennai, Kattupalli, Onion 1 61233 130 Kakinada, Visakhapatnam Mundra, Hazira, JNPT, Kochi, Tuticorin, Chennai, Kattupalli, Tomato 2 25692 19 Kakinada, Visakhapatnam Eggplant 3 26275 5 None Mundra, Hazira, JNPT, Kochi, Tuticorin, Chennai, Kattupalli, Cabbage 4 8715 26 Kakinada, Visakhapatnam Okra 5 5443 3 None Fruits Rank Production Papaya 1 174601 9 Mundra, Hazira, JNPT Mundra, Hazira, JNPT, Kochi, Tuticorin, Chennai, Kattupalli, Date 2 102778 265 Kakinada, Visakhapatnam Banana 3 70767 12 Mundra, Hazira, JNPT, Kochi, Tuticorin, Chennai, Kattupalli Mango 4 50998 12 Mundra, Hazira, JNPT, Kochi, Tuticorin, Chennai, Kattupalli Mundra, Hazira, JNPT, Kochi, Tuticorin, Chennai, Kattupalli, Pomegrenate 5 24059 70 Kakinada, Visakhapatnam Among vegetables, eggplant and okra have very short shelf life. The minimum logistical TATs were calculated to be at least 9 and 5 days respectively. They can therefore not be shipped to any other ports as their perishability threshold is shorter than the logistical TAT to other ports. Among fruits, papaya has a relatively short shelf life of 9 days. It could therefore only be shipped to Mundra, Hazira and JNPT ports in closest proximity to the Kandla port in terms of logistical TATs. 80 Table 36: Product-TAT comparison of fruits and vegetables produced near Mundra port (CEZ Kutch). Maximum Destination Ports where the produce can Vegetables Rank Production Days be shipped Kandla, Hazira, JNPT, Kochi, Tuticorin, Chennai, Kattupalli, Kakinada, Onion 1 61233 130 Visakhapatnam Kandla, Hazira, JNPT, Kochi, Tuticorin, Chennai, Kattupalli, Kakinada, Tomato 2 25692 19 Visakhapatnam Eggplant 3 26275 5 None Kandla, Hazira, JNPT, Kochi, Tuticorin, Chennai, Kattupalli, Kakinada, Cabbage 4 8715 26 Visakhapatnam Okra 5 5443 3 None Fruits Rank Production Papaya 1 174601 9 Kandla, Hazira, JNPT Kandla, Hazira, JNPT, Kochi, Tuticorin, Chennai, Kattupalli, Kakinada, Date 2 102778 265 Visakhapatnam Kandla, Hazira, JNPT, Kochi, Tuticorin, Banana 3 70767 12 Chennai, Kattupalli Kandla, Hazira, JNPT, Kochi, Tuticorin, Mango 4 50998 12 Chennai, Kattupalli Kandla, Hazira, JNPT, Kochi, Tuticorin, Chennai, Kattupalli, Kakinada, Pomegranate 5 24059 70 Visakhapatnam From Mundra port also, among vegetables, eggplant and okra could not be shipped to any other ports for the same reason as in Table 35 with reference to Kandla port. Among fruits, papaya’s short shelf life allows its shipment to be limited to Kandla, Hazira and JNPT ports in closest proximity to the Mundra port in terms of logistical TATs. 81 Table 37: Product-TAT comparison of fruits and vegetables produced near Hazira port (CEZ Suryapur). Destination Ports where the produce can be Fruits Rank Production Maximum Days shipped Kandla, Mundra, JNPT, Kochi, Tuticorin, Mango 1 299568 12 Chennai, Kattupalli, Kakinada, Visakhapatnam Kandla, Mundra, JNPT, Kochi, Tuticorin, Banana 2 1710119 12 Chennai, Kattupalli, Kakinada, Visakhapatnam Sapota 3 118405 9 Kandla, Mundra, JNPT Papaya 4 101240 9 Kandla, Mundra, JNPT Indian Jujube 5 6923 9 Kandla, Mundra, JNPT Vegetables Rank Production Cucurbits 1 212382 7 None Okra 2 205146 3 None Eggplant 3 151602 5 None Kandla, Mundra, JNPT, Kochi, Tuticorin, Tomato 4 61629 19 Chennai, Kattupalli, Kakinada, Visakhapatnam Kandla, Mundra, JNPT, Kochi, Tuticorin, Cauliflower 5 22917 19 Chennai, Kattupalli, Kakinada, Visakhapatnam Among fruits, sapota, papaya and the Indian jujube have relatively short shelf lives of 9 days, which corresponds with the logistical TAT of Kandla, Mundra and JNPT ports in closest proximity to the Hazira port. The top vegetables grown in CEZ Suryapur, cucurbits, okra and eggplant have shelf lives of 7, 3 and 5 days respectively. The TAT for even the closest port from Hazira port is 9 days, increasing up to 13 days for the farthest port. From Hazira port also therefore, among vegetables, cucurbits, okra and eggplant cannot be shipped to any other ports due to short perishability thresholds in comparison with the logistical CEZ-wise TATs in Table 22. 82 Table 38: Product-TAT comparison of fruits and vegetables produced near JNPT port (CEZ North Konkan). Maximum Vegetables Rank Production Days Destination Ports where the produce can be shipped Kandla, Mundra, Hazira, Kochi, Tuticorin, Chennai, Kattupalli, Onion 1 2111549 130 Kakinada, Visakhapatnam Kandla, Mundra, Hazira, Kochi, Tuticorin, Chennai, Kattupalli, Tomato 2 219718 19 Kakinada, Visakhapatnam Fruits Rank Production Kandla, Mundra, Hazira, Kochi, Tuticorin, Chennai, Kattupalli, Grapes 1 449061 100 Kakinada, Visakhapatnam Kandla, Mundra, Hazira, Kochi, Tuticorin, Chennai, Kattupalli, Banana 2 75223 12 Kakinada, Visakhapatnam Kandla, Mundra, Hazira, Kochi, Tuticorin, Chennai, Kattupalli, Mango 3 47053 12 Kakinada, Visakhapatnam All the top vegetables and fruits grown in CEZ North Konkan have shelf lives of 12 or more days. The logistical TAT from JNPT port is 9 to 12 days to the rest of the ports in the selected CEZ, therefore, all the products growing close to JNPT port could be shipped to those ports. Table 39: Product-TAT comparison of fruits and vegetables produced near Kochi port (CEZ Malabar). Maximum Destination Ports where the produce can be Vegetables Rank Production Days shipped Tapioca 1 1366279 Perishability data not available Drumstick 2 6053 Perishability data not available Kandla, Mundra, Hazira, JNPT, Tuticorin, Ginger 3 1543 175 Chennai, Kattupalli, Kakinada, Visakhapatnam Sweet Kandla, Mundra, Hazira, JNPT, Tuticorin, Potato 4 220 160 Chennai, Kattupalli, Kakinada, Visakhapatnam Fruits Rank Production Kandla, Mundra, Hazira, JNPT, Tuticorin, Coconut 1 1438666667 40 Chennai, Kattupalli, Kakinada, Visakhapatnam Kandla, Mundra, Hazira, JNPT, Tuticorin, Banana 2 541406 12 Chennai, Kattupalli, Kakinada, Visakhapatnam Kandla, Mundra, Hazira, JNPT, Tuticorin, Mango 3 121886 12 Chennai, Kattupalli, Kakinada, Visakhapatnam Kandla, Mundra, Hazira, JNPT, Tuticorin, Pineapple 4 60578 16 Chennai, Kattupalli, Kakinada, Visakhapatnam Papaya 5 27916 9 Tuticorin 83 The two top vegetables grown near CEZ Malabar to be considered for step b, ginger and sweet potato, both have shelf lives far exceeding the logistical TAT to other ports from the selected CEZs in Table 22. Among fruits however, the shelf life of papaya (9 days) makes the product only suitable to be shipped to Tuticorin port in CEZ Mannar. The other top fruits grown in the region could be easily shipped to all the other ports in the selected CEZs owing to their high perishability thresholds. Table 40: Product-TAT comparison of fruits and vegetables produced near Tuticorin port (CEZ Mannar). Maximum Destination Ports where the produce can be Vegetables Rank Production Days shipped Tapioca 1 119509 Perishability data not available Kandla, Mundra, Hazira, JNPT, Kochi, Chennai, Onion 2 20155 130 Kattupalli, Kakinada, Visakhapatnam Kandla, Mundra, Hazira, JNPT, Kochi, Chennai, Tomato 3 6931 19 Kattupalli, Kakinada, Visakhapatnam Eggplant 4 3271 5 None Okra 5 2565 3 None Fruits Rank Production Kandla, Mundra, Hazira, JNPT, Kochi, Chennai, Coconut 1 243798567 40 Kattupalli, Kakinada, Visakhapatnam Kandla, Mundra, Hazira, JNPT, Kochi, Chennai, Banana 2 413495 12 Kattupalli, Kakinada, Visakhapatnam Kandla, Mundra, Hazira, JNPT, Kochi, Chennai, Mango 3 23150 12 Kattupalli, Kakinada, Visakhapatnam Kandla, Mundra, Hazira, JNPT, Kochi, Chennai, Jackfruit 4 8144 16 Kattupalli, Kakinada, Visakhapatnam Kandla, Mundra, Hazira, JNPT, Kochi, Chennai, Pomefruit 5 2287 40 Kattupalli, Kakinada, Visakhapatnam Among the top vegetables grown near Tuticorin port in CEZ Mannar, and considered for this step, eggplant and okra cannot be shipped to any other ports in the selected CEZs for their shorter perishability threshold compared to the logistical TAT. Fruits grown in the region, however, can be supplied to all the ports in the selected CEZs by sea route owing to their high perishability thresholds. 84 Table 41: Product-TAT comparison of fruits and vegetables produced near Chennai port (CEZ VCIC South). Destination Ports where the produce Vegetables Rank Production Maximum Days can be shipped Eggplant 1 12967 5 None Tapioca 2 5383 Perishability data not available Okra 3 4905 3 None Kandla, Mundra, Hazira, JNPT, Kochi, Tuticorin, Kattupalli, Kakinada, Tomato 4 513 19 Visakhapatnam Kandla, Mundra, Hazira, JNPT, Kochi, Tuticorin, Kattupalli, Kakinada, Sweet potato 5 393 160 Visakhapatnam Fruits Rank Production Kandla, Mundra, Hazira, JNPT, Kochi, Tuticorin, Kattupalli, Kakinada, Coconut 1 25849400 40 Visakhapatnam Kandla, Mundra, Hazira, JNPT, Kochi, Tuticorin, Kattupalli, Kakinada, Mango 2 93319 12 Visakhapatnam Kandla, Mundra, Hazira, JNPT, Kochi, Tuticorin, Kattupalli, Kakinada, Banana 3 64116 12 Visakhapatnam Kandla, Mundra, Hazira, JNPT, Kochi, Tuticorin, Kattupalli, Kakinada, Guava 4 800 12 Visakhapatnam Among the top vegetables grown near Chennai port in CEZ VCIC South, and considered for this step, eggplant and okra, again cannot be shipped to any other ports in the selected CEZs for reasons of shorter perishability threshold compared to logistical TAT. For the fruits grown in the region, all the ports were found to be viable for shipping owing to their high perishability thresholds. 85 Table 42: Product-TAT comparison of fruits and vegetables produced near Kattupalli port (CEZ VCIC South). Destination Ports where the produce can be Vegetables Rank Production Maximum Days shipped Eggplant 1 12967 5 None Tapioca 2 5383 Perishability data not available Okra 3 4905 3 None Kandla, Mundra, Hazira, JNPT, Kochi, Tuticorin, Tomato 4 513 19 Chennai, Kakinada, Visakhapatnam Sweet Kandla, Mundra, Hazira, JNPT, Kochi, Tuticorin, potato 5 393 160 Chennai, Kakinada, Visakhapatnam Fruits Rank Production Kandla, Mundra, Hazira, JNPT, Kochi, Tuticorin, Coconut 1 25849400 40 Chennai, Kakinada, Visakhapatnam Kandla, Mundra, Hazira, JNPT, Kochi, Tuticorin, Mango 2 93319 12 Chennai, Kakinada, Visakhapatnam Kandla, Mundra, Hazira, JNPT, Kochi, Tuticorin, Banana 3 64116 12 Chennai, Kakinada, Visakhapatnam Kandla, Mundra, Hazira, JNPT, Kochi, Tuticorin, Guava 4 800 12 Chennai, Kakinada, Visakhapatnam Among the top vegetables grown near Kattupalli port in CEZ VCIC South, and considered for this step, eggplant and okra met the same fate of not being shippable to any other ports in the selected CEZs for reasons of shorter perishability threshold compared to logistical TAT. Fruits grown in the region could be shipped to all the ports owing to their high perishability thresholds. 86 Table 43: Product-TAT comparison of fruits and vegetables produced near Visakhapatnam port (CEZ VCIC North). Maximum Vegetables Rank Production Days Destination Ports where the produce can be shipped Tapioca 1 178077 Perishability data not available Kandla, Mundra, Hazira, JNPT, Kochi, Tuticorin, Tomato 2 100626 19 Chennai, Kattupalli, Kakinada Eggplant 3 82754 5 None Kandla, Mundra, Hazira, JNPT, Kochi, Tuticorin, Onion 4 38222 130 Chennai, Kattupalli, Kakinada Okra 5 37376 3 None Fruits Rank Production Kandla, Mundra, Hazira, JNPT, Kochi, Tuticorin, Coconut 1 2051347333 40 Chennai, Kattupalli, Kakinada Mango 2 1778262 12 JNPT, Kochi, Tuticorin, Chennai, Kattupalli, Kakinada Banana 3 960437 12 JNPT, Kochi, Tuticorin, Chennai, Kattupalli, Kakinada Papaya 4 144375 9 Chennai, Kattupalli, Kakinada Lemon 5 65392 9 Chennai, Kattupalli, Kakinada Among the top vegetables grown near Visakhapatnam port in CEZ VCIC North, and considered for this step, once again, eggplant, and okra were found not eligible for shipping to any other ports in the selected CEZs for reasons of shorter perishability threshold compared to logistical TAT. While the top three fruits grown in the region could be shipped to all the ports owing to their high perishability thresholds, shipping of papaya (ranked 4) and lemon (ranked 5) is limited to Chennai, Kattupalli and Kakinada ports owing to their comparatively lower perishability thresholds in comparison with the logistical TATs. 87 Table 44: Product-TAT comparison of fruits and vegetables produced near Kakinada port (CEZ VCIC North). Vegetables Rank Production Maximum Days Destination Ports where the produce can be shipped Tapioca 1 178077 Perishability data not available Kandla, Mundra, Hazira, JNPT, Kochi, Tuticorin, Chennai, Tomato 2 100626 19 Kattupalli, Visakhapatnam Eggplant 3 82754 5 None Kandla, Mundra, Hazira, JNPT, Kochi, Tuticorin, Chennai, Onion 4 38222 130 Kattupalli, Visakhapatnam Okra 5 37376 3 None Fruits Rank Production Kandla, Mundra, Hazira, JNPT, Kochi, Tuticorin, Chennai, Coconut 1 2051347333 40 Kattupalli, Visakhapatnam Mango 2 1778262 12 JNPT, Kochi, Tuticorin, Chennai, Kattupalli, Visakhapatnam Banana 3 960437 12 JNPT, Kochi, Tuticorin, Chennai, Kattupalli, Visakhapatnam Papaya 4 144375 9 Chennai, Kattupalli, Visakhapatnam Lemon 5 65392 9 Chennai, Kattupalli, Visakhapatnam Among the top vegetables grown near Kakinada port in CEZ VCIC North and considered for this step, eggplant and okra cannot be shipped to other ports on account of shorter perishability threshold compared to logistical TAT. Once again in the case of Kakinada port, the top three fruits grown in the region could be shipped to all the ports owing to their high perishability thresholds, but papaya and lemon could only be shipped via sea route to Chennai, Kattupalli and Kakinada ports because of their lower perishability thresholds in comparison with the logistical TATs. On the basis of the product results listed in step a (Table 30) compared with results in section 4.1.2 (Table 22), certain product-route combinations were considered invalid. Summary Table 45 lists the fruits which are considered. 88 Table 45: Conclusions of Product-TAT comparison for fruit. S.NO. FRUITS RESULT 1 Papaya Met logistic TAT for some or all ports 2 Date Met logistic TAT for some or all ports 3 Mango Met logistic TAT for some or all ports 4 Pomegranate Met logistic TAT for some or all ports 5 Banana Met logistic TAT for some or all ports 6 Sapota Did not meet logistics TAT for any port route 7 Indian Jujube Did not meet logistics TAT for any port route 8 Grapes Met logistic TAT for some or all ports 9 Coconut Met logistic TAT for some or all ports 10 Pineapple Met logistic TAT for some or all ports 11 Jackfruit Met logistic TAT for some or all ports 12 Pomefruit (apple/pear) Met logistic TAT for some or all ports 13 Guava Met logistic TAT for some or all ports 14 Watermelon Production data is not available 15 Lemon Met logistic TAT for some or all ports Among product-TAT comparison for all fruits, papaya and lemon meet the logistical TATs only for a few ports. Sapota and the Indian jujube do not meet the criteria for shipping to any port. It should also be noted here that production data for watermelon was unavailable. 89 A summary with respect to vegetable perishability and logistical TAT comparisons is summarised below in Table 46. Table 46: Conclusions of Product-TAT comparison for vegetables S NO. VEGETABLES RESULT 1 Onion Met logistic TAT for some or all ports 2 Tomato Met logistic TAT for some or all ports 3 Eggplant Did not meet logistics TAT for any port route 4 Cabbage Met logistic TAT for some or all ports 5 Okra Did not meet logistics TAT for any port route 6 Cucurbits Did not meet logistics TAT for any port route 7 Cauliflower Met logistic TAT for some or all ports 8 Tapioca No perishability data available 9 Drumstick No perishability data available 10 Ginger Met logistic TAT for some or all ports 11 Sweet potato Met logistic TAT for some or all ports Among product-TAT comparison for all vegetables, perishability data for tapioca and drumsticks was unavailable therefore not considered for this step at all. Cucurbits, eggplant and okra did not meet the logistical TATs to any of the ports in the selected CEZs from section 4.1.2. All other vegetables met the logistical TAT criteria for all ports across the CEZ selection therefore could be shipped to all ports. This summary of product perishability-route combination was further refined under step c in the next section, to arrive at products that were in oversupply (as defined in the methodology) in the selected CEZs for shipment to other CEZs. 90 4.1.2.3. Oversupply in the CEZ of production To check oversupply of the CEZ-wise products shortlisted in criteria b, production per capita per month for the harvest months for each product was compared to their average consumption per month at the national level. First, the latest population data available from every district within the CEZ was listed (Table 47). Below is the population of all districts in the individual selected CEZs. Table 47: District-wise population data under each selected CEZ 65 CEZ State Districts Population 1 Kutch Gujarat Kutch 2,092,371 TOTAL 2,092,371 Nashik 6,107,187 Thane 11,060,148 2 North Konkan Maharashtra Mumbai 3,085,411 Pune 9,429,408 Raigarh 2,634,200 TOTAL 32,316,354 Ernakulam 3,282,388 Alappuzha 2,127,789 3 Malabar Kerala Kollam 2,635,375 Thiruvananthapuram 3,301,427 TOTAL 11,346,979 Kanyakumari 1,870,374 4 Mannar Tamil Nadu Tirunelveli 3,077,233 Thoothukudi 1,750,176 TOTAL 6,697,783 Thiruvallur 3,728,104 5 VCIC South Tamil Nadu Chennai 4,646,732 Kancheepuram 3,998,252 TOTAL 12,373,088 Guntur 4,887,813 Krishna 4,517,398 West Godavari 3,936,966 6 VCIC North Andhra Pradesh East Godavari 5,154,296 Visakhapatnam 4,290,589 Vizianagaram 2,344,474 Srikakulam 2,703,114 TOTAL 27,834,650 Dahej 13495 7 Suryapur Gujarat Hazira 16724 TOTAL 30219 65 http://www.censusindia.gov.in/(S(ogvuk1y2e5sueoyc5eyc0g55))/Tables_Published/Basic_Data_Sheet.aspx 91 Next, oversupply of the shortlisted products was determined. The production in kg in each selected CEZ was divided by the number of harvest months to arrive at average production per month (Table 48). Table 48: Total per capita monthly product consumption (in kg) data for selected produce in urban areas in 2011-2012 66 Vegetable Urban (kg/month) Remarks Potato 1.612 Shall not be considered as it does not meet logistics TAT in terms of Eggplant 0.358 perishability Onion 0.951 Tomato 0.806 Shall not be considered as it does not meet logistics TAT in terms of Cucurbits 0.387 perishability Cabbage 0.271 Tapioca n/a Perishability and consumption data not available Cauliflower 0.326 Shall not be considered as it does not meet logistics TAT in terms of Okra 0.281 perishability Garlic 0.081097 Ginger 0.07313 Drumstick n/a Perishability and consumption data not available Sweet Potato 0.009 Data of 2009-10 (2011-12 data not available) Fruit Urban (kg/month) Consumption of banana is 6.69 units /month/person. Assuming one banana weighs 0.16 kg, we take consumption in kgs. /month/person as 1.07 kg. Banana 1.07 (Assumption based on average weight of banana to be around 160 gms 67) Mango 0.202 Papaya 0.081 Sapota n/a Consumption data not available Grapes 0.084 Consumption of lemon is 2.117 units/month/person. Assuming the average weight of a lemon to be 0.058kgs, we take consumption in Lemon 0.123 kgs./month/person as 0.123 kg 68 Pomegranate n/a Consumption data not available Consumption of coconut is 0.755 units /month/person. Assuming one coconut weighs 1 kg, we take consumption in kgs. /month/person as 0.755 Coconut 0.755 kg. (Assumption based on average weight of a coconut to be 1 kg/piece. 69 ) Pome Fruits (Apple, Pears) 0.195 Guava 0.088 Jackfruit 0.008 Date 0.015 Consumption of Pineapple is 0.027 units /month/person. Assuming weight of one pineapple to be 1 kg, we take consumption in kgs. /month/person as Pineapple 0.027 0.027kg. (Assumption based on average weight pineapple to be 1 kg. 70) Indian Jujube n/a Consumption data not available Products not considered going further are in red text in the table. 66 http://mospi.nic.in/sites/default/files/publication_reports/Report_no558_rou68_30june14.pdf https://www.iivr.org.in/sites/default/files/Technical%20Bulletins/7.%20Vegetable%20Statistics.pdf 67 http://www.foodstandards.gov.au/science/monitoringnutrients/ausnut/foodmeasures/Pages/-Fruits-and-vegetable-measures-program- --data-table.aspx 68 http://www.kgbanswers.co.uk/how-much-does-the-average-lemon-weigh/2155453 69 http://www.clovegarden.com/ingred/coconut.html 70 http://www.traditionaloven.com/foods/multi-units-converter/pineapple-raw-extra-sweet-variety.html 92 Since not all products grow throughout the year, the months in which the products are harvested and available in the market for selling are considered. This average production per month was then further divided by the population reflected in Table 47 to arrive at production per month per kg per person. Above is the data with respect to per capita monthly consumption of the fruits and vegetables (in kg) at national level shortlisted in criteria b (Table 48). The remarks in the tables make it clear why a product was considered or eliminated going further. And the cases in which the data was available in numbers and not in kg, the calculation has also been explained. Oversupply is said to occur when the production is double or more than the consumption quantity in harvest time. For e.g. in CEZ Kutch, consumption of onion (all 12 months) x 2 = 1.90 kg/month/person; Production of onion in CEZ Kutch (also harvested all 12 months of the year) = 3.66 kg/month/person. The quantity of production was found to be higher than twice the consumption quantity, therefore onion was considered to be in oversupply in CEZ Kutch. A summary specifying the CEZs, in which oversupply of fruits is observed, is presented below (Table 49). Table 49: Summary of CEZs in which specific fruits were in oversupply CEZ Suryapur CEZ Malabar CEZ Mannar CEZ North CEZ Kutch CEZ VCIC CEZ VCIC Konkan North South CEZs FRUITS Papaya x x x x Date palm (date) x Mango x x x x Banana x x x x Grapes x Coconut x x x x Pineapple x Jackfruit x Pomefruit (apple, pear) Guava Lemon x 93 The summary of CEZs, in which specific vegetables were observed to be in oversupply, is presented below (Table 50). Table 50: Summary of CEZs in which specific vegetables were in oversupply CEZ North Konkan CEZ VCIC North CEZ VCIC South CEZ Suryapur CEZ Malabar CEZ Mannar CEZ Kutch CEZs VEGETABLES Onion x x Tomato x x x x Cauliflower x Ginger Sweet potato Cabbage x The products resulting from step c were further explored across various combinations of routes by applying the criterion d pertaining to the undersupply of products in one or more of the selected CEZs. 4.1.2.4. Undersupply in one of the destination CEZs Average monthly wholesale prices over the most recent three years were collated in one big city in the sourcing CEZ and destination CEZ each. As per criterion d in the methodology, the big city with the wholesale market must meet two conditions: i. it must have a population of more than 1 million people; ii. it should be situated within approximately 200 km to 300 km 71 of the production district. The list of 17 such big cities that meet the criteria are listed below (Table 51). 71 The city could be in another CEZ, as stated in the methodology. 94 Table 51: Big cities with population greater than 1 m with wholesale opportunities identified in each corresponding potential destination CEZ Sr. List of Cities >1 m No. CEZ States and within 200-300 km Population No city more than 1 m population within Kutch Gujarat 200 km to 300 km 1 Ahmedabad 5.6 million 2 Surat 4.5 million Suryapur Gujarat 3 Vadodara 2.1 Million 4 Rajkot 1.3 million 5 Mumbai 18 million 6 Pune 3.1 million 7 Nashik 1.5 million 8 Vasai- Virar 1.2 million North Konkan Maharashtra 9 Pimpri-Chinchwad 1.7 Million 10 Thane 1.9 million 11 Kalyan-Dombivli 1.2 million 12 Navi Mumbai 1.12 million 13 Malabar Tamil Nadu Coimbatore 1.6 million 14 Mannar Tamil Nadu Madurai 1.4 million 15 Tamil Nadu Chennai 7.1 million VCIC South 16 Puducherry Puducherry 1.2 million Andhra 17 VCIC North Pradesh Visakhapatnam 2.0 million Simultaneously, various routes between ports from the selected CEZs were also determined based on the port-wise connectivity available (Table 17 and Table 18). Routes under scenario 1 based on port-to-port CEZ connectivity are listed below in Table 52: 95 Table 52: Routes inferred from connectivity Table 17 in Scenario 1 Origin Destination Kandla - Kutch JNPT - North Konkan Mundra - Kutch JNPT - North Konkan Mundra - Kutch Kochi - Malabar Mundra - Kutch Tuticorin -Mannar Mundra - Kutch Kattupalli VCIC South JNPT - North Konkan Mundra - Kutch Kochi - Malabar Mundra - Kutch Kochi - Malabar Tuticorin -Mannar Tuticorin -Mannar Mundra - Kutch Tuticorin -Mannar Kochi - Malabar Tuticorin -Mannar Kattupalli VCIC South Kattupalli VCIC South Mundra - Kutch Kattupalli VCIC South Kochi - Malabar Kattupalli VCIC South Tuticorin -Mannar Several route combinations in both Scenario 1 and 2 revealed that the distance between them by road can be traversed more swiftly than the time it would take for shipping by sea between many of these ports. For example, the road distance between Hazira port and JNPT port is 343.9 km, which can be covered in 5.7 hours. At 60 kmph speed as per the port-to-port transit times shared in Table 21 it would take almost a day using the short sea route from Hazira port to JNPT port. Similarly, the road distance between Kochi port and Tuticorin port is 320.6 km and can be covered at a speed of 60 kmph in 5.34 hours. The time taken via the sea route between Kochi port and Tuticorin port is also nearly 24 hours (Table 21). Therefore, only the route combinations highlighted in yellow in the above Table 52 were taken into account for the purpose of this study. Criteria d mandated that there must be at least one big city with a population of over 1 million within 200 km to 300 km from the production districts. The national census data revealed that not a single city that met the conditions above was in close proximity to the ports in Kutch – the only district in CEZ Kutch. However, as observed in Table 17 and Table 18, Mundra port enjoys extensive shipping connectivity with other ports across CEZs selected for this study. Therefore, for the purpose of this study, Mundra port was to be considered for districts of CEZ Suryapur (Rajkot is 254.3 km away) as an exception. 96 Routes based on port-to-port CEZ connectivity in Scenario 2 are listed below in Table 53. Table 53: Routes inferred from connectivity Table 18 in Scenario 2 Origin Destination Mundra - Kutch Hazira - Suryapur Mundra - Kutch JNPT - North Konkan Mundra - Kutch Kochi - Malabar Mundra - Kutch Tuticorin -Mannar Mundra - Kutch Kattupalli - VCIC South Mundra - Kutch Visakhapatnam - VCIC North Mundra - Kutch Kakinada - VCIC North Hazira - Suryapur Mundra - Kutch Hazira - Suryapur JNPT - North Konkan JNPT - North Konkan Mundra - Kutch Kochi - Malabar Mundra - Kutch Kochi - Malabar Hazira - Suryapur Kochi - Malabar Tuticorin -Mannar Tuticorin -Mannar Mundra - Kutch Tuticorin -Mannar Hazira - Suryapur Tuticorin -Mannar Kochi - Malabar Tuticorin -Mannar Kattupalli VCIC South Tuticorin -Mannar Visakhapatnam - VCIC North Kattupalli VCIC South Mundra - Kutch Kattupalli VCIC South Kochi - Malabar Kattupalli VCIC South Tuticorin -Mannar Vizag - VCIC North Mundra - Kutch Vizag - VCIC North Kochi - Malabar Vizag - VCIC North Tuticorin -Mannar Vizag - VCIC North Kattupalli VCIC South Routes highlighted in yellow in both scenarios were thus noted to be logistically most viable for short sea shipping, even though shipping connectivity exists in all cases. Based on these routes it was clear that the shipping route modality for produce in oversupply in Suryapur CEZ could be explored to CEZs Malabar, Mannar, VCIC South and VCIC North. Additionally, produce in oversupply from these CEZs to CEZ Suryapur could also be explored for short sea shipping based on price differences between the origin and destination markets. Table 45 and Table 46 in criteria c list the fruits and vegetables that will survive the logistical turnaround time and are also in oversupply and shall be considered. This led to the consideration of banana, mango, cauliflower and tomato for supply from CEZ Suryapur to other selected CEZs. Mango was eliminated from consideration for development or implementation since its season had passed for the implementation time duration of this project. Thus, banana, cauliflower and tomato were the remainder products for consideration. 97 The methodology for this project considered wholesale prices in calculating the price difference between the origin and destination markets to deduce presence of opportunities. As per the criterion d, destination CEZ markets had to have a price difference in the monthly price averages of at least INR 1,000 /quintal in order for the product to be considered for supply from the origin CEZ. Results of the analysis of these product-route combinations (PRCs) are presented in Table 54 to Table 57 below. The price difference is a necessary condition for the determination of undersupply in the destination CEZ. The sourcing CEZ particularly must be within approximately 200 km to 300 km distance from the main production area. The large wholesale market at the destination CEZ must also be approximately 200 km to 300 km from the port. For banana, in the destination markets, data was only available for one month in the case of CEZ Malabar, no data was available for CEZ Mannar and CEZ VCIC South. Those routes were therefore not considered for the shipping of banana. Therefore, the shipping route between CEZ VCIC North and CEZ Suryapur (Table 54) was explored. Table 54: Comparison of monthly average prices of banana in CEZ VCIC North and CEZ Suryapur. Average price Price difference (INR/Quintal) (INR/Quintal) Month Visakhapatnam Vadodara JAN 123.95 1218.08 FEB 113.69 1006.93 MAR 136.39 934.17 APR 118.76 1030.89 MAY 128.76 998.78 JUN 1060 111.78 JUL 575 595.23 AUG 190.57 914.29 SEP 180 809.1 OCT 161.2 898.02 NOV 135.71 1151.26 DEC 144.89 1217.64 TOTAL PRICE 2878.35 10886.17 Avg monthly price 239.86 907.18 667.32 98 Comparison of both wholesale markets reveals that the average price difference for banana between them was less than 1000 INR per quintal and therefore does not meet the criteria. The PRC was therefore not considered. For tomato supply from CEZ Suryapur to other CEZs, data availability was only for one month in the case of CEZ Malabar and CEZ VCIC South, and for two months in CEZ Mannar. These PRCs were also therefore dropped. Next, prices between the wholesale markets in CEZ VCIC North and CEZ Suryapur for tomato were compared (Table 55). Table 55: Comparison of monthly average prices of tomato in Visakhapatnam in CEZ VCIC North and in Vadodara in CEZ Suryapur. Price Average price difference (INR/Quintal) (INR/Quintal) Month Visakhapatnam Vadodara JAN 820.96 115.42 FEB - 775.04 MAR 850 177.17 APR 850 66.78 MAY 1088.71 231.11 JUN 1419.55 480.63 JUL 1056.81 923.41 AUG 678.84 422.58 SEP 802.3 104.85 OCT 806 93.51 NOV 666 744.79 DEC 879.46 78.1 TOTAL PRICE 9918.63 4213.39 Avg monthly price 901.69 351.12 550.57 Prices between CEZ VCIC North and CEZ Suryapur (Table 55) were compared and the price difference was found, once again, to be less than 1000 INR again, so this PRC was dropped too. For the supply of cauliflower from CEZ Suryapur to other CEZs, data was not available for CEZ Malabar, CEZ Mannar and CEZ VCIC North to ascertain the price difference against their destination markets. In VCIC South, data was only available for a month in one year. This PRC was therefore eliminated altogether as well. Here onwards, produce in oversupply from CEZ Malabar, CEZ Mannar, CEZ VCIC South and VCIC North were considered for shipment to CEZ Suryapur. 99 In CEZ Malabar, coconut, banana, mango and pineapple were considered for shipment to CEZ Suryapur. Data for pineapple was not available, and so was not considered for price comparison. Only a month’s data was available for banana; and as stated earlier, the season for mango had already passed. Therefore, the only produce that could be considered for supply from CEZ Malabar to CEZ Suryapur was coconut. Table 56: Comparison of monthly average prices of coconut in Coimbatore in CEZ Malabar and in Surat in CEZ Suryapur. Average price Price difference (INR/Quintal) (INR/Quintal) Month Coimbatore Surat JAN 1016.67 16483.33 FEB 1109.01 MAR 1084.71 13915.29 APR 1109.66 14888.89 MAY 1203.33 3176.91 JUN 1174.34 JUL 1195.15 20554.06 AUG 1217.41 21282.59 SEP 1224.82 OCT 1322.82 19569.75 NOV 1172.72 18104.21 DEC 1273.56 TOTAL PRICE 14104.2 127975.03 Avg monthly price 1175.35 15996.88 14821.53 This PRC appeared attractive, however, the price information available for coconut in CEZ Suryapur was dated. Data with respect to current prices of coconut in CEZ Suryapur was therefore taken into consideration for comparison between the origin wholesale market in CEZ Malabar and destination CEZ Suryapur (Table 56). When the average price of coconut in CEZ Malabar (INR 1175.35) and CEZ Suryapur’s current price for coconut (2400 INR 72/month/quintal) 73 was compared, the price difference was approximately 1225 INR, which was still higher than 1000 INR per quintal – allowing the PRC to meet criterion d. In CEZ Mannar mango, banana, coconut, and jackfruit were eligible for shipment to CEZ Suryapur. However, price data for banana and jackfruit wasn’t found and the season for mango had passed, as mentioned earlier. 72 The weight of each coconut is approximately 1kg and the price per coconut was 24 INR. A quintal is 100 kg, therefore price for a quintal was (100* 24=) 2400 INR, Appendix 17 73 https://www.apmcahmedabad.com/ , http://www.wholesalehub.in 100 Therefore, the shipment of coconut from CEZ Mannar to CEZ Suryapur was considered (Table 57). Table 57: Comparison of monthly average prices of coconut in Madurai in CEZ Mannar and in Surat in CEZ Suryapur Average price Price difference (INR/Quintal) (INR/Quintal) Month Madurai Surat JAN FEB MAR 6300.95 8699.05 APR 6266.67 9731.88 MAY 5593.75 -1213.51 JUN 5838.69 JUL 5637.5 16111.71 AUG 5770 16730 SEP 5666.76 OCT 6375.03 14517.54 NOV 3644.96 15631.97 DEC 4347.5 TOTAL PRICE 55441.81 80208.64 Avg monthly price 5544.18 11458.38 5914.20 When the average price of coconut in CEZ Mannar (INR 5544.18) and CEZ Suryapur (11458.38 INR) and the current price for coconut (2400 INR) in CEZ Suryapur were compared, the price in CEZ Suryapur was determined to be lower by 3144.18 INR with respect to current pricing. Hence, as the price in the destination CEZ is already lower than the price in origin, the PRC was not considered. In CEZ VCIC South, only coconut and mango were found to be in oversupply. But mango was not to be considered due to the harvest season and data for coconut was not available. This PRC was therefore not considered. In CEZ VCIC North, coconut, banana, mango and tomato could have been considered for supply to CEZ Suryapur. However, since Mango season would have passed by the implementation time for the project, comparisons for banana (Table 54) and tomato (Table 55) and have already been made and the PRCs found unviable due to a price difference lower than INR 1000 per Quintal, data for coconut was scarce and available for only three months, this PRC was also entirely dropped. The analyses of these product routes reveal that CEZ Malabar-CEZ Suryapur for coconut was the only PRC to meet criterion d. Figure 13 below is a visual representation of the selected PRC. 101 Figure 13: Route map of PRC: CEZ Malabar to CEZ Suryapur by short sea shipping route The map above shows the route from the source CEZ Malabar to the destination CEZ Suryapur, including the wholesale markets in or nearest to the respective CEZs. The PRC selected for further analysis is shown in Table 58 below. Table 58: Selected PRC for further analysis Origin Origin Origin Destination Destination Destination Product CEZ Port State CEZ Port State Malabar Kochi Kerala Suryapur Hazira/Mundra Gujarat Coconut 102 With the identification of a viable PRC the process of product selection is completed. Fruits and vegetables with sufficient shelf life were shortlisted. Of these products, there had to be abundance in a certain CEZ with a well-connected port and a shortage in certain other CEZs with an attractive port. In the next section, potential markets and farmers for the PRC were identified and scored to arrive at attractive Farmer-Market Combinations (FMCs) for further supply chain design and evaluation. But in this section at least an initial feasibility check was explored from transportation costs and product loss perspectives. 4.1.3. Initial feasibility check In this section, the cost feasibility of short sea shipping for the selected PRC is analysed. Additionally, its competitiveness is deduced in comparison with road transport – both, ambient trucks as well as reefer vans – in terms of costs, lead time and product losses. 4.1.3.1. Financial feasibility Cost feasibility of short sea shipping of coconut was determined by comparing wholesale market prices at CEZ Malabar (origin) with prices at CEZ Suryapur (destination) where the product in oversupply is to be sold. First, the logistics costs for various modes of transport from farm in CEZ Malabar to buyer in CEZ Suryapur were listed (Table 59). Table 59: Cost of ambient truck, reefer van and short sea shipping from farm in CEZ Malabar to buyer in CEZ Suryapur Amount Mode of Transport (INR) Ambient transport - 19-ton of truck load 148000 74 Reefer transport - 19-ton truck load 222000 75 Short sea Shipping – 40-ft reefer container 76 186860 77 Since the coconuts will be under refrigeration in the case of reefer van and short sea shipping, it is assumed that there shall be no loss incurred on the produce. Therefore, only product loss in the case of ambient trucking was calculated (Table 60). 74 http://www.truckbhada.com 75 Assumed at 1.5x cost of ambient trucking 76 Appendix 20 77 Appendix 17 103 Table 60: Loss incurred in the case of ambient trucking from farm in CEZ Malabar to buyer in CEZ Suryapur (refer Table 59 for assumption of base capacity) Loss of produce in road transit Loss of produce suffered in ambient road transport currently being used is 10% of total weight in kg 1900 Loss in INR, on sale price @24000 INR per ton 45600 Loss value added to the cost of ambient transport, total cost of ambient road transport in INR 193600 When total loss of produce in ambient trucking is added to its cost, the total expense to the buyer is at 193600 INR. 4.1.3.2. Competitiveness Price difference between the wholesale markets at CEZ Malabar and CEZ Suryapur is 1225 INR per Quintal as seen in criterion d (Table 56). Basis this amount the additional earning per ton and total earnings for 19 tons of coconut are listed below in Table 61. Table 61: Additional earnings for the former using short sea shipping to transport the 19 tons of coconuts from CEZ Malabar to CEZ Suryapur Cost Feasibility Analysis for Short Sea Shipping from farm in CEZ Malabar to Amount buyer in CEZ Suryapur (INR) Price difference between CEZ Malabar and CEZ Suryapur Markets for Coconut Per Quintal 1225 Price difference between CEZ Malabar and CEZ Suryapur Per ton (1 ton = 10 quintal) 12250 Total additional earning for seller for 19 tons of coconut 232750 Cost of Short Sea Shipping for 40 ft. reefer container 186860 Net additional earning for 19 ton of coconuts using short sea shipping 45890 Per ton additional earning for farmer 2415 Transit time from CEZ Malabar to CEZ Suryapur by road (ambient trucking as well as reefer van) would take about 7-8 days, whereas the sea route would take 10 days (Table 21). As observed in the above table, despite the marginally longer transport time, the cost of transporting coconuts by short sea shipping is less than the additional profits earned by the farmer from the PRC transaction. Therefore, there is a net earning of INR 2415 per ton (INR 2.66 per kg) for the farmer after deducting all costs. In addition, with economies of scale there is further opportunity to avail lower shipping costs using the short sea route as the vessel can carry more than one container, whereas the cost reduction opportunity in road transport (both ambient and cold) is unavailable with rising fuel costs. 104 Additionally, there is no loss of produce in transit in the redesigned supply chain using the short sea shipping method, whereas in the case of only ambient transport 10% loss is expected. Given the short supply of reefer road transport vehicles in India, there is a high probability of unavailability of the same when needed and the farmer may miss the market opportunity or buyer commitment. 4.2. Selection of farmer-market combinations In the previous phase of this study, the product route combination of transporting coconuts via the short sea shipping route from CEZ Malabar to CEZ Suryapur was selected as the PRC input for this phase of the study. Figure 14: Route map of PRC: CEZ Malabar to CEZ Suryapur by short sea shipping route 105 CEZ Suryapur falls in the western-most state of India, Gujarat. The dry coconut or ‘copra’ is used as a food item and as holy offering in shrines and on special occasions across the country. However, 10 per cent of the total production of coconuts in India is consumed in its tender form. This form of the fruit is a source of coconut water, which is a popular thirst quencher known to possess health and hygienic value and is a popular agrarian business. Virgin coconut oil is also extracted from the fresh kernel without any chemical processing, in turn boasting of rich vitamins, minerals and anti-oxidants, therefore making it a popular oil – whether for cosmetic, food, or industrial use. Most production of coconut is purchased by traders, wholesalers or online retailers. Major urban wholesale markets within the stipulated 200-km to 300-km distance of CEZ Suryapur include Rajkot, Ahmedabad, Vadodara and Surat cities. An approach guided by the market demand was used to arrive at the most attractive farmer-market combinations after matching market players and farmers who produce/aggregate the coconut crop. The chief operatives within this approach comprise characteristics of the markets, and requirements of the markets that would make them most attractive for the implementation of this methodology. In the next section, market players who may be willing to procure coconut from CEZ Malabar are identified. To ensure confidentiality as per the request of market players as well as farmers, in this sections of determining farmer-market combinations, pseudo names are used. 4.2.1. Market research 4.2.1.1. Identification of market players With reference to the product route combination considered promising, various market players were identified in the western part of India, in the 200-km to 300-km area from CEZ Suryapur as defined in the methodology. The Gujarat market comprises several players selling coconut. To ensure that farmer-market combinations calculated were inclusive of the various market opportunities available to the farmer, various types of market representatives were interviewed (Table 62). Interviews were conducted with market players in Rajkot, Ahmedabad, Vadodara and Surat cities of Gujarat and the Mumbai Metropolitan area. Even though the market players in the Mumbai Metropolitan Area were not within the stipulated 200-km to 300-km distance from Mundra Port in the destination CEZ Suryapur, they are close to the distance from Hazira Port 78 as it was also concluded to be a possible option for destination in the route selection. Additionally, the shipping line operating the fleet from Kochi port to Mundra also makes a stop at Hazira Port. 78 On-land distance from Hazira port to Mumbai is 308.2 km. 106 The identified market players expressed interest in additional suppliers of coconuts, and in sourcing the product through short sea shipping transportation. Wholesale markets, traders, online retail chains in urban areas, retailers, foodservice companies and other outlet organizations corresponding to the potential destination of CEZ Suryapur were identified by interviewing market representatives of the same. Table 62 Identified market players for coconut corresponding to the destination CEZ Suryapur 79. Stakeholder type Name of Market Location Online Retailer Farmfresh Mumbai Online Retailer Go2fresh Mumbai Wholesaler Sameer Surat Wholesaler LPO Surat Wholesaler Patidar Traders Ahmedabad Wholesaler Sun Traders Ahmedabad Wholesaler Triumph Traders Ahmedabad Wholesaler PP Enterprises Rajkot Wholesaler Gopal Coconut Vadodara The identified number of market players was nine. Of these, seven were wholesalers and two were online retailers. Of these shortlisted, six players (all wholesalers) were unable to purchase large quantity beyond 2 MT, whereas the quantity carried by the 20-ft container is approximately 19 MT. These players were therefore not considered for the purpose of determining the farmer-market combination. One of the wholesalers from Gujarat, and the two online retailers from the Mumbai Metropolitan area expressed interest to purchase the stipulated minimum quantum. They also displayed willingness to enter into long-term partnerships based on assured delivery and consistent quality of produce and reliable constant supply apart from competitive pricing. Below are the market players that were identified for the purpose of this study for conducting the farmer-markets match analysis. 80 79 For the purpose of maintaining confidentiality of stakeholders, all names have been changed to pseudo-names. 80 Appendices 2, 3, and 4 107 Table 63 Shortlisted market players for coconut corresponding to the destination CEZ Suryapur. Stakeholder type Name of Market Location Online Retailer Farmfresh Mumbai Online Retailer Go2fresh Mumbai Wholesaler Sameer Surat As stated in the methodology, the aim was to find sufficient market players to be able to identify the most promising farmer-market combinations3.2.3. After potential market players were shortlisted, the attractiveness of these market players was sought to be identified through a second round of interviews. 4.2.1.2. Determination of market player attractiveness As referred to in the methodology, markets were measured based on a range of criteria (e.g. information sharing, payment, like to add products, etc.). The category of (potential) markets (e.g. wholesalers, online retailers, etc.) as well as the importance they attached to each of these criteria was determined. Based on consensus among the various stakeholders, weightages were assigned to each of these criteria that can help in the redesign of the supply chain. Weightage of the criterion affects the final score. Attractiveness of the market is thus determined based on whether high weightage criteria are adequately fulfilled. Apart from the type of supplier from whom the market players sourced their products, below is the list of relevant criteria considered for the measurement and scoring of market players: Information Sharing: What type of information was shared by this market player with their partners at and until the time of the interview? (E.g. None, Rejected, Indent, Order, or Planning) Payment: How did the market player want to pay the purchase price to its supplier(s) and when? (E.g. online after 15 days, cash after 15 days, online transfer, immediate cash, or advance payment) Sales/Purchase Price: Which were the expected sales and purchase prices of the considered product at the time of the interviews? (E.g. less than market price, market price, average price, or more than market price) 108 Sourcing Period: For market players, during which calendar period was the considered product sourced at the time of the interviews? (E.g. not confirmed, after 15 days, after a week, after 4 days, or immediate) As shortlisted market players indicated during the interviews that they sourced coconuts throughout the calendar year, this criterion is irrelevant. Therefore, this criterion was not scored and excluded from the results and analyses. Trend of Sales: Which sales trend existed during the sourcing period? (E.g. decreasing, fluctuating, stable, slightly increasing, or increasing) Volume Estimate per Week: What was the estimated volume of the considered product that the market player expected to sell in the specific time period pertaining to this study? (e.g. 500 pieces, 1000 pieces, 2800 pieces, 3500 pieces, or 5000 pieces) Sourcing Stakeholders: Varied sourcing players are identified under this parameter to determine which markets buy from which suppliers along the supply chain. (E.g. online retailers, trader, wholesaler, aggregator, or farmer) Like to Add Products: This criterion helps to identify if the market is expecting to procure more variety of the same produce. (E.g. none, one, or more than 1) In the table below, a uniform scoring range (0-4) is assigned to each possible answer corresponding to each of these criteria. Table 64 Uniform scoring range with each possible answer corresponding to each criterion with the score assigned. Scoring range Criteria 0 1 2 3 4 Info sharing None Rejected Indent Order Planning online after cash after 15 Payment 15 days days online transfer Imm. Cash adv. Payment less than more than market Sales/ purchase price market Market price - Average price price Slightly Trend of sales Decreasing Fluctuating Stable increasing Increasing Volume est. per week 500 pcs 1000 pcs 2800 pcs 3500 pcs 5000 pcs online Sourcing stakeholders retailers trader wholesaler aggregator farmer Like to add products none - one - more than 1 109 As indicated in the methodology, for the scoring and determination of the overall market player attractiveness, the following set of steps included: 1. The range of possible answers to each criterion was determined; 2. Possible answers with respect to attractiveness were arranged L to R- least to most attractive; 3. A score to each possible answer (where 0 is least attractive and 4 is most attractive) was assigned; 4. Weightage to each criterion was assigned; 5. During the interviews of the market players, this information was also collected. 6. The answers received from the market players were scored in order to determine their attractiveness (sum of weighted scores). Below are the scores tabulated and represented as per the example in the methodology. Each table corresponds to one market player of the three considered to be sufficiently attractive and selected for the determination of the market player requirements. The scoring has been carried out based on the ranges defined (also mentioned in the table below) and the weighted sum calculations of the relevant criterion scores of the three market players. Table 65 Determination of market attractiveness of online retailer, FARMFRESH WEIGHT MAX MAX. WEIGHTA FARMFRE ED SCOR WEIG Scoring range GE SH SCORE E HT Criteria 0 1 2 3 4 (H*I) (H*K) Plannin info sharing None Rejected Indent Order g 2 4 8 4 8 online cash adv. after 15 after 15 online Imm. Paymen payment days days transfer Cash t 3 0 0 4 12 more than Sales/ purchase less than Market Average market price market price - price price 3 3 9 4 12 Slightly Decreasi Fluctuati increasi Increasi Trend of sales ng ng Stable ng ng 1 2 2 4 4 volume est. per 2800 3500 5000 week 500 pcs 1000 pcs pcs pcs pcs 2 4 8 4 8 sourcing online wholesa aggregat stakeholders retailers trader ler or farmer 3 3 9 4 12 like to add more products none - one - than 1 1 4 4 4 4 TOTAL 40 60 MARKET ATTRACTIVE NESS (%) (Wt Score/ Max Wt) 67% The total weighted score calculated for FARMFRESH is 40 and the maximum weighted score is 60. Its market attractiveness is therefore 67% (40/60). 110 Table 66 Determination of the overall attractiveness of GO2FRESH. WEIGHT MAX. WEIGHTA GO2FRES ED WEIGH Scoring range GE H SCORE MAX T Criteria 0 1 2 3 4 (H*M) (H*O) info sharing None Rejected Indent Order Planning 2 4 8 4 8 online cash after 15 after 15 online Imm. adv. payment days days transfer Cash Payment 3 0 0 4 12 less more than Sales/ purchase than Market Average market price market price - price price 3 3 9 4 12 Decreas Fluctuati Slightly Trend of sales ing ng Stable increasing Increasing 1 2 2 4 4 volume est. per week 500 pcs 1000 pcs 2800 pcs 3500 pcs 5000 pcs 2 2 4 4 8 sourcing online wholesal stakeholders retailers trader er aggregator farmer 3 3 9 4 12 like to add products none - one - more than 1 1 4 4 4 4 TOTAL 36 60 MARKET ATTRACTIVE NESS (%) (Wt Score/ Max Wt) 60% The total weighted score calculated for GO2FRESH is 36 and the maximum weighted score is 60. Its market attractiveness therefore stands at 60% (36/60). Table 67 Determination of the overall attractiveness of WHOLESALER-SAMEER MAX. WEIGHTA WHOLESALE WEIGHTE WEIG Scoring range GE R & Trader D SCORE MAX HT Criteria 0 1 2 3 4 (H*Q) (H*W) info sharing None Rejected Indent Order Planning 2 2 4 4 8 online cash after 15 after 15 online Imm. adv. payment days days transfer Cash Payment 3 3 9 4 12 less more than Sales/ purchase than Market Average market price market price - price price 3 3 9 4 12 Decreas Fluctuati Slightly Trend of sales ing ng Stable increasing Increasing 1 1 1 4 4 volume est. per week 500 pcs 1000 pcs 2800 pcs 3500 pcs 5000 pcs 2 1 2 4 8 sourcing online wholesal stakeholders retailers trader er aggregator farmer 3 3 9 4 12 like to add products none - one - more than 1 1 2 2 4 4 TOTAL 36 60 MARKET ATTRACTIVE NESS (%) (Wt Score/ Max Wt) 60% The total weighted score calculated for WHOLESALER-SAMEER is 36 and the maximum weighted score is 60. Its market attractiveness is also 60% (36/60). 111 The summary of percentage scores is presented in the below table. Table 68 Determination of overall attractiveness percentage of the interviewed market players Stakeholder type Market Percentage (%) Online Retailer Farmfresh 67% Online Retailer Go2fresh 60% Wholesaler Sameer 60% While GO2FRESH and the WHOLESALER – SAMEER, both have scored identical market attractiveness, FARMFRESH has higher market attractiveness at 67%. 81 4.2.1.3. Determination of market player requirements Since this study takes a market-driven approach, product and logistical requirements are considered from the perspective of the market players to determine the most attractive farmer-market combinations. Below is the list of relevant market requirements, with explanation, considered for the measurement and scoring of the market players: Product Variety and Label: This parameter specifies the variety in which the fruit is required – brown coconut or tender coconut and the label if need be. Product Requirement: This parameter identifies what are the exact expectations from the market for product specifications, such as high water-content, minimum scars, natural green colour, etc. for produce. Delivery Acceptance Logistics: This parameter identifies how the market deals with delivery of produce - whether it accepts or rejects the produce based on what the farmer is able to deliver. Delivery Frequency: This parameter identifies different options of delivery frequency as expected by the market and how it matches with farmer’s intention and capability of delivering the product when the market needs it – whether on demand, once a week, twice a week, three times a week, or daily. 81 Please note that the conclusion related to these figures is presented in paragraph 4.2.3.1 because it cannot be evaluated in isolation, but in correspondence to the result in the section on match analysis. 112 Mode of Transport: Different modes of transport as expected by the market and as used by the farmer are matched through this parameter - own transport (bike, auto, vans), part truck, tempo, open/ closed trucked. Packaging: This parameter identifies either of the two types of packaging prevalent and expected by each market player and what the farmer can offer, i.e. loose cargo, vinyl bag or brown jute bag. Sourcing Region: This is a necessary parameter that compares the different options of distances at which the farms are located from the markets for tender coconuts. However, the product-route combinations were evaluated and CEZ Malabar to CEZ Suryapur was already concluded at the end of the product- route selection. The sourcing region is therefore not applicable as a parameter for the remainder of this study and therefore not scored and excluded from the results and analysis. Like to Add Products: This parameter helps to identify if the market is expecting to procure more variety of the same produce. E.g. none, one, or more than one. The table below shows the scoring range assigned to each possible answer, where 0 is for the minimum requirement with least effort and investment, and 4 stands for the highest requirement with proportionate effort and monetary value. Table 69 Scoring range assigned to each possible answer for market requirement criteria Scoring range Criteria 0 1 2 3 4 Product variety and label - - Brown coconut - Tender coconut Product No empty /dry Green natural in 10% to 20% scar 0% to 10%scar on 300-400 ml water requirements nuts colour & Tender on skin skin content Delivery acceptance logistics Rejected - - Accepted Delivery frequency On call Weekly twice a week 3 days in a week Daily Own transport (Bike, Auto, Full truck load (Open / Way of transport - Vans) Part truck Tempo Close) Packaging Loose cargo - white vinyl bag jute bag Like to add products none - one - more than one The table below illustrates the scores of each shortlisted market player for the market requirements considered most vital. 113 Table 70 Score of Market Requirements pertaining to each shortlisted market player Online Online Scoring range Retailer Retailer Wholesaler Criteria 0 1 2 3 4 FARMFRESH GO2FRESH Sameer Product variety and Brown Tender label - - coconut - coconut 4 4 4 Green No natural in 10% to 0% to Product empty colour & 20% scar 10%scar on 300-400 ml requirements /dry nuts Tender on skin skin water content 4 4 4 Delivery acceptance logistics Rejected - - Accepted 4 4 4 Delivery twice a 3 days in a frequency On call Weekly week week Daily 3 1 1 Own transport (Bike, Full truck Way of Auto, load (Open / transport - Vans) Part truck Tempo Close) 4 4 3 Loose white Packaging cargo - vinyl bag jute bag 0 0 4 Like to add more than products none - one - one 4 4 2 As per the methodology, these market player requirements and their scores were input for the match analysis in Section 4.2.3. 4.2.2. Farmer research As per the results of the product-route selection, the produce considered for shipment is coconut and the product-route combination considered most efficient was from origin CEZ Malabar to destination CEZ Suryapur. In the previous section, the market requirement of tender coconut was identified as Product Variety required by the market players. Therefore, two farmers were identified as potential suppliers of tender coconut in the Malabar region – one in Palakkad and one in Nettoor – both in the state of Kerala (Figure 14) who had the potential to meet the methodology criteria. 114 PALAKKAD NETTOOR Figure 15 Map of Kerala 4.2.2.1. Identification of farmers The below table lists the farmers identified and who are willing to enter in a long-term partnership with a market player for transportation through the short sea shipping method for the supply of tender coconut. The production areas lie within approximately 200 km to 300 km distance from the source CEZ Malabar in the southern part of the west coast of India in the state of Kerala. 115 Table 71 Producers of tender coconuts identified for the study Stakeholder type Name of Producer Location Farmer & Aggregator AAR Palakkad Farmer Manu Nettoor A decline in production of tender coconut was observed in Kerala and other South Indians states this year, attributed to deficient monsoon in the two previous seasons. Cultivators in Kerala (in CEZ Malabar) were said to be at the receiving end as they depend on the rains for the yield of tender coconuts. 82 Palakkad, which contributes around 12-14 per cent of Kerala's coconut production, witnessed a drastic drop in production due to the drought. 83 In July 2016, an invasive crop pest – the white fly - was known to have infested coconut groves across Kerala rapidly affecting the production of tender coconuts significantly. The pest was observed to have spread swiftly when the weather was warm and humid. 84 Given the above two factors, the Coconut produce quantity needed for this project for this specific year was available in comparatively less supply than other years. Therefore less number of farmers or aggregators were able to source the quantum of 19 tons of coconut, During the market research process, the market requirements had already been deduced. In order to fulfil these requirements, it was a prerequisite of the selected market players that farmers possessed aggregator skills such as sorting, grading and knowledge of packaging as per market player specifications. A second prerequisite of the market players was the ability to communicate in the same language. This was considered important with regards to building trust among both supply chain actors. AAR possessed both these skills 85, and was the only farmer found interested in supplying the minimum quantum of tender coconut and setting up a short sea shipping supply chains. Therefore, AAR was chosen as the farmer for this study. AAR also met the other the criteria on farmer selection. AAR was: - capable of selling sufficient (consolidated) volume for short sea shipping, i.e. quantity corresponding to minimal one 20-foot container per customer order (approx. 19 MT); - fulfilling the condition of taking a market driven approach to supply chain focusing on the market player instead of selling to the nearest (wholesale) market; 82 http://www.newindianexpress.com/cities/kochi/2017/may/09/coconut-production-on-a-downward-spiral-1603008.html 83 http://www.thehindubusinessline.com/economy/agri-business/drought-shrinks-coconut-output-in-south-india/article9578040.ece 84 http://www.thehindu.com/news/cities/bangalore/Whiteflies-plaguing-coconut-plantations-in-south-India/article16794064.ece 85 Appendix 25 116 - ensures that the quality specifications of the market player are met sufficiently and consistently apart from providing competitive prices. 4.2.2.2. Determination of farmer status The current status of the farmer was determined in the context of market player criteria. Since the study takes a market driven approach, the farmer was expected to meet market player criteria for product variety and label, quality and logistical specifications. The scaling of scores for the farmer corresponding to market player requirement criteria has been done separately. Below is the resultant table based on the scoring range for market requirement criteria for AAR. Table 72 Uniform scoring range for farm aggregator assigned to each possible answer corresponding to each criterion. Scoring range Criteria 0 1 2 3 4 AAR Product variety Brown Tender and label - coconut coconut 4 Green natural in 10% to 0% to 300-400 ml Product No empty colour & 20% scar 10%scar on water requirements /dry nuts Tender on skin skin content 4 Delivery acceptance logistics Rejected - - Accepted 4 Delivery twice a 3 days in a frequency On call Weekly week week Daily 3 Own transport (Bike, Auto, Full truck Vans, load (Open / Way of transport - bullock cart) small truck Tempo Close) 2 Loose white vinyl Packaging cargo - bag jute bag 0 Like to add more than products none - one - one 4 Individual score of AAR corresponding to market players, FARMFRESH, GO2FRESH and SAMEER is given in Table 73. 117 Table 73 Individual scores of AAR corresponding to individual market player requirements Online Wholesaler Scoring range Online Retailer Retailer /trader Criteria 0 1 2 3 4 FARMFRESH GO2FRESH Sameer Product variety and Brown Tender label - - coconut - coconut 4 4 4 No Green 10% to empty natural in 20% 0% to 300-400 ml Product /dry colour & scar on 10%scar water requirements nuts Tender skin on skin content 4 4 4 Delivery acceptance logistics Rejected - - Accepted 4 4 4 Delivery twice a 3 days in frequency On call Weekly week a week Daily 3 1 1 Own transport (Bike, Full truck Way of Auto, Part load (Open transport - Vans) truck Tempo / Close) 4 4 3 white Loose vinyl Packaging cargo - bag jute bag 0 0 4 Like to add more than products none - one - one 4 4 2 Similar to market requirements, these farmer characteristics were also considered as input for the match analysis in section 4.2.3. 4.2.3. Match analysis In both the market identification (4.2.1.1) and the farmer identification (4.2.2.1), interviews and considerations have led to the deselection of some farmers and markets. Three market players and one farmer are still considered as potential supply chain participants. Therefore, match analysis of three farmer-market combination (FMCs) were performed. Table 74 Three farmer-market combinations (FMCs) included in match analysis Farmer / Market FARMFRESH GO2FRESH SAMEER AAR FMC1 FMC2 FMC3 118 4.2.3.1. Identification of the ‘best match’ The ‘best match’ (most promising FMC) is identified by: 1) Listing for each FMC, the quantitative difference for each market requirement (market score – farmer score = GAP score); 2) Weighing each market criterion and multiply with the GAP scores to gain the weighted GAP scores; 3) Adding the sum of both the weighted GAP scores and the maximum weighted GAP scores; 4) Calculating the total GAP score (sum weighted GAP scores / sum max weighted GAP scores); 5) Present the FMC MATCH score (1- GAP). The three individual FMCs are tabulated below for the farmer AAR for tender coconuts with respect to each individual market player. It should be noted here that the individual farmers from whom the farmer sources part of the tender coconuts can only supply quantities for which small trucks prove to be cost efficient. The Way of Transport criterion therefore shows a gap in all three FMC scenarios. Table 75 FMC 1: Individual attractiveness of AAR corresponding to requirement criteria of online retailer FARMFRESH FMC 1: AAR - FARMFRESH GAP (Market Player - Max. Weighted GAP Weighted GAP Market player: Farmfresh Farmer) Farmer: Weight AAR Criteria Product variety and label 4 4 0 3 0 12 Product requirements 4 4 0 3 0 12 Delivery acceptance logistics 4 4 0 1 0 4 Delivery frequency 3 3 0 2 0 8 Way of transport 4 2 2 2 4 8 Packaging 0 0 0 1 0 4 Sourcing region 0 0 0 1 0 4 Like to add products 4 4 0 1 0 4 TOTAL 2 14 4 56 GAP (%) (total weighted GAP / Max Weighted GAP) 7 MATCH (%) (1-GAP) 93 119 The Match of FMC1 is 93%. As stated at the beginning of this sub-section, the 7% gap is on account of Way of Transport. All three market players, including FARMFRESH stated the requirement for a full truck load (open or closed) under the Way of Transport parameter. Table 76 FMC 2: Individual attractiveness of AAR corresponding to requirement criteria of online retailer, GO2FRESH FMC 2: AAR – GO2FRESH Max. Weighted Weighted GAP Market player: Go2fresh Farmer: Weight AAR GAP GAP Criteria Product variety and label 4 4 0 3 0 12 Product requirements 4 4 0 3 0 12 Delivery acceptance logistics 4 4 0 1 0 4 Delivery frequency 1 3 2 2 4 8 Way of transport 4 2 2 2 4 8 Packaging 0 0 0 1 0 4 Sourcing region 0 0 0 1 0 4 Like to add products 4 4 0 1 0 4 TOTAL 4 14 8 56 GAP (%) (total weighted GAP / Max Weighted GAP) 14 MATCH (%) (1-GAP) 86 The Match of FMC2 is 86%. Apart from the gap on the Way of Transport parameter, GO2FRESH also indicated that they would require the delivery of tender coconuts on call, while the farmer can only deliver on three fixed days of the week. Therefore, a gap of 2 (based on the scoring range) exists here. 120 Table 77 FMC 3: Individual attractiveness of AAR corresponding to requirement criteria of wholesaler, SAMEER FMC 3: AAR – SAMEER Max. Weighted GAP Weighted GAP Market player: Farmer: Sameer Weight AAR GAP Criteria Product variety and label 4 4 0 3 0 12 Product requirements 4 4 0 3 0 12 Delivery acceptance logistics 4 4 0 1 0 4 Delivery frequency 1 3 2 2 4 8 Way of transport 3 2 1 2 2 8 Packaging 4 0 4 1 4 4 Sourcing region 0 0 0 1 0 4 Like to add products 2 4 2 1 2 4 TOTAL 9 14 12 56 GAP (%) (total weighted GAP / Max Weighted GAP) 21 MATCH (%) (1-GAP) 79 The Match of FMC1 is 86%. Similar to GO2FRESH, SAMEER also indicated the requirement for the parameter of Delivery Frequency of tender coconuts to be on call, while the farmer can only deliver on three fixed days of the week. So, a gap of 2 exists here as well. Additionally, the gap of 2 is observed for the Way of Transport parameter here as well. Thirdly, for the parameter Like to Add Products, SAMEER indicated one variety of produce, while the aggregator has more than one variety of produce to offer. So, a gap of 2 is observed for this parameter as well. It can be concluded from the outcomes that for Packaging, Like to Add Products, as well as Way of Transport, there does exist a gap between the requirements defined by the market player and to what extent the farmer can fulfil these market requirements currently. 121 The table below summarises FMC attractiveness for the farmer AAR with respect to each market player. Table 78 Summary of attractiveness of AAR corresponding to all three shortlisted market players Tender Coconut Market players → Online retailer Online retailer Wholesaler FARMFRESH GO2FRESH SAMEER Market Attractiveness (%) 67 60 60 Farm Aggregator ↓ Match between farmer and market player (%) AAR 93 86 77 Observed from the above three sets of resultant tables, it is evident that FMC 1, i.e. farmer AAR’s supply of tender coconuts to market player FARMFRESH, is the most attractive farmer-market combination as it has the highest match score. Moreover, the market player of FMC1 (FARMFRESH) also has the highest Market Attractiveness score. 4.2.3.2. Soft elements The farmer, market player or project partner that executes this methodology might have qualitative preferences. This could lead to an adjustment of the ‘best match’: FMC1. The soft element observed to be a limiting aspect during this study is communication between the farmer and the market players. Market players shortlisted for the purpose of this study do not buy from farmers directly in current supply chains on account of the language barrier and trust deficit due to cultural differences and physical distance. 122 The primary reason is on account of the drastically distant geographies where the source and destination CEZs are situated Figure 13: Route map of PRC: CEZ Malabar to CEZ Suryapur by short sea shipping route As a multilingual country, India actively speaks 122 languages. 86 Trust between stakeholders located in physically distant geographies as in the case of short sea shipping supply chain is a significant concern as currently the farmers are more accustomed to sell to known local buyers with a very small number of exceptions that engage in export supply. Nevertheless, with reference to the section 4.2.2.1 on farmer identification, it was expected that a lack of trust would be a risk that can be mitigated as the communication barrier is bridged by the farmer (AAR) who also plays the role of aggregator between the market player and other coconut farmers. 86 http://www.censusindia.gov.in/Census_Data_2001/Census_Data_Online/Language/gen_note.html 123 Therefore, no soft elements influence the conclusion of the match analysis result: FMC 1, i.e. farmer AAR’s supply of tender coconuts to market player FARMFRESH, is the most attractive product-market combination. FMC1 will therefore be considered as a viable input for the next phase: Supply chain design. 4.3. Supply chain design As described in the methodology, dependent on the available opportunities one or multiple supply chain designs (scenarios) can be considered. Each supply chain scenario will be described according to four levels: physical (4.3.1), logistical control (4.3.2), information (4.3.3) and organisational (4.3.4). The different scenario(s) will be evaluated afterwards (4.3.5). The observations made in this section will aid the conclusion and design of the supply chain to be put into test implementation and will be the input for Phase D: Test implementation. As determined in 4.2.3, the FMC of the lead farmer from CEZ Malabar supplying tender coconuts to the online retailer from CEZ Suryapur is considered for the purpose of this supply chain design. As will be observed, results of section 4.3.5 illustrate that the most important consideration refer to storage conditions during the entire supply chain: Ambient, Partial cooling, Maximum cooling. Therefore, the selected supply chain design scenarios for the FMC under consideration differ on Physical design. For the Logistical control, Information and Organization (described in 3.3, no scenarios are set in case of ‘Logistical control’ and ‘Information’ because the short sea shipping supply chain information and control design for all three storage conditions will be identical. With regard to ‘Organization’, no scenarios are set because only one option is considered as realistic. This can be illustrated by the table below: Table 79 Link between scenarios and parameters of the logistical concept Physical design Logistical control Information Organization Scenario 1: ambient Scenario 2: partly cooled Identical for all three scenarios Scenario 3: cooled ambient 124 4.3.1. Physical design Currently, most fresh produce is not transported via cold chain in India. Due to the lack of cold chain infrastructure in the country at present and to ensure that this supply chain design remains applicable for the stakeholders, it was decided to establish three scenarios using the storage and carriage temperature as the control/determining variable to draw comparisons of quality of the fresh produce. These scenarios had to mimic: 1. ambient conditions where no cold chain infrastructure was available; 2. a partial cold chain condition where cold chain warehousing infrastructure was not available at the farmer and/or buyer end but carriage through a reefer container was possible; and 3. maximum cooling conditions in which cold chain infrastructure was available throughout the supply chain from end to end. Scenario 1: Ambient Prevalent supply chains entail a lead time of approximately 18 days including consolidating the produce from different farmers, sorting and grading, packaging, loading and transport by road and sea and unloading the produce to a central collection location, processing and documentation, transport, and distribution from the central delivery location to the retail chains and then to the final consumer. All of this is done under ambient temperatures (approximately 24.5 °C). The below Figure 15 illustrates the process flow imitated in the simulation of the ambient scenario 1. As noted in section 4.2.2.2, the lead farmer sources the produce from the individual farmers in small trucks. The produce is transported from the lead farmer’s farm in a 20-ft container at ambient temperature. 125 HARVESTING AT VARIOUS FARMS UNLOADING AT MARKET PLAYER STORING AT MARKET PLAYER IN & TRANSPORT OF HARVEST TO WAREHOUSE WAREHOUSE LEAD FARMER LOCATION & • HALF - 1 DAY • HALF - 1 DAY CONTAINER TRANSPORTED TO LEAD FARMER • 1 DAY SORTING OF TENDER COCONUT TRANSPORT FROM PORT TO TRANSPORTED FROM MARKET AT FARM & LOADING IN THE MARKET PLAYER PLAYER TO RETAILER CONTAINER • 1DAY • 2-3 DAYS • HALF - 1 DAY CONTAINER DISPATCH TO PORT PROCESSES AND STORING AT RETAILER • 1 DAY DOCUMENTATION AT ARRIVAL • 1 DAY (PDA) • 2 DAYS PROCESSES AND TRANSPORT AT SEA PICKUP BY FINAL CONSUMER DOCUMENTATION AT • 3 DAYS • 1 DAY DEPARTURE (PDD) • 2 DAYS Figure 16 Process flow of supply chain for ambient scenario Scenario 2: Partial Cooling During the interview with FARMFRESH (the shortlisted market player in the selected FMC1), the limitation of the absence of cold storage at the market player’s warehousing facility was discussed. This would have led to ambient storage of coconuts for 4 to 5 days after delivery to the market player warehouse. Additionally, given the Indian market conditions, it is not expected that even online retailers such as FARMFRESH would invest in cold storage for fresh produce in the immediate future. It is expected that ambient storage towards the latter part of the supply chain would still result in sufficient quality at the consumer end (18 days post-harvest). This scenario therefore contains a cold chain up to the movement of unloading at the market player warehouse. The below Figure 16 illustrates the process flow imitated in the simulation of the partial cooling scenario 2. Unlike in the case of the ambient scenario, in the partial cooling temperature scenario, a reefer container was considered for the transport of the tender coconuts from the farm to the FARMFRESH warehouse. However, the absence of cold storage facilities at the market player warehousing would occur under ambient temperature. 126 PRE-COOLING OF EMPTY REEFER TRANSPORT FROM PORT TO UNLOADING AT MARKET PLAYER CONTAINER MARKET PLAYER IN AMBIENT IN WAREHOUSE • 1 DAY • 1 DAY • HALF - 1 DAY HARVESTING AT VARIOUS FARM PROCESSES AND STORING AT MARKET PLAYER IN & TRANSPORT OF HARVEST TO DOCUMENTATION AT ARRIVAL WAREHOUSE LEAD FARMER LOCATION & (PDA) • HALF - 1 DAY CONTAINER TRANSFERD TO LEAD • 2 DAYS FARMER • 1 DAY SORTING OF TENDER COCONUT TRANSPORT AT SEA TRANSPORT FROM MARKET AT FARM & LOADING IN THE • 3 DAYS PLAYER TO RETAILER IN AMBIENT CONTAINER • 2-3 DAYS • HALF - 1DAY CONTAINER DISPATCH TO PORT PROCESSES AND STORING AT RETAILER PICK UP BY FINAL CONSUMER • 1 DAY DOCUMENTATION AT DEPARTURE • 1 DAY • 1 DAY (PDD) • 2 DAYS Figure 17 Process flow of supply chain for partial cooling scenario Scenario 3: Maximum Cooling Under ideal conditions, it is assumed that cold storage of the tender coconuts would take place only after the first two days post-harvest. The end to end maximum cooling period is therefore considered from this point in time to the time that the produce reaches the end consumer. This scenario would also result in superior consumable quality of the produce and farmers earning significantly higher revenue from high end market players located geographically in distant locations, who would be willing to pay a premium for this superior quality. Additionally, it is assumed in this scenario that from the lead farmer to the port of origin, and onwards to port of destination through short sea mode and till the final delivery to the market player’s premises, the produce will move in a reefer container and the market player will also have cold chain storage facility at his end. The below Figure 17 illustrates the process flow imitated in the simulation of the maximum cooling scenario 3. In the case of maximum cooling, it was assumed that the market player would invest in a cold storage warehouse after receiving the shipment of the tender coconuts. 127 PRE-COOLING OF EMPTY REEFER TRANSPORT FROM PORT TO UNLOADING AT MARKET PLAYER CONTAINER MARKET PLAYER IN A REEFER IN WAREHOUSE • 1 DAY CONTAINER • HALF - 1 DAY • 1 DAY HARVESTING AT VARIOUS FARM PROCESSES AND STORING AT MARKET PLAYER IN & TRANSPORT OF HARVEST TO DOCUMENTATION AT ARRIVAL WAREHOUSE LEAD FARMER LOCATION & (PDA) • HALF - 1 DAY CONTAINER TRANSFERD TO LEAD • 2 DAYS FARMER • 1 DAY SORTING OF TENDER COCONUT TRANSPORT AT SEA TRANSPORT FROM MARKET AT FARM & LOADING IN THE • 3 DAYS PLAYER TO RETAILER IN REEFER CONTAINER VEHICLE • HALF - 1 DAY • 2-3 DAYS CONTAINER DISPATCH TO PORT PROCESSES AND STORING AT RETAILER IN COLD PICK UP BYFINAL CONSUMER • 1 DAY DOCUMENTATION AT DEPARTURE CHAMBER • 1 DAY (PDD) • 1 DAY • 2 DAYS Figure 18 Process flow of supply chain for maximum cooling scenario 4.3.2. Logistical control Given Indian market conditions of agricultural land holding, consumer behaviour and consumption patterns, the trend of small farms as well as small retail shops is assumed to continue even if farmer organisations and aggregator communities are established. The purchase of fresh produce such as tender coconuts by several small vendors as well as small markets is therefore expected to occur concurrently to large scale purchases. Additionally, doorstep deliveries through e-commerce retail (such as GO2FRESH) have also witnessed significant growth in recent times and are growing as a trend. It can therefore be assumed that small scale buyers as well as small farms will continue to exist in the Indian fresh produce supply chain. 128 Customer Order Decoupling Point (CODP) and Cross Docking In current supply chains in which the market player is involved, as well as in this short sea shipping supply chain, the CODP will rest at the market player warehouse. However, with better information sharing in the future (elucidated in section 3.3.3), the final customer manifest indent can be given to the farmer so that orders may be pre-packed for end users such as retailers or hotel chains (e.g. private label packaging) at the farmer location before shipment. This will enable entities such as FARMFRESH to share individual smaller buyer packaging requirements with the farmer, and reduce the time taken to customise sorting and packaging of the fresh produce inventory at their warehouse for individual buyers. Additionally, it enables cross-docking of the tender coconut shipments as no further value-added processes need to be performed at the warehouse. In the future, this change of the CODP from the market player warehouse to the lead farmer aggregation location will result in a reduction in lead time and fewer stocks at the market player end. It will therefore result in lower supply chain cost and better tender coconut quality at the consumer end. The availability of better quality at the consumer end might lead to the opportunity to serve markets that demand supreme quality and are willing to pay a higher price, enhancing the business prospects of the short sea shipping supply chain. Continuous replenishment The question of continuous replenishment exists where frequent delivery of smaller fresh produce consignment sizes is possible. In this case, since a large quantum of produce is being moved, continuous replenishment from the lead farmer is ruled out. The current processes after delivery at the warehouse of FARMFRESH are based on continuous replenishment only. However, in a complete cold chain scenario the continuous replenishment from the marker player warehouse to the retails is a distinct possibility. 4.3.3. Information Below is the information shared along the supply chain for short sea shipping: 1. Order indent prepared by the market player based on orders by retailers. 2. Order quantity, quality specification & delivery time lines are communicated to the lead farmer. 3. Lead farmer communicates order quantity, quality specification & delivery time lines to the other farmers. 129 4. Post confirmation of orders from other farmers, the lead farmer confirms the order quantity, quality specification & the delivery time lines to the market player. 5. Market player issues the final order with confirmed quantity, order details and delivery time lines to the lead farmer. 6. Market player contacts the logistics company with information about the shipment quantity and shipment ready date. 7. The Logistics Company confirms container availability, vessel schedule and trailer truck availability to the market player. 8. The market player communicates indicative pickup date to the lead farmer. 9. The farmer confirms the pickup date and communicates the same to the other farmers. 10. The Logistics company informs the container number & trailer license plate number to the market player. 11. The Market player shares the container number & trailer license plate number with the lead farmer. 12. The lead farmer prepares the invoice and related documents and sends them to the market player & Logistics Company. 13. The container is dispatched on the trailer to the farm on the agreed date. 14. The container reaches the farm and is loaded & sealed, and the lead farmer prepares the packing list. 15. The container is dispatched from the farm and the Seal number and the packing list are shared with the market player and logistic company. 16. The container arrives at container freight station and bill of lading is prepared based on the invoice documents. 17. Container is loaded on the vessel & bill of lading, Invoice and packing list are sent to the market player. 18. The container is tracked by all stake holders using the bill of lading number or the container number. 19. Container is offloaded at the destination port and moved to the container freight station for documentation process. The market player is informed. 20. The container is loaded onto the trailer. The trailer license plate number and the driver contact number are shared with market player. 21. The trailer reaches the market player warehouse and shipment is offloaded. Quantity and quality check is done, and order receipt is confirmed to the lead farmers & the logistics company. 22. The lead farmer is paid the remaining amount or total amount based on payment terms. 23. Market player segregates and repacks the coconuts for final shipments to the retailers and prepares the delivery order & invoice and shares the same with the retailers. 130 24. The retailers receive the shipment, issue order receipt to the market player and make the final payment to the market player. 25. The end consumer picks the product from the retailer. Retailer prepares the invoice and checkout upon payment. Almost all information listed above is shared via email, fax or in printed document form. All other information such as schedules and phone numbers are shared on the telephone and emails. The tracking of bill of lading is done using the online track and trace systems available of shipping lines on their internet web pages or by calling up their customer service team. Key information, including tracking and tracing information of the short sea shipping is currently available at the data points given in the table below. This information sharing is expected to improve in the future due to technology advancement and increased data connectivity. Table 80 Key information in the supply chain currently accessible Data Point Generated By Shared With Order confirmation Market Player Lead Farmer (trigger point of supply chain) Container & trailer availability Logistics Company Market Player, and the vessel schedule Lead Farmer Invoice and packing list Lead Farmer Market Player, Logistics company Seal no, container no, trailer Logistics company Market Player, licence plate number, bill of Lead Farmer lading number Trailer no at the destination Logistics company Market Player port Driver contact number Logistics company Market Player The data points mentioned in the above Table 80 already exist to facilitate other (non-short sea shipping) supply chains. The information management utilized for short sea shipping will apply in non-short sea shipping supply chains as well. No additional information is mandated to be shared and no additional data points need to be created in order to successfully implement the short sea shipping supply chain. 131 The dynamic and live availability of data helps to monitor quality of fresh produce under specific temperature and humidity conditions and to control them. With increased use of mobile data in rural India, it is expected that more mobile platforms will be developed to allow information sharing of all major data points mentioned in the table above on a real-time basis. 4.3.4. Organization As mentioned in the phase C of the methodology, supply chains involving smallholder farmers usually apply one of four organisation models: 1. Bilateral relations between individual farmer and market player 2. Contract farming 3. Linkage through lead farmer 4. Linkage through cooperatives Linkage through lead farmer has been considered as the most effective organisational model to connect the farmer with the market player for the purpose of this study and selected FMC. Reasons for this organization choice are listed below: It has been documented that despite wide cultivation of tender coconuts in the state of Kerala, very few large plantations exist in the region. In fact, over 95% of coconut trees in Kerala are grown in the peripheries of homes, making the cultivators traditional smallholders as described in the methodology (3.3.4). As mentioned in the results of Phase B (4.2.2.1), the state of Kerala (which lies in the CEZ Malabar region) experienced drought for two consecutive years. In addition, the coconut farms also suffered an insect attack. These two facts reduced the options for finding lead farmers and a single farmer for sourcing the minimum quantity required was identified. This minimum quantity is a large batch of tender coconuts of at least the capacity of one 20ft refer container. Additionally, in average years in which farmers do not face the challenges of drought and insect attacks, many smallholder farmers exit – who are not able to harvest a full container at once. 87 Therefore, consolidation of tender coconuts from multiple farmers is required, a prerequisite for an effective organization model. An Analytical Study On Agriculture In Kerala (pg 11) http://www.keralaagriculture.gov.in/pdf/a_s_06042016.pdf; see 87 Appendix 7 for screenshot 132 In addition, as observed in the sub-section Soft Elements of the Phase B results, a communication barrier due to language is also observed as a limiting aspect between individual farmers from the origin CEZ Malabar and the market player from the destination region, causing lack of trust between the market player and the farmer. However, it was expected, that the lead farmer (AAR) conversant in local language Malayalam, language of business English and national language Hindi, would bridge this issue by playing the role of aggregator between the market player and other coconut farmers. Moreover, smaller individual farmers do not currently possess the requisite skill sets and financial capacity to absorb the uncertainties of the market, which a lead farmer could mitigate with greater ease. The small individual farmer therefore receives payment immediately in cash on delivery to the lead farmer, whereas the lead farmer accepts payment as agreed with the market player. In this organization model, the lead farmer takes a financial risk. The above considerations led to the selection of Linkage through Lead Farmer as the organisational model for the purpose of this study. 4.3.5. Supply chain evaluation Conclusions for the feasibility of the redesigned short sea supply chain are made on the basis of the below performance indicators: 1. Cost of the harvesting & aggregation process, and transport, and the cost feasibility analysis 2. Quality loss of Tender Coconuts during the total time between receiving the order at the farmer and product replenishment at the market player (order lead time). 4.3.5.1. Cost As observed in the case of the three storage temperature scenarios, the first three activities in the supply chain remain identical. Below is the logistics cost of transport for the various modes of transport from the farm in CEZ Malabar to market player in the destination CEZ Suryapur as calculated in the results of Phase A. Since the price difference is calculated between wholesale markets at CEZ Malabar and CEZ Suryapur (1225 INR, as seen in criteria d of the results shown in section 4.1), it can be concluded that cost of transporting the harvest to the local market is included in the prices. The total cost of the current supply chain therefore also includes the post-harvest costs incurred. Basis this amount the additional earning per ton and total earnings for 19 tons of coconut were calculated in Phase A as well. Table 61 from the 4.1.3.1 is reproduced for reference below as Table 81. 133 Table 81 Additional earnings for the former using short sea shipping to transport 19T coconuts from CEZ Malabar to CEZ Suryapur Cost Feasibility Analysis for Short Sea Shipping from farm in CEZ Malabar to Amount buyer in CEZ Suryapur (INR) Price difference between CEZ Malabar and CEZ Suryapur Markets for Coconut Per Quintal 1225 Price difference between CEZ Malabar and CEZ Suryapur Per ton (1 ton = 10 quintal) 12250 Total additional earning for seller for 19 tons of coconut 232750 Cost of Short Sea Shipping for 40 ft. reefer container 186860 Net additional earning for 19 ton of coconuts using short sea shipping 45890 Per ton additional earning 2415 There is a net earning of INR 2415 per ton (INR 2.66 per kg) after deducting all costs. To conclude the supply chain evaluation on Costs: all three scenarios are feasible from a business perspective. 4.3.5.2. Quality loss As discussed in the section on throughput times, apart from port-to-port connectivity 88, the entire supply chain process of farm to consumption is longer. The same has been documented in the storage temperature scenarios process flows in this report in section 44, factoring in various steps of farmer group activities as well as logistical processes. The calculation of lead time for all three scenarios is listed in the table below: Table 82 Lead time calculation for all three temperature scenarios SCENARIO 2: SCENARIO 3: SL. SCENARIO 1: PARTIAL MAXIMUM No. ACTIVITIES AMBIENT COOLING COOLING HARVESTING AT VARIOUS FARMS & TRANSPORT OF HARVEST TO LEAD FARMER LOCATION & CONTAINER TRANSFERD TO LEAD FARMER/PRE- COOLING OF EMPTY REEFER CONTAINER (For Scenario 1 & 2) 1 1 Day 1 Day 1 Day SORTING OF TENDER COCONUT AT 2 FARM & LOADING IN THE CONTAINER Half-1 Day Half-1 Day Half-1 Day 3 CONTAINER DISPATCH TO PORT 1 Day 1 Day 1 Day 88 Appendix 11 134 SCENARIO 2: SCENARIO 3: SL. SCENARIO 1: PARTIAL MAXIMUM No. ACTIVITIES AMBIENT COOLING COOLING PROCESSES AND DOCUMENTATION AT 4 DEPARTURE (PDD) 2 Days 2 Days 2 Days 5 TRANSPORT AT SEA 3 Days 3 Days 3 Days PROCESSES AND DOCUMENTATION AT 6 ARRIVAL (PDA) 2 Days 2 Days 2 Days TRANSPORT FROM PORT TO MARKET 7 PLAYER 1 Day 1 Day 1 Day UNLOADING AT MARKET PLAYER 8 WAREHOUSE Half-1 Day Half-1 Day Half-1 Day STORING AT MARKET PLAYER IN 9 WAREHOUSE Half-1 Day Half-1 Day Half-1 Day TRANSPORTED FROM MARKET PLAYER TO RETAILER (By Reefer vehicle for 10 Scenario 3) 2-3 Days 2-3 Days 2-3 Days STORING AT RETAILER (In Cold Chamber for Scenario 3) 11 1 Day 1 Day 1 Day 12 PICKUP BY FINAL CONSUMER 1 Day 1 Day 1 Day Total Days 17 Days 17 Days 17 Days Total time taken from farm to consumer is 17 days in all three scenarios. However, adverse weather conditions at sea may cause a delay. To factor in this delay, one extra day is added to the days taken for transport at sea. It can therefore be concluded that the lead time for all three scenarios is 18 days. Comparisons are drawn between the three storage temperature scenarios for expected loss of produce. For the purpose of this study, out of the options mentioned in the methodology, the definition of losses relevant are: market acceptance of the fresh produce, and the value of the produce at the end of the supply chain in all three temperature scenarios. It should be noted here that the selected market player, FARMFRESH also demanded an extra quantity of approximately 10% above the order size, anticipating loss of produce in transit, in terms of market acceptability. It is expected that the produce will be of at least consumable quality in all three scenarios. However, this cannot be concluded definitely without testing the supply chain. The value of the fresh produce can only be deduced at the time of the final implementation (not under laboratory conditions), during which it is delivered to the market player. Therefore, for the purpose of this study, product loss needs to be evaluated only in terms of the market acceptance of the tender coconuts. 135 Considering the product quality and the end of the short sea shipping supply chain, there exists uncertainty about the quality of tender coconuts in the absence of end-to-end cold chain. More particularly, once a the refer container carrying tender coconuts reaches the market player premises, the absence of cold chain storage at FARMFRESH location posed a challenge and brought forth question on the impact of product quality in part cold chain. To conclude the supply chain evaluation on quality loss: the effect of the supply chain design on quality loss is unclear, to be able to eliminate uncertainties a test implementation is required. 4.4. Test implementation The primary purpose of this study is to test the potential of short sea shipping as a method for the transportation of fresh produce as commodity. Tender coconuts were chosen as the commodity. In order to test the technical feasibility of a short sea shipping chain for tender coconuts, a test transport with fresh coconuts was planned. The expected time between harvest and cooling of the tender coconuts was to be 5-6 days due to absence of a (pre)cooling facility. Prompt post-harvest cooling is essential to prolong the shelf-life of tender coconuts. It was decided not to risk two entire containers of coconuts (to test ambient as well as cold chain scenarios) for this trial because of temperature management issues, which could result in a total loss of the cargo due to quality decay. Instead, a controlled small-scale transport simulation with three storage temperature scenarios was performed. A cold storage facility capable of warehousing temperatures of 13 °C was used at the origin CEZ Malabar in Palakkad to provide optimal cooling conditions. Results of this section on the chain simulation are documented in the following sections: 1. Scope 2. Define test implementation team 3. Measurement protocol 4.4.1. Scope With reference to section 4.3 on the need for test implementation, it was concluded that a controlled small-scale laboratory simulation mimicking the lead time with the three temperature scenarios was required to be carried out to demonstrate to the members of the consortium which scenarios of the test implementation are feasible. 136 With reference to section 4 on Physical design, these three scenarios are as listed below: a. Ambient (A): 6 crates of tender coconuts were tested under ambient temperature conditions for 18 days (to reflect the number of days taken to implement the entire supply chain). As mentioned in section, one extra day is added to the days taken for transport at sea (17 days). Therefore, the lead time for all three scenarios is considered to be 18 days. b. Partial cooling (P): It was decided to observe two crates of tender coconuts shifted to ambient temperature for 4-5 days towards the end of the experiment under chain simulation. c. Maximum cool (M): The ideal scenario of end-to-end cold chain was also simulated to demonstrate the maintenance of superior quality aspects of the tender coconuts. 4.4.2. Define test implementation team As stated in the Phase D methodology of this study, it was not mandatory for members of the consortium to be part of the chain simulation, however, all members of the consortium had to be in agreement to its participants. As the test implementation of the suggested supply chain method via short sea shipping is a laboratory simulation, the involvement of the logistics service provider was not relevant. The Palakkad Ice & Cold Storage at Palakkad was treated as a make-shift laboratory for the purpose of this experiment. Participating members of the test implementation team are listed below: 1. Quality Supervisor – This role required the person to set the parameters of quality of the tender coconuts from the start of the experiment. It also required for the individual to supervise the measurements and rating of the tender coconuts on the pre-determined days of the experiment as well as direct the researcher on nuances of quality that underwent change. 2. Research Analyst – The research analyst was part of the local partner team who noted all results, documented and offered primary observations gathered from the log meters and testers. 3. Farmer – Farm aggregator AAR provided sorting and grading services apart from sourcing the coconuts from the two farms 4. Testers – A sample size of 4 to 8 local dwellers, as per availability, was requested to test the tender coconuts on the planned measurement days for quality testing of the tender coconuts. On Day 5 (December 5, 2017): 6 persons tested the quality of the tender coconuts, on Day 12 (December 26, 2017): 4 persons tested the quality of the tender coconuts, and on Day 18 (January 1, 2018): 8 persons tested the quality of the tender coconuts. Tester demographics included workers & managers from factories close to the cold storage facility in Palakkad. 137 4.4.3. Measurement protocol As mentioned earlier in this section on test implementation (4.4.2), the experiment was conducted to show the potential of transporting fresh coconuts in a short sea shipping chain and the importance of prompt cold chain as a tool to reduce post-harvest food losses. The execution of the test implementation involved 144 pieces of tender coconuts divided into 18 crates containing 8 pieces of tender coconuts each. This test simulation was conducted for a period of 18 days. 9 crates each containing 8 pieces of tender coconut were sourced from Farm A and Farm B respectively. The coconuts were sourced from two farms to eliminate the risk of external factors that might affect the test results of the measurements of the tender coconuts. The quality of the produce was measured on the basis of: a. Taste of the water content b. Internal and external surface appearance c. Colour of the water d. Odour of the water The temperature measurements were constantly noted and maintained with the help of log meters. The schedule of quality measurements of the tender coconuts was planned to reflect the various stages of the supply chain. The same is shown in the table below: Table 83 Schedule of the test implementation corresponding to the actual supply chain involving short sea shipping transport method Storage Storage temperature Storage temperature Scenario 2: temperature scenario 3: Full cold scenario 1: Partly cold chain 13°C Ambient chain (FCC) Supply chain steps in Actions during demonstration Day reality pilot (which we do not do (which we do during the test during the test pilot) pilot) Ambient Part cool Max cool Day- - Post harvesting, sorting, 15 1 and loading at farm Ambient Ambient Ambient Ambient/ Ambient/ 2 Ambient 13°C 13°C 3 - on land / to port Ambient 13°C 13°C 4 - PDD Ambient 13°C 13°C Dec- 19 5 pictures and quality measurements Ambient 13°C 13°C 6 - Transport at Sea Ambient 13°C 13°C 7 Ambient 13°C 13°C 8 Ambient 13°C 13°C 9 - PDA Ambient 13°C 13°C 10 Ambient 13°C 13°C 11 - On land / to buyer Ambient 13°C 13°C 138 Storage Storage temperature Storage temperature Scenario 2: temperature scenario 3: Full cold scenario 1: Partly cold chain 13°C Ambient chain (FCC) Supply chain steps in Actions during demonstration Day reality pilot Dec- 26 12 pictures and quality measurements Ambient 13°C 13°C 13 - Storage at buyer Ambient Ambient 13°C 14 Ambient Ambient 13°C - shipment to 15 retailer/hotel/market Ambient Ambient 13°C - Shelf life during retailer and storage by final 16 consumer Ambient Ambient 13°C 17 Ambient Ambient 13°C Jan-01 18 pictures and quality measurements Ambient Ambient 13°C In order to realize this measurement plan, a cold storage facility that enabled 13 °C storage at high relative humidity (optimal storage conditions for fresh coconuts) was chosen to conduct the experiment. Coconuts from two farms, coded T and R, were used in order to see if results were consistent for all scenarios. From each plantation, 18 crates were filled with 8 coconuts each in a randomized way. The key measurement in the experiment was on the last day, which is at the end of 18 days. However, to observe the stages of deterioration of the tender coconuts, measurement milestones were set on days 1, 5, 12 and 18. Pictures were taken of all crates and quality was determined by picture, observation of internal and external surface defects, and taste. 89 Apart from these days, a daily log was also maintained in order to observe the gradual changes in the appearance of the tender coconuts. A range was scored in order to rate the quality of the tender coconuts measured on each of the test days, where 1 stood for VERY GOOD, 2 for GOOD, 3 for ACCEPTABLE, 4 for SUB- STANDARD, and 5 stood for BAD. Specific Crate codes with their measurement days are mentioned in the table below. It should be noted here that two random samples from one crate each from Group T and Group R were measured on Day 1 post-harvest. The same convention of testing was maintained for Day 5. These two crates contained ten coconuts each. Since they were disposed of immediately, these two crates were not coded. 89 Appendix 6 139 Table 84 Codes of the crates to be measured as per the test implementation schedule Temperature Scenarios Measurement Days PART AMBIENT MAX COOL COOL T1 A1 T4 M1 T7 P1 R1 A1 R4 M1 R7 P1 Day 5 Measurement T2 A2 T5 M2 T8 P2 R2 A2 R5 M2 R8 P2 Day 12 Measurement T3 A3 T6 M3 T9 P3 R3 A3 R6 M3 R9 P3 Day 18 Measurement Pictures for both plantations (T and R) are shown in the following section, separately. In order to avoid confusions, each crate, from which a random sample of tender coconut was measured, was disposed of. On day five, tender coconuts from crates T1A1, T4M1, T7P1 from group T and R1A1, R4M1, R7P1 from group R were measured. On day 12, tender coconuts from crates T2A2, T5M2, T8 P2 from group T and R2A2, R5M2 and R8 P2 from group R were tested. After this test, two crates, T9P3, and R9P3 were shifted to the ambient environment. On day 18, tender coconuts from crates T3A3, T6M3, T9 P3 from group T and R3A3, R6M3, R9P3 from group R were subsequently measured. 140 Table 85 Day-wise photographic results of the test implementation for crates from group T Coconuts Origin T Day 1 Ambient Part Cool Max Cool Day 5 Day 12 Day 18 141 Table 86 Day-wise photographic results of the test implementation for crates from group R Coconuts Origin R Day 1 Ambient Part Cool Max Cool Day 5 Day 12 Day 18 It was observed that in Maximum Cool scenario (>13 °C, ~ 85 Humidity), condition of the tender coconuts in various quality categories such as taste, external appearance, internal appearance & milk colour is VERY GOOD for 18 to 19 days. Under Partial Cooling temperature scenario, (12 days > (>13 °C, ~ 85 Humidity, 7 days in ambient conditions), the condition of tender coconuts in various quality categories such as taste, external appearance, internal appearance & milk colour was considered to be GOOD or ACCEPTABLE for a period of 18 Days. 142 In Ambient temperature scenario (~24.5 °C), the tender coconuts were observed to have a shelf life of up to 5 to 6 days at the most. After that, the tender coconuts were observed to be SUB- STANDARD or BAD. This included rotting and browning of the outer skin of the otherwise natural green colour of the tender coconut. The tender coconut also began to smell rotten and the colour of the water turned an opaque milky white, which is an indication of the water having turned rancid. Through the results 90 of the laboratory simulation with the three temperature scenarios it was observed that of the three scenarios, partial cooling and maximum cooling are both feasible for the purpose of this study. The ambient scenario showed that the fresh produce will not remain in consumable quality till the end of the duration of the supply chain, therefore is not feasible. It was also concluded that the maximum cooling scenario offers a distinct advantage in terms of product quality, likely to fetch a premium for the farmer owing to the superior quality of the produce. 90 Appendix 6 143 5. Conclusion and discussion Auke Schripsema, Han Soethoudt, Seth Tromp, Bhairavi Jani, Priyanca Vaishnav, Anjali Anit, Sibasish Pradhan The main goal of this project is to exploit efficient new urban short sea food supply chains within emerging economies that significantly reduce urban food losses and improve business profitability. Therefore, this study covers the development and piloting of a methodology for new short sea urban supply chains in India and its local knowledge transfer by capacity building and setting up partnerships that can implement the newly designed urban food supply chain. The methodology was built upon existing knowledge and methodologies from literature. However, none of the methodologies found in literature include a methodology that facilitates a (potential) business (consortium) in developing a concrete short sea shipping supply chain and comparing this supply chain with existing supply chains with regard to both business effectivity and the reduction of food losses. Therefore, in developing and applying the methodology, the project team involved in this study has been pioneering with the concept of short sea shipping. The documented methodology in this report consists of four steps: 1. The selection of product-route combinations resulting in a table including all, from a cost point of view, potentially feasable short sea shipping routes between coastal economic zones(CEZs), and a selection of fruits and vegetable products which are promising and suitable in terms of quality decay to be transported on these routes. 2. With respect to these product-route combinations, the selection of farmer-market combinations is established resulting in a table with the potentially viable combinations of farmers and market players (both by name or pseudonym). This table quantitatively scores both the market player attractiveness and the match between market player requirements and farmer status showing the most feasible farmer-market combination. At this stage the product label and variety are specified. 3. With respect to these farmer-market combinations, an evaluated supply chain design, (proof-of-concepts) with physical design, logistical control, information and organisation designs determined. 4. With respect to these supply chain designs, a pilot setting is created resulting in a proof- of-principle that reduces the risk and uncertainty of the envisioned implementation of a short sea shipping supply chain. The results of these for steps are: 1. The only product-route combination, that meets all the criteria of the methodology, is the route from CEZ Malabar to CEZ Suryapur for coconuts. 2. The most attractive farmer-market combination is the supply of Tender coconuts from farmer AAR to market player FARMFRESH. 144 3. Three supply chain design scenarios (ambient, partial cooling, maximum cooling), that differ on physical design only, are feasible from a business perspective. However, the effect of the supply chain design on quality loss could not be determined based on theory alone. 4. A test implementation (pilot setting) was performed to be able to eliminate uncertainties with regard to the effect on quality loss. Through the results of the laboratory simulation it was observed that of the three scenarios, partial cooling and maximum cooling are both feasible for the purpose of this study. The ambient scenario showed that the Tender coconuts will not remain in consumable quality till the end of the duration of the supply chain, and therefore is not feasible. Additionally, successive to the results, the project teams discussed the process of designing and following the methodology itself and its usability in future applications: One crucial aspect of this methodology turned out to be the availability of (quantitative) data. Even though India has a lot of data resources available, not all data could easily be found. In some cases, only interviews instead of literature or databases could provide the required data. In case of emerging economies, data availability might be a significant challenge. Additional interviews are probably limited due to budget constraints. It is a time-consuming process to perform interviews as well as finding the right expert beforehand. Secondly, the methodology describes multiple considerations and criteria that facilitate the applicant in eliminating both unfruitful options with respect to business effectivity and food loss reduction. In doing so, it focusses on the best opportunity available. Given time and budget constraints that are present in any project, this can be a powerful tool: in a relatively short time period the energy of the team(s) involved is focussed on the best opportunity available. On the other hand, options that still can be considered as large opportunities might be ignored. An additional remark that comes to mind with regard to the offered methodology is the broadness of it. It starts from an emerging economy, in this case the country of India (the seventh largest country in the world) and narrows down to the implementation of a short sea shipping supply chain. It involves a broad variety of expertise: market specialists, economists, experts on the reduction of food losses, fresh-product experts and different supply chain actors (each having their own crucial role during an implementation). 145 The question is whether this methodology adds value to project teams with a relatively smaller scope as well. Probably, two different type of projects with a smaller scope could benefit from the presented methodology as well: - Operational projects with a specific market player and farmer (group): these projects might be initiated by the private sector that already has a source (farmer, farmer group, region) and the market player (retailer, hotel chain, etc.) in mind. As an example: how to realize an agro-logistical supply chain (probably with help or short sea shipping) of apples from Himachal farmers to Hyderabad retailers cost effectively and with a minimum amount of food losses? A project like this is expected to benefit from the supply chain design and test implementation part of this methodology. The PRC and FMC section are not required. - Strategic projects to improve a country’s food security, food quality or export opportunities: These kinds of projects are usually executed by government bodies. Especially the PRC analyses could be of value. - Other modalities: Although the methodology developed is dedicated to short sea shipping, it can be applied to other modalities as well. In fact, the methodology can be used to realize a new agro-logistical supply chain in general. 146 References 1. FAO, Global food losses and food waste. 2011. 2. Schripsema, A.S. and E.H. Westra, Reducing Food Losses in India. 2014. 3. Russo, F., G. Musolino, and V. Assumma, Competition between ro–ro and lo–lo services in short sea shipping market: The case of Mediterranean countries. 2015: p. 7. 4. Brooks, Hodgson, and Frost, Short Sea Shipping on the East Coast of North America: An analysis of opportunities and issues. 2006. 5. Ministry of Shipping Government of India, Concept Note on Sagar Mala Project: Working Paper. 2014: p. 32. 6. A.N.Perakis and A.Denisis, A survey of short sea shipping and its prospects in the USA. 2008: p. 25. 7. Yonge, M. and L. 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Solidaridad, Smallholder farmers: how to overcome three paradoxes. 2016. 21. India, M.o.S.G.o., The Sagarmala Post Vol 1. 2017. 147 Appendices 148 Appendix 1: Number of shipping lines servicing the CEZs (criteria d, 4.1.1.4) Interview no.: 001072017 Ref.: page 32, para 2 / line 3 Interviewer: Anjali Anit Interviewee: Mr. Rajan S. (Blue Sea Shipping Agency Pvt. Ltd.) / P. Deshmukh (SCA Logistics Pvt. Ltd.) Mumbai Date: 15th June 2017 / 17th June 2017 Time: 11:30 am Q - Which shipping lines have services to the ports in the table below? A - Indian Flag vessel – SCI Shipping line subject to availability of cargo, and foreign flag-bearing vessels (operated by other shipping lines) having services to the given port Table 87 Shipping lines servicing the CEZs (criterion d) Sr.no. CEZ State Port Does the port have shipping line services? Connectivity Kandla Yes SCI / MSC Line 1 Kutch Gujarat 11 shipping Mundra Yes line/SCI/Shreyas Shipping SCI / Maersk Line Pipavav Yes /Shreyas Shipping 2 Saurashtra Gujarat No Sikka Dahej No 3 Suryapur Gujarat Hazira Yes SCI / Shreyas Shipping Feeder Vessel - North JNPT Yes Seimatech / OEL. 4 Maharashtra Konkan SCI /Shreyas Shipping Mumbai No Dighi No 5 South Maharashtra, Konkan Goa Jaigarh No Mormugao No 6 Dakshin Karnataka Mangalore Yes SCI/Shreyas Shipping Feeder Vessel - BTL / Chakiat /Maersk 7 Malabar Kerala Kochi Yes LineSCI /Shreyas 149 Sr.no. CEZ State Port Does the port have shipping line services? Connectivity Feeder Vessel - BTL 8 Mannar Tamil Nadu / Chakiat /Maersk Line SCI /Shreyas Tuticorin Yes Shipping 9 Poompuhar Tamil Nadu Cuddalore No Feeder Vessel -MOL Chennai Yes Feeder. SCI 10 VCIC South Tamil Nadu Ennore No Feeder Vessel -TS Kattupalli Yes Line SCI/Shreyas Shipping VCIC 11 Andhra Pradesh Central Krishnapatnam No VCIC Visakhapatnam Yes SCI /Shreyas Shipping 12 Andhra Pradesh North Kakinada Yes SCI /Shreyas Shipping 13 Kalinga Odisha Paradip No Dhamra No Feeder Vessel - Mexicon Shipping / Kolkata Yes Parma Shipping. SCI / Shreyas Shipping 14 Gaud West Bengal Feeder Vessel - Mexicon Shipping / Haldia Yes Parma Shipping. SCI / Shreyas Shipping 150 Appendix 2: Minimum Shipping Connection to Other Ports (criteria e, 4.1.2) Interview no.: 001072017 Ref.: page 32, para 2 Interviewer: Anjali Anit Interviewee: Rajan S. (Blue Sea Shipping Agency Pvt. Ltd.) / P. Deshmukh (SCA Logistics Pvt. Ltd.) Mumbai Date: 15th June 2017 / 17th June 2017 Time: 11:30 am Q Do all the ports that fall under Indian CEZs have at least one shipping line connection to other ports? A Yes, all ports have connectivity to at least one other port. Several ports are serviced by Shipping Corporation of India vessels (Indian flag-bearing vessels) subject to availability of cargo, as well as one or more foreign flag-bearing vessels (operated by other shipping lines). 151 Appendix 3: Feeder Vessels Plying per week between Ports (criteria g, 4.1.3) Interview no.: 003072017 Ref: page 40, para 2 Interviewer: Anjali Anit Interviewees: Rajan. S (Blue Sea Shipping Agency Pvt. Ltd.) / P. Deshmukh (SCA Logistics Pvt Ltd), Mumbai Date: 17th June 2017 | Time: 11: 30 am Q – Which ports from the CEZs shortlisted after criteria d & e offer Feeder Vessel services? A– 1. Kandla port is serviceable with three feeder vessels: one is Paramount Shipping and their frequency to port is weekly. The other two are Orient Liner, and Parekh Shipping and their frequency to port is twice a week each. 2. Mundra port is serviceable with four feeder vessels: Paramount Shipping and Seimatech have weekly frequency to the port, while the other two - Orient Liner, and Parekh Shipping have frequency to port is twice a week each. 3. Pipavav port is serviceable with feeder vessel services three times a week 4. Hazira port is serviceable with feeder vessels three to four times a week 5. JNPT port is serviceable with 2 feeder vessels: one is Orient Liner, and the other is Parekh Shipping. Their frequency to the port is twice a week each. 6. Kochi port is serviceable with 3 feeder vessels, one is BTL, second is Chakiat and third is Maersk Line their frequency to port is weekly each 7. Tuticorin port is serviceable with 3 feeder vessels, one is BTL, second is Chakiat and third is Maersk Line their frequency to port is weekly each 8. Chennai port is serviceable with 2 feeder vessels, one is TS Line and other is MOL there frequency to port is twice a week each 9. Kattupalli port is serviceable with 2 feeder vessels, one is TS Line and other is MOL, whose frequency to port is twice a week each 10. Visakhapatnam port is serviceable with feeder vessels twice a week 11. Kolkata port is serviceable with 2 feeder vessels are Mexicon Shipping, and Parma Shipping Each of their frequency to the port is every 15 days. 12. Haldia port is serviceable with 2 feeder vessels are Mexicon Shipping and Parma Shipping Each of their frequency to the port is every 15 days. 152 Table 88 Tabulated results of feeder-vessel connectivity to ports shortlisted in criteria d & e, for criteria g No of Sr.no CEZ Port Feeder Company Frequency Type . Vessels Feeder Kandla 1 Paramount Shipping Weekly Vessel Orient Liner / Parekh Twice a Feeder Kandla 2 Shipping week Vessel 1 Kutch Paramount Shipping Feeder Mundra 2 /Seimatech Weekly Vessel Orient Liner / Parekh Twice a Feeder Mundra 2 Shipping week Vessel Orient Liner / Parekh Twice a Feeder North JNPT 2 Shipping week Vessel 3 Konkan Mumbai NA NA NA NA BTL / Chakiat /Maersk Feeder 6 Malabar Kochi 3 Line Weekly Vessel BTL / Chakiat /Maersk Feeder 7 Mannar Tuticorin 3 Line Weekly Vessel Twice a Feeder Chennai 2 TS Line /MOL week Vessel VCIC 9 South Twice a Feeder Kattupalli 2 TS Line /MOL week Vessel Mexicon Shipping /Parma Every 15 Feeder 13 Gaud Kolkata 2 Shipping days Vessel Mexicon Shipping /Parma Every 15 Feeder Haldia 2 Shipping days Vessel 153 Appendix 4: Availability of Cranes, Trailers, Side Loaders and Carriers or Ro-Ro services (criteria h, 4.1.3) Interview no.: 004072017 Ref: page 40, para 3 Interviewer: Anjali Anit Interviewee: Rajan S. (Blue Sea Shipping Agency Pvt. Ltd.) / P. Deshmukh (SCA Logistics Pvt. Ltd.) Mumbai Date: 17th June 2017 Time: 11: 30 am Q Do the Kandla, Mundra, Pipavav, Hazira, JNPT, Mangalore, Kochi, Tuticorin, Chennai, Kattupalli, Visakhapatnam, Kakinada, Kolkata, Haldia ports (ports in table 5-6) have cranes, trailers, side loaders and carriers? A All the ports mentioned in the question make available the facility of cranes, trailers, side loaders and carriers (in the case of container transport). Q Do the Kandla, Mundra, Pipavav, Hazira, JNPT, Mangalore, Kochi, Tuticorin, Chennai, Kattupalli, Visakhapatnam, Kakinada, Kolkata, Haldia ports (only the ones in table 5) have roro services? A At present, none of the ports offer scheduled Ro-Ro services. 154 Appendix 5: Draft of Kolkata Port (criteria i, 4.1.3) Interview no.: 005072017 Ref.: page 40, para 4 Interviewer: Anjali Anit Interviewee: Rajan S. (Blue Sea Shipping Agency Pvt. Ltd.), Mumbai Date: 17th June 2017 Time: 11: 30 am Q What is the draft at Kolkata port? A Kolkata port is the oldest operating in India, so port draft level is 5.5 meters 155 Appendix 6: Container Freight Stations in a 15-Km radius of Ports (criteria j, 4.1.3) Interview no.: 006072017 Ref.: page 40, para 5 Interviewer: Anjali Anit Interviewee: Mr. P. Deshmukh (SCA Logistics Pvt. Ltd.) Mumbai Date: 17th June 2017 | Time: 11:30 am Q No of CFS in radius of 15 km of given port A All ports that fall under the CEZs listed by the Ministry of Shipping, Govt. of India, have container freight stations (CFS) in a radius of 15 km from the port. Table 89 Tabulation of container freight stations at ports in the list, after Criteria d & e are applied, is as below: Sr.no. CEZ State Port CFS Name Location Gandhidham- Regal Shipping Pvt. Ltd. Kutch Liladhar Pasoo KSEZ Kandla Kandla Desh Gujarat Kandla Port 1 Kutch Gujarat Seabird Marine Pvt. Ltd. Mundra Liladhar Pasoo Mundra Saurashtra CFS Pvt. Ltd. Mundra Ameya Logistics Mundra Mundra Adani Ports/SEZ Mundra Gateway Distripark Ltd. Pipavav Liladhar Pasoo Pipavav Saurasht 2 Gujarat Central Warehousing Corp. Pipavav ra ACTL CFS Pipavav Pipavav APM Terminals Ltd. Pipavav 3 Suryapur Hazira Hazira Parekh Group Hazira Central Warehousing Corp. Kalamboli Apollo Logisolution Pvt. Ltd. Panvel North Maharasht 4 Navkar Corp. Panvel Konkan ra Continental Warehouse Corp. Nhava Sheva JNPT Seabird Marine Agencies Nhava Sheva Sima Mulls Customs Free Bonded Warehouse Mangalore 6 Dakshin Karnataka Jay Narayan Shipping Co. Mangalore Sulekha Mangalore Mangalore Mangalore Central Warehousing Corp. Mangalore 156 Sr.no. CEZ State Port CFS Name Location Kochi Bonded Warehouse Ernakulam 7 Malabar Kerala BLR Logistics Kochi Kochi Cochin Port FTWZ Cochin Central Warehousing Corp. Tuticorin Tamil Tuticorin Warehousing Madathur 8 Mannar Nadu Kalmandapam Warehousing Chennai Tuticorin Virugambakkam Warehousing Chennai ACME Warehousing Pvt. Ltd. Chennai Continental Warehousing Pvt. Ltd. Chennai VCIC Tamil Kaveri Warehousing Chennai 10 South Nadu Chennai Custom Bonded Warehousing Corp. Chennai APM Terminal South Asia Kattupalli Kattupalli Central Warehousing Corp. Kattupalli SICAL Logistics Visakhapatnam Visakhapatna EULER Herms Visakhapatnam VCIC Andhra m Maha Murthy Logistics Pvt. Ltd. Visakhapatnam 12 North Pradesh Euler Hermes Kakinada Mohasin Group of Companies Kakinada Kakinada Concor Logistics Park Kakinada West Bengal State Warehousing Corporation Kolkata BLR Logistics Kolkata Kolkata Veritas Logistics Kolkata West 14 Gaud Bengal Appejay Infralogistics Pvt. Ltd. Haldia AL Logistics (Greenways) Pvt. Ltd. Haldia LCL Logix Pvt. Ltd. Haldia Haldia Amco Cargo System India Pvt. Ltd. Haldia 157 Appendix 7: Charging points for reefers at Container Freight Stations (criteria j, 4.1.3) Interview no.: 007072017 Ref.: page 40, para 5 Interviewer: Anjali Anit Interviewee: Mr. Rajan S. (Blue Sea Shipping Agency Pvt. Ltd.) Mumbai Date: 17th June 2017 | Time: 11: 30 am Q Which container freight stations offer charging points for reefer containers? A 1. CEZ Kutch: At Kandla port, there is 1 CFS service with 48 plug-points for reefer containers. At Mundra port there is 1 CFS service that has 24 refer plug points for reefer containers 2. Hazira port has 1 CFS service available with 6 reefer plugs 3. JNPT port has 3 CFS services with 3 plug points for reefer containers 4. Kochi port is having 1 CFS service available 11 plug points for reefer containers and also cold storage facility 5.Tuticorin port has 1 CFS service with 48 reefer charging points 6. Chennai port has 3 CFS services available. 1 CFS has 11 plug points and also cold storage facility, second CFS has 55 plug points for reefer and also cold storage facility, third CFS has 36 plug points for reefer containers 7. Kattupalli port has 1 CFS service available for plug point for reefer 8. Visakhapatnam port has 1 CFS service available 75 plug point for reefer, and also has cold storage facility 9. Kakinada port has 1 CFS service available 151 plug point for reefer, and also has cold storage facility 10. Kolkata port has 2 CFS services, 1 CFS has 22 plug point for reefer and also cold storage facility and other CFS is having 29 plug point for reefer 158 Table 90 Table for criteria j Plug Point Sr. (s) no CEZ Port CFS Name Type Availab . le at CFS Kandla 1 Kandla Port Trust 48 Reefer Points 24 Reefer Plug Points for 1 Kutch Storage of Reefer Containers along aith Dg Mundra 1 Hind Terminals Power 3 Suryapur Hazira 1 Parekh Group 6 Reefer Points Navkar Corp Ltd, New 92 Reefer Plugs Mumbai, and Cold Storage Facility North EFC – Container Freight 52 Reefer Plugs 4 JNPT Station, MIDC road Konkan and Reefer Gantry at CFS SBW Logistics Pvt. Ltd., 3 CFS at Taloja 32 Reefer Plugs 11 Plug Points Triway Container Freight 7 Malabar and Cold Station Kochi 1 Storage Facility Concor Corp of India Ltd, 8 Mannar near Madurai by-pass road, Tuticorin 1 Tuticorin 48 Reefer Plugs Triway Container Freight 11 Plug Points Station and Cold Storage Facility 55 Plug Points Sanco Container Freight and Cold 10 VCIC South Station Storage Facility Concor Freight Station at 36 Plug Points Chennai 3 Chennai Reefer Plug Kattupalli 1 T G Terminals Points Alliance Shipping and 75 Reefer Plugs 12 VCIC North Visakhapatna Logistics /Gateway East with Cold m 1 India CFS Storage Facility 159 Plug Point Sr. (s) no CEZ Port CFS Name Type Availab . le at CFS 151 Plug Points Cochin Port CFS at and Cold Kakinada 1 Willington Island Storage Facility Century Container Freight 22 Plug Points Station JJP and Cold 14 Gaud Storage Facility Century Container Freight Kolkata 2 Station Sonai 29 Plug Points 160 Appendix 8: Port-to-Port connectivity (4.1.4, Sc. 1) Interview no.: 008072017 Ref.: page 44 Interviewer: Anjali Anit Interviewee: Rajan S. (Blue Sea Shipping Agency Pvt. Ltd.) / P. Deshmukh (SCA Logistics Pvt. Ltd.), Mumbai Date: 17th June 2017 | Time: 11:30 am Q What is the connectivity from origin to destination ports in the potentially viable short sea shipping routes between the Indian CEZ pairs that have been shortlisted in section 4.1.4, Scenario 1? A 1. Kandla Port is having connectivity with Mundra port thru feeder vessel & Barge service, Kandla port to JNPT port has connectivity through shipping line MSC Line 2. Mundra port is having connectivity with Kandla port thru Feeder vessel, Barge service and shipping line (SCI / Shreyas Shipping) 2.1 Mundra port is having connectivity with JNPT port thru Feeder vessel and shipping line 2.2 Mundra port is having connectivity with Kochi port thru shipping line bearing Indian flag vessel (SCI / Shreyas Shipping) 2.3 Mundra port is having connectivity with Tuticorin port thru shipping line bearing Indian flag vessel (SCI / Shreyas Shipping) 2.4 Mundra port is having connectivity with Kattupalli thru feeder vessel and shipping line bearing Indian flag vessel (SCI / Shreyas Shipping) 3. JNPT port is having connectivity with Mundra port thru feeder vessel and shipping line bearing Indian flag vessel (SCI / Shreyas Shipping) 4. Kochi port is having connectivity with Mundra port thru shipping line bearing Indian flag vessel (SCI / Shreyas Shipping) 4.1 Kochi port is having connectivity with Tuticorin port thru feeder vessel and shipping line bearing Indian flag vessel (SCI / Shreyas Shipping) 5. Tuticorin port is having connectivity with Mundra port thru shipping line bearing Indian flag vessel (SCI / Shreyas Shipping) 5.1 Tuticorin port is having connectivity with Kochi port thru shipping line bearing Indian flag vessel (SCI / Shreyas Shipping) 5.2 Tuticorin port is having connectivity with Kattupalli port thru shipping line bearing Indian flag vessel (SCI / Shreyas Shipping) 161 6. Chennai port is having connectivity with Kattupalli port thru Feeder vessel and shipping line bearing Indian flag vessel (SCI) 7. Kattupalli port is having connectivity with Mundra port thru shipping line bearing Indian flag vessel (SCI / Shreyas Shipping) 7.1 Kattupalli port is having connectivity with Kochi port thru shipping line bearing Indian flag vessel (SCI / Shreyas Shipping) 7.2 Kattupalli port is having connectivity with Tuticorin port thru shipping line bearing Indian flag vessel (SCI / Shreyas Shipping) 7.3 Kattupalli port is having connectivity with port Chennai thru Feeder vessel and shipping line bearing Indian flag vessel (SCI / Shreyas Shipping) Table 91 Tabulation of above interview: No. of Name of the Cargo Service Frequ company Moved Route s ency Type Ref Empty Kandla -Mundra point 1 Contain - Jabeleli - Weekl Feeder Kandla Paramount Shipping er Kandla 1 y Vessel port Import Kandla -Mundra Barge point 1 Arcadia Shipping & Contain - Pipavav - Weekl Servic Kandla Trading Company er Kandla 1 y e port Import / Export Contain er /Empty Shippi Contain Weekl ng MSC Line er Kandla - JNPT 1 y Line Import / Export Contain er /Empty point 1 Orient Liner / Contain Kandla - Mundra Twice Feeder Kandla Parekh Shipping er - JNPT -Jabeleli 2 a week Vessel port Import MSC Line /CMA / CGM / Hapag- Export Lloyd /Hamburg Contain Shippi point 2 Sud/OOCL/APL/ er Mundra - JNPT - Weekl ng Mundra NYK/YML/Emirat /Empty Jabeleli 11 y Line port 162 No. of Name of the Cargo Service Frequ company Moved Route s ency Type Ref es Shipping Contain /KMTC/MOL er Import / Export Contain er /Empty point 2 Contain Weekl Feeder Mundra Seimatech er Mundra- JNPT 1 y Vessel port Import / Export Contain er /Empty Contain Weekl Feeder Seimatech / OEL er JNPT- Mundra 2 y Vessel Point 3 Import / Export Contain er Colombo - Kochi /Empty - Colombo - BTL / Chakiat Contain Tuticorin - Weekl Feeder /Maersk Line er Colombo 3 y Vessel point 4.3 Import / Export Contain er /Empty Kattupali - Contain Chennai - Twice Feeder TS Line er Colombo 1 a week Vessel point 7.3 Import / Export Contain er Chennai - /Empty Kattupalli - point 6 Contain Singapore -Port- Twice Feeder Chennai MOL Feeder er Klang 1 a week Vessel port 163 Source: Ministry of shipping Government on India, Vision for Coastal Shipping. Link - shipping.nic.in Table 92 Connectivity to port Name of the company Cargo Moved Route No.of Services Frequency Mundra - Cochin - Tuticorin - Mangalore - Shreyas Shipping Containers Hazira -Mundra 2 Weekly Mundra -Hazira - Nhavasheva - Mundra - Karachi - Shreyas Shipping Containers Mundra 1 Every 10 days Nhavasheva- Goa-Nhava Sheva-Pipavav- Shreyas Shipping Containers Nhava Sheva 1 Weekly Mundra - Tuticorin - Visakhapatnam - Kattupalli - Tuticorin- Cochin -Jebel Shreyas Shipping Containers Ali - Mundra 2 Fortnightly Visakhapatnam - Kolkata -Haldia - Visakhapatnam - Kakinada - Shreyas Shipping Containers Visakhapatnam 1 Weekly Mundra - Pipavav - Cochin - SCI Container Tuticorin 3 Weekly 164 Appendix 9: Port-to-Port Connectivity (4.1.4, Sc. 2) Interview no.: 009072017 Ref.: page 44 Interviewer: Anjali Anit Interviewee: Rajan S. (Blue Sea Shipping Agency Pvt. Ltd.) / P. Deshmukh (SCA Logistics Pvt Ltd), Mumbai Date: 17th June 2017 | Time: 11:30 am Q What is the connectivity from origin to destination ports in the potentially viable short sea shipping routes between the Indian CEZ pairs that have been shortlisted in section 4.1.4, Scenario 2? A 1. Mundra port has connectivity with Hazira port, JNPT port, Kochi port, Tuticorin port, Kattupalli port, Visakhapatnam port, Kakinada port through Indian flag-bearing vessel (SCI / Shreyas Shipping) 2. Hazira port is having connectivity with Mundra port and JNPT port through Indian flag- bearing vessel (SCI / Shreyas Shipping) 3. JNPT port is having connectivity with Mundra port through feeder vessel and through Indian flag-bearing vessel (SCI / Shreyas Shipping) 4. Kochi port is having connectivity with Mundra port, Hazira port, Tuticorin port through Indian flag-bearing vessel (SCI / Shreyas Shipping) 5. Tuticorin port is having connectivity with Mundra port, Hazira port, Kochi port, Kattupalli port, Visakhapatnam through Indian flag-bearing vessel (SCI / Shreyas Shipping) 6. Visakhapatnam port is having connectivity with Mundra port, Kochi, Tuticorin port, Kattupalli port through Indian flag-bearing vessel (SCI / Shreyas Shipping) Source: Ministry of shipping Government of India under section Vision for Coastal Shipping. Link - shipping.nic.in 165 Table 93 Connectivity to port Name of the company Cargo Moved Route No. of Services Frequency Mundra - Cochin - Tuticorin - Mangalore - Shreyas Shipping Containers Hazira -Mundra 2 Weekly Mundra -Hazira - Nhavasheva - Mundra - Karachi - Shreyas Shipping Containers Mundra 1 Every 10 days Mundra - Tuticorin - Visakhapatnam -Kattupalli - Tuticorin- Cochin -Jebel Shreyas Shipping Containers Ali - Mundra 2 Fortnightly Visakhapatnam -Kolkata - Haldia - Visakhapatnam - Kakinada - Shreyas Shipping Containers Visakhapatnam 1 Weekly Mundra - Pipavav - Cochin - SCI Container Tuticorin 3 Weekly 166 Appendix 10: Distance between all ports from selected CEZs and Hazira port Interview no.: 010072017 Ref.: page 44 Interviewer: Anjali Anit Interviewee: Mr. Gourab Nandi , Adani Hazira Port Pvt Ltd (AHPPL) Date: 7th July 2017 | Time: 12:07 pm Q What is the distance from Hazira port to Kandla port, Mundra port, JNPT port, Kochi port, Tuticorin port, Chennai port, Kattupalli port, Kakinada port, and Visakhapatnam port? A A Nautical Mile from Hazira port to Kandla port – 420 nm, Hazira port to Mundra port – 396 nm , Hazira port to JNPT port – 257 nm, Hazira port to Kochi port – 866 nm ,Hazira port to Tuticorin port – 1113 nm , Hazira port to Chennai port – 1462 nm , Hazira port to Kattupalli port – 1472.8 , Hazira port to Kakinada port – 1812 , Hazira port to Visakhapatnam – 1881 nm. Table 94 Distance between all ports from CEZ Suryapur and Hazira port ORIGIN DESTINATION Kochi - Malabar Kandla - Kutch Mundra - Kutch JNPT - North Konkan Chennai - VCIC South Kattupalli - VCIC South Kakinada - VCIC North Visakhapatnam - VCIC North Tuticorin - Mannar CEZ Port Suryapur Hazira 420 396 257 866 1113 1462 1472.8 1812 1881 167 Appendix 11: PORT - PORT CALCULATION Table 95 PORT - PORT CALCULATION (NAUTICAL MILES) ORIGIN DESTINATION Mundra - Kutch Visakhapatnam - VCIC North Kandla - Kutch Hazira - Suryapur Kochi - Malabar Tuticorin - Mannar JNPT - North Konkan Chennai - VCIC South Kattupalli - VCIC South Kakinada - VCIC North Sr.no. CEZ Port Kandla 24 420 453 1063 1309 1658 1668.8 2008 2077 1 Kutch Mundra 24 396 429 1039 1285 1634 1644.8 1984 2052 3 Suryapur Hazira 420 396 257 866 1113 1462 1472.8 1812 1881 4 North Konkan JNPT 453 429 257 611 857 1206 1216.8 1556 1625 7 Malabar Kochi 1063 1039 866 611 267 616 626.8 966 1035 8 Mannar Tuticorin 1309 1285 1113 857 267 349 359.8 699 767 Chennai 1658 1634 1462 1206 616 349 10.8 350 419 10 VCIC South Kattupalli 1668.8 1644.8 1472.8 1216.8 626.8 359.8 10.8 339.2 408.2 Kakinada 2008 1984 1812 1556 966 699 350 339.2 68 12 VCIC NORTH Visakhapatnam 2077 2052 1881 1625 1035 767 419 408.2 68 Vessel Speed: 20 NM/ Hour Note: The knot (/nɒt/) is a unit of speed equal to 1 nautical mile (1.852 km) per hour, approximately 1.151 mph. The ISO Standard symbol for the knot is kn. 168 Table 96 PORT - PORT CALCULATION (HOURS) ORIGIN DESTINATION Mundra - Kutch Visakhapatnam - VCIC North Kandla - Kutch Hazira - Suryapur Kochi - Malabar Tuticorin - Mannar JNPT - North Konkan Chennai - VCIC South Kattupalli - VCIC South Kakinada - VCIC North Sr.no. CEZ Port Kandla 1.2 21 22.65 53.15 65.45 82.9 83.44 100.4 103.85 1 Kutch Mundra 1.2 19.8 21.45 51.95 64.25 81.7 82.24 99.2 102.6 3 Suryapur Hazira 21 19.8 12.85 43.3 55.65 73.1 73.64 90.6 94.05 4 North Konkan JNPT 22.65 21.45 12.85 30.55 42.85 60.30 60.84 77.8 81.25 7 Malabar Kochi 53.15 51.95 43.3 30.55 13.35 30.8 31.34 48.3 51.75 8 Mannar Tuticorin 65.45 64.25 55.65 42.85 13.35 17.45 17.99 34.95 38.35 Chennai 82.9 81.7 73.1 60.30 30.8 17.45 0.54 17.5 20.95 10 VCIC South Kattupalli 83.44 82.24 73.64 60.84 31.34 17.99 0.54 16.96 20.41 Kakinada 100.4 99.2 90.6 77.8 48.3 34.95 17.5 16.96 0 3.4 12 VCIC NORTH Visakhapatnam 103.85 102.6 94.05 81.25 51.75 38.445 20.95 20.41 3.4 Note: If the TAT is less than 24 hours we have taken it as 1 day and if the TAT is more than 24 hours, then, 2 days, and so on so forth - in the below table. 169 Table 97 PORT - PORT CALCULATION (DAYS) ORIGIN DESTINATION North Visakhapatnam - VCIC Kattupalli - VCIC South Kakinada - VCIC North JNPT - North Konkan Chennai - VCIC South Tuticorin - Mannar Hazira - Suryapur Kochi - Malabar Mundra - Kutch Kandla - Kutch Sr.no. CEZ Port Kandla 1 1 1 3 3 4 4 5 5 1 Kutch Mundra 1 1 1 3 3 4 4 5 5 3 Suryapur Hazira 1 1 1 2 3 4 4 4 4 4 North Konkan JNPT 1 1 1 2 2 3 3 4 4 7 Malabar Kochi 3 3 2 2 1 2 2 3 3 8 Mannar Tuticorin 3 3 3 2 1 1 1 2 2 Chennai 4 4 4 3 2 1 1 1 1 10 VCIC South Kattupalli 4 4 4 3 2 1 1 1 1 Kakinada 5 5 4 4 3 2 1 1 0 1 12 VCIC NORTH Visakhapatnam 5 5 4 4 3 2 1 1 1 170 Appendix 12: Port-to-port turnaround time from origin to each destination port in each selected CEZ. Table 98 Kandla port (CEZ Kutch) On Transport On Post harvesting, PDD PDA Total land at sea land CEZ sorting and loading at farm to to PORT port buyer ORIGIN DESTINATION Kandla Mundra 2 1 2 1 2 1 9 Kandla Hazira 2 1 2 1 2 1 9 Kandla JNPT 2 1 2 1 2 1 9 Kandla Kochi 2 1 2 3 2 1 11 Kandla Tuticorin 2 1 2 3 2 1 11 Kutch Kandla Chennai 2 1 2 4 2 1 12 Kandla Kattupalli 2 1 2 4 2 1 12 Kandla Kakinada 2 1 2 5 2 1 13 Kandla Visakhapatnam 2 1 2 5 2 1 13 Table 99 Mundra port (CEZ Kutch) On Transport On Post harvesting, land PDD PDA at sea land Total CEZ sorting and loading at farm to to PORT port buyer ORIGIN DESTINATION Mundra Kandla 2 1 2 1 2 1 9 Mundra Hazira 2 1 2 1 2 1 9 Mundra JNPT 2 1 2 1 2 1 9 Mundra Kochi 2 1 2 3 2 1 11 Mundra Tuticorin 2 1 2 3 2 1 11 Kutch Mundra Chennai 2 1 2 4 2 1 12 Mundra Kattupalli 2 1 2 4 2 1 12 Mundra Kakinada 2 1 2 5 2 1 13 Mundra Visakhapatnam 2 1 2 5 2 1 13 171 Table 100 Hazira port (CEZ Suryapur) On Transport On Post harvesting, PDD PDA Total land at sea land CEZ sorting and loading at farm to to port PORT buyer ORIGIN DESTINATION Hazira Kandla 2 1 2 1 2 1 9 Hazira Mundra 2 1 2 1 2 1 9 Hazira JNPT 2 1 2 1 2 1 9 Hazira Kochi 2 1 2 2 2 1 10 Suryapur Hazira Tuticorin 2 1 2 3 2 1 11 Hazira Chennai 2 1 2 4 2 1 12 Hazira Kattupalli 2 1 2 4 2 1 12 Hazira Kakinada 2 1 2 4 2 1 12 Hazira Visakhapatnam 2 1 2 4 2 1 12 Table 101 JNPT port (CEZ North Konkan) On Transport On Post harvesting, PDD PDA Total land at sea land CEZ sorting and loading at farm to to PORT port buyer ORIGIN DESTINATION JNPT Kandla 2 1 2 1 2 1 9 JNPT Mundra 2 1 2 1 2 1 9 JNPT Hazira 2 1 2 1 2 1 9 JNPT Kochi 2 1 2 2 2 1 10 North JNPT Tuticorin 2 1 2 2 2 1 10 Konkan JNPT Chennai 2 1 2 3 2 1 11 JNPT Kattupalli 2 1 2 3 2 1 11 JNPT Kakinada 2 1 2 4 2 1 12 JNPT Visakhapatnam 2 1 2 4 2 1 12 172 Table 102 Kochi port (CEZ Malabar) On Transport On Post harvesting, PDD PDA Total land at sea land CEZ sorting and to to loading at farm PORT port buyer ORIGIN DESTINATION Kochi Kandla 2 1 2 3 2 1 11 Kochi Mundra 2 1 2 3 2 1 11 Kochi Hazira 2 1 2 2 2 1 10 Kochi JNPT 2 1 2 2 2 1 10 Malabar Kochi Tuticorin 2 1 2 1 2 1 9 Kochi Chennai 2 1 2 2 2 1 10 Kochi Kattupalli 2 1 2 2 2 1 10 Kochi Kakinada 2 1 2 3 2 1 11 Kochi Visakhapatnam 2 1 2 3 2 1 11 Table 103 Tuticorin port (CEZ Mannar) On Transport On Post harvesting, PDD PDA Total land at sea land CEZ sorting and loading at farm to to PORT port buyer ORIGIN DESTINATION Tuticorin Kandla 2 1 2 3 2 1 11 Tuticorin Mundra 2 1 2 3 2 1 11 Tuticorin Hazira 2 1 2 3 2 1 11 Tuticorin JNPT 2 1 2 2 2 1 10 Mannar Tuticorin Kochi 2 1 2 1 2 1 9 Tuticorin Chennai 2 1 2 1 2 1 9 Tuticorin Kattupalli 2 1 2 1 2 1 9 Tuticorin Kakinada 2 1 2 2 2 1 10 Tuticorin Visakhapatnam 2 1 2 2 2 1 10 173 Table 104 Chennai port (CEZ VCIC South) On Transport On Post harvesting, PDD PDA Total land at sea land CEZ sorting and loading at farm to to port PORT buyer ORIGIN DESTINATION Chennai Kandla 2 1 2 4 2 1 12 Chennai Mundra 2 1 2 4 2 1 12 Chennai Hazira 2 1 2 4 2 1 12 Chennai JNPT 2 1 2 3 2 1 11 VCIC Chennai Kochi 2 1 2 2 2 1 10 South Chennai Tuticorin 2 1 2 1 2 1 9 Chennai Kattupalli 2 1 2 1 2 1 9 Chennai Kakinada 2 1 2 1 2 1 9 Chennai Visakhapatnam 2 1 2 1 2 1 9 Table 105 Kattupalli port (CEZ VCIC South) On Transport On Post harvesting, PDD PDA Total land at sea land CEZ sorting and loading at farm to to PORT port buyer ORIGIN DESTINATION Kattupalli Kandla 2 1 2 4 2 1 12 Kattupalli Mundra 2 1 2 4 2 1 12 Kattupalli Hazira 2 1 2 4 2 1 12 Kattupalli JNPT 2 1 2 3 2 1 11 VCIC Kattupalli Kochi 2 1 2 2 2 1 10 South Kattupalli Tuticorin 2 1 2 1 2 1 9 Kattupalli Chennai 2 1 2 1 2 1 9 Kattupalli Kakinada 2 1 2 1 2 1 9 Kattupalli Visakhapatnam 2 1 2 1 2 1 9 174 Table 106 Visakhapatnam port (CEZ VCIC North) On Transport On Post harvesting, PDD PDA Total land at sea land CEZ sorting and PORT loading at farm to to port buyer ORIGIN DESTINATION Visakhapatnam 2 5 2 1 Kandla 2 1 13 Visakhapatnam 2 5 2 1 Mundra 2 1 13 Visakhapatnam 2 4 2 1 Hazira 2 1 12 Visakhapatnam 2 4 2 1 JNPT 2 1 12 VCIC Visakhapatnam Kochi 2 3 2 1 North 2 1 11 Visakhapatnam Tuticorin 2 2 2 1 2 1 10 Visakhapatnam Chennai 2 1 2 1 2 1 9 Visakhapatnam 2 1 2 1 Kattupalli 2 1 9 Visakhapatnam Kakinada 2 1 2 1 2 1 9 175 Table 107 Kakinada port (CEZ VCIC North) Post On Transport On harvesting, PDD PDA Total land at sea land CEZ sorting and PORT loading at farm to to port buyer ORIGIN DESTINATION 2 5 2 1 Kakinada Kandla 2 1 13 2 5 2 1 Kakinada Mundra 2 1 13 2 4 2 1 Kakinada Hazira 2 1 12 2 4 2 1 Kakinada JNPT 2 1 12 VCIC North Kochi 2 3 2 1 Kakinada 2 1 11 Tuticorin 2 2 2 1 Kakinada 2 1 10 Chennai 2 1 2 1 Kakinada 2 1 9 2 1 2 1 Kakinada Kattupalli 2 1 9 Visakhapatnam 2 1 2 1 Kakinada 2 1 9 176 Appendix 13: Selected products with their average production quantities (in MT) and total quantities per CEZ for each of the seven CEZs Table 108 CEZ Kutch Districts Fruits Vegetables Banana Pomegranate Papaya Date Mango Cabbage Onion Eggplant Okra Tomato Kutch 70767 24059 174601 102778 50998 8715 61233 26275 5443 25692 Total 70767 24059 174601 102778 50998 8715 61233 26275 5443 25692 Rank 3 5 1 2 4 4 1 3 5 2 Table 109 CEZ Suryapur Distric ts Fruits Vegetables Banan Papa Mang Indian Gua Sapot Cocon Cucurb Toma Eggpla Cowp Cauliflo a ya o Jujube va a ut its to Okra nt ea wer Baruc 10783 5633 2336 2406 h 30 3 3 6923 6141 51483 11227 0 26186 6783 52778 2824 6783 4277 1218 Surat 9 7 8 3 1794 40005 28892 10 81673 22917 Navsa 10400 1666 2083 7563 5927 ri 0 0 67 2 4259 120894 21510 6 43743 6310 17101 1012 2995 1184 2051 15160 Total 19 40 68 6923 6141 05 6053 212382 61629 46 2 13093 22917 Rank 2 4 1 5 6 3 7 1 4 2 3 6 5 Table 110 CEZ North Konkan Districts Fruits Vegetables Banana Grapes Mango Onion Tomato Nasik 373306 14413 1513359 152371 Thane 6648 13225 7895 Pune 67585 75755 598190 56606 Raigad 990 19415 2846 Total 75223 449061 47053 2111549 219718 Rank 2 1 3 1 2 177 Table 111 CEZ Malabar Districts Fruits Vegetables Banana Coconut Mango Papaya Pineapple Drumstick Ginger Sweet Potato Tapioca Ernakulam 499918 216000000 27837 7585 54951 610 319 51 215452 Alappuzha 4405 222666667 18409 7587 547 301 271 43 74322 Kolam 17402 410666667 49480 3318 3241 2124 788 29 587113 Thiruvananthapuram 19681 589333333 26154 9426 1839 3018 165 97 489392 Total 541406 1438666667 121880 27916 60578 6053 1543 220 1366279 Rank 2 1 3 5 4 2 3 4 1 Table 112 CEZ Mannar Districts Fruits Vegetables Bana Jackfr Man Pineap Grap Pomefr Okr Eggpla Ging Sweet Tapio Onio Toma Coconut na uit go ple es uit a nt er Potato ca n to Kanyaku 1937 14862996 11376 8144 2144 1061 209 738 123 mari 29 6.7 426 1 2197 2100 235 2015 95168600 554 2287 2533 Tirunelveli 66 6 6 5748 5 6931 4134 24379856 2315 256 11950 2015 Total 8144 1061 554 2287 3271 123 95 6.7 0 5 426 9 5 6931 Rank 2 1 4 3 6 7 5 5 4 7 6 1 2 3 Table 113 CEZ VCIC South Districts Fruits Vegetables Coconut Banana Mango Guava Watermelon Tapioca Sweet Potato Okra Eggplant Tomato* Tiruvallur 5904833 50794 77005 800 2718 131 1369 6778 76 Kancheepuram 19944567 13322 16314 2665 262 3536 6189 437 Total 25849400 64116 93319 800 5383 393 4905 12967 513 Rank 1 3 2 4 2 5 3 1 4 178 Table 114 CEZ VCIC North Districts Fruits Vegetables Banan Lemo Papay Sapot Orang Beans & Onio Tomat Cabbag Tapioc Eggplan Coconut Mango Okra a n a a e mutter n o e a t 19321 1221 2094667 36441 26100 11864 2927 1159 Guntur 3 0 24985 13779 Krishna 38507 35997000 8365 5170 528158 1454 5383 16845 2197 9625 West 25554 524369333 28951 27829 89485 19135 1589 Godavari 1 5940 1250 13892 East 28289 1271 743674000 25235 170446 16358 10946 Godavari 2 7 15181 174763 20293 Visakhapatna 22984 112446000 9036 591 106017 10649 3827 m 16867 2064 10395 12609 337780000 43369 1330 117548 3571 5321 Vizianagaram 6 12329 3319 7208 Srikakulam 41204 294986333 4441 313 766608 1906 16969 8479 4041 7562 96043 205134733 14437 177826 3737 Total 65392 19268 16358 34165 38222 7 3 5 2 6 100626 9557 178077 82754 Rank 3 1 5 4 6 2 7 6 5 4 2 7 1 3 179 Appendix 14: District-wise three-year as well as average production data in each of the selected CEZs. Table 115 CEZ Kutch Sr Average No Production across most Production Harvest Data years . Product recent three years (MT) (MT) Seasons considered District: Kutch FRUITS 2000, 2012, 1 Banana 16160 97450 98690 70767 Whole Year 2014 Pomegra February, 2 nate 3437 44681 24059 March, April 2000, 2014 2000, 2012, 3 Papaya 9500 212017 302286 174601 Whole Year 2014 March , April, 4 Date 53838 151718 102778 May, June 2000, 2014 April, May , 2000, 2012, 5 Mango 12730 62415 77850 50998 June 2014 VEGET ABLES February, 2000, 2012, 1 Cabbage 2025 9428 14693 8715 March 2015 September, November, December, January, Febuary,Marc 2005, 2006, 2 Onion 23100 22700 46300 61233 h,April, May 2007 July , August, 2000, 2012, 3 Eggplant 6715 22590 49521 26275 September 2015 April, May, 2000, 2012, 4 Okra 2465 7147 6716 5443 June 2015 January, February, 2000, 2012, 5 Tomato 2490 29760 44827 25692 March, April 2015 180 Table 116 CEZ Suryapur Sr Average No Production across most Production Harvest Data years . Product recent three years (MT) (MT) Seasons considered District: Bharuch FRUITS 2012, 2013, 1 Banana 1092634 1059100 1083255 1078330 Whole Year 2014 2012, 2013, 2 Papaya 65840 57399 45759 56333 Whole Year 2014 2012, 2013, 3 Mango 24528 22348 23214 23363 April, May , June 2014 Indian October, 2011, 2013, 4 Jujube 6875 6863 7031 6923 November 2014 2012, 2013, 5 Guava 6080 6131 6211 6141 Whole Year 2014 VEGET ABLES August, Cucurbit September, 2011, 2013, 1 s 31588 60085 62776 51483 October 2014 January, 2. February, March, 2012, 2013, 2 Tomato 8910 11993 12778 11227 April 2014 2012, 2013, 3 3.Okra 22662 22990 26528 24060 April, May June 2014 July , August, 2012, 2013, 4 4.Brinjal 22321 26892 29346 26186 September 2014 5.Cowpe 2011, 2013, 5 a 6751 6460 7138 6783 June, July August 2014 District: Surat FRUITS 1. 2011, 2012, 1 Banana 520093 589450 473824 527789 Whole Year 2013 2011, 2012, 2 2. Mango 63200 64400 75914 67838 April, May , June 2013 January, February, May, 3 3. Sapota 63200 22345 42773 June 2011, 2013 2011, 2012, 4 4. Papaya 31960 31920 20862 28247 Whole Year 2013 5. 5 Coconut 1747 1840 1794 Whole Year 2011, 2013 VEGET ABLES 2011, 2012, 1 1. Okra 122271 118035 125125 121810 April, May June 2013 181 Sr Average No Production across most Production Harvest Data years . Product recent three years (MT) (MT) Seasons considered July , August, 2011, 2012, 2 2.Brinjal 82215 70540 92264 81673 September 2013 3.Cucurb July , August, 3 its 23985 56025 40005 September 2011, 2013 January, 4.Tomat February, March, 2011, 2012, 4 o 22412 28200 36064 28892 April 2013 5. Cauliflo 2011, 2012, 5 wer 19100 25250 24400 22917 February, March 2013 District: Navsari FRUITS 2011, 2012, 1 1. Mango 201600 213066 210436 208367 April, May, June 2013 2011, 2012, 2 2.Banana 75000 120850 116150 104000 Whole Year 2013 January, February, may, 3 3. Chiku 74400 76863 75632 June 2011, 2013 4 4.Papaya 16120 17199 16660 Whole Year 2011, 2013 5.Cocon 5 ut 4500 3818 4159 Whole Year 2011, 2013 VEGET ABLES 1. August, Cucurbit September, 1 s 132250 109538 120894 October 2011, 2013 2011, 2012, 2 2.Okra 56400 62140 59287 59276 April, May June 2013 July , August, 2011, 2012, 3 3.Brinjal 43365 43617 44247 43743 September 2013 January, 4. February, March, 2011, 2012, 4 Tomato 19740 20640 24150 21510 April 2013 5. 5 Cowpea 6160 6459 6310 June, July August 2011, 2013 182 Table 117 CEZ North Konkan Sr Average N Production across most Production Data years o. Product recent three years (MT) (MT) Harvest Seasons considered District: Nasik FRUI TS 1.Grap 733 February, 1 es 515 13096 373306 March, April 2002, 2012 2.Mang 103 May, June, 2 o 10 18515 14413 July 2002, 2012 VEGE TABL ES August 1.Onio 793 223297 ,September, 1 n 741 6 1513359 October 2002, 2012 2.Toma 843 March, April, 2 to 1 296311 152371 May 2002, 2012 District: Thane FRUI TS 1.Bana 116 1 na 70 1625 6648 Whole Year 2002, 2012 2. 140 May, June, 2 Mango 00 12450 13225 July 2002, 2012 VEGE TABL ES 1.Toma 114 March, April, 1 to 9 14640 7895 May 2002, 2012 District: Pune FRUI TS 1. 763 1 Banana 50 58819 67585 Whole Year 2002, 2012 2. 172 February, 2 Grapes 02 14307 15755 March, April 2002, 2012 VEGE TABL ES August 1.Onio 325 ,September, 1 n 210 871169 598190 October 2002, 2012 2.Toma 254 March, April, 2 to 12 87799 56606 May 2002, 2012 183 District: Raigadh FRUI TS 1.Bana 173 1 na 0 250 990 Whole Year 2002, 2012 2. 186 May, June, 2 Mango 70 20160 19415 July 2002, 2012 VEGE TABL ES 1. Tomat 101 March, April, 1 o 1 4680 2846 May 2002, 2012 Table 118 CEZ Malabar Sr Average No Production across most Production Data years . Product recent three years (MT) (MT) Harvest Seasons considered District: Ernakulam FRUIT S 2012, 2013, 1.Banana 50746 54056 44953 49918 Whole Year 1 2014 2.Cocon 209000 227000 212000 2012, 2013, 216000000 Whole Year 2 ut 000 000 000 2014 3. 2003, 2012, 17262 32276 33978 27839 March, April 3 Mango 2013 4. 2002, 2003, 7248 7115 8391 7585 Whole Year 4 Papaya 2012 5. May, June, July, Pineappl 56478 57669 50706 54951 2002, 2003, August 5 e 2012 VEGET ABLES 1.Drums March,April,July, 2003, 2012, 675 563 593 611 1 tick August,September 2013 December , 2012, 2013, 2.Ginger 388 304 265 319 2 January, February 2014 May, June, 3.SweetP 46 72 36 51 September, 2012, 2013, otato 3 October 2014 July, August, 4. 211550 205464 229341 215452 September, 2012, 2013, Tapioca 4 October 2014 District: Alappuzha 184 FRUIT S 2012, 2013, 1.Banana 5002 4481 3733 4405 Whole Year 1 2014 2. 233000 217000 218000 2012, 2013, 222666667 Whole Year 2 Coconut 000 000 000 2014 2012, 2013, 3.Mango 16221 18300 20707 18409 March, April 3 2014 4. Pineappl 392 569 681 547 Whole Year 2003, 2012, 4 e 2014 May, June, July, 2003, 2012, 5.Papaya 5581 9255 7925 7587 5 August 2014 VEGET ABLES 1.Drums March,April,July, 2012, 2013, 277 292 334 301 1 tick August,September 2014 December , 2012, 2013, 2.Ginger 261 292 261 271 2 January, February 2014 May, June, 3.SweetP 21 33 75 43 September, 2012, 2013, otato 3 October 2014 July, August, 4. 72919 62212 87834 74322 September, 2012, 2013, Tapioca 4 October 2014 District: Kolam FRUIT S 2012, 2013, 1.Banana 16022 19151 17032 17402 Whole Year 1 2014 2.Cocon 372000 473000 387000 2012, 2013, 410666667 Whole Year 2 ut 000 000 000 2014 2003, 2012, 3.Mango 27296 62540 58605 49480 March, April 3 2013 2002, 2003, 4.Papaya 1021 1042 7891 3318 Whole Year 4 2012 5. May, June, July, Pineappl 4497 4206 1019 3241 2002, 2003, August 5 e 2012 VEGET ABLES 1.Drums March,April,July, 2012, 2013, 3224 1648 1501 2124 1 tick August,September 2014 December , 2012, 2013, 2.Ginger 819 716 828 788 2 January, Febuary 2014 185 May, June, 3.SweetP 22 22 44 29 September, 2012, 2013, otato 3 October 2014 July, August, 4.Tapioc 531482 568257 661600 587113 September, 2012, 2013, a 4 October 2014 District: Thiruvananthapuram FRUIT S 2012, 2013, 1.Banana 24290 18753 15999 19681 Whole Year 1 2014 2.Cocon 552000 551000 665000 2012, 2013, 589333333 Whole Year 2 ut 000 000 000 2014 2003, 2012, 3.Mango 24264 25617 28582 26154 March, April 3 2013 2002, 2003, 4.Papaya 6855 6979 14443 9426 Whole Year 4 2012 5.Pineap May, June, July, 2002, 2003, 2207 2005 1306 1839 5 ple August 2012 VEGET ABLES 1. March,April,July, Drumsti 3585 2661 2807 3018 2003, 2012, August,September 1 ck 2013 December , 2012, 2013, 2.Ginger 154 138 203 165 2 January, Febuary 2014 May, June, 3.SweetP 147 77 66 97 September, 2012, 2013, otato 3 October 2014 July, August, 4.Tapioc 414271 467512 586394 489392 September, 2012, 2013, a 4 October 2014 186 CEZ Mannar Sr Average No Production across most Production Harvest Data years . Product recent three years (MT) (MT) Seasons considered District: Kanyakumari FRUITS 21997 2009, 2011, 1.Banana 163039 198178 193729 Whole year 1 0 2013 38990 1584000 2871000 June,July,Augus 2009, 2011, 2.Coconut 148629967 2 0 00 00 t,September 2013 2009, 2011, 3.Jackfruit 8819 7469 8144 Whole year 3 2013 March, April, 4.Mango 2955 1333 2144 4 May, June 2002, 2003 5 5.Pineapple 1578 543 1061 Whole year 2002, 2003 VEGETABL ES March,July,Aug 1.Okra 194 224 209 1 ust, September 2002, 2003 February,Marc 2.Eggplant 335 1141 738 2 h,July, August 2002, 2003 2009, 2011, 3.Ginger 225 110 35 123 Whole year 3 2013 January, 1997, 1998, 4.SweetPotato 230 1035 13 426 4 Febuary 2008 18958 July, August, 2009, 2011, 5.Tapioca 115400 36303 113761 5 1 September 2013 District: Tirunelveli FRUITS 23557 2009, 2011, 1.Banana 211954 211773 219766 Whole year 1 0 2013 20580 1280000 1573000 June,July,Augus 2009, 2011, 2.Coconut 95168600 2 0 00 00 t,September 2013 April,May,Aug 3.Grapes 566 541 554 3 ust,September 2002, 2003 March, April, 4.Mango 28716 13296 21006 4 May, June 2002, 2003 5.Pome July,August,Sep Fruit(Apple, 1836 2738 2287 tember 5 Pears) 2002, 2003 VEGETABL ES March,July,Aug 1. Okra 2644 2067 2356 1 ust, September 2002, 2003 February,Marc 2.Eggplant 3062 2003 2533 2 h,July, August 2002, 2003 187 September, 2009, 2011, 3.Onion 26371 24035 10059 20155 3 October 2013 July, August, 2009, 2011, 4.Tapioca 5894 7908 3441 5748 4 September 2013 September,Oct 5.Tomato 7255 6607 6931 5 ober 2002, 2003 Table 119 CEZ VCIC South Average Sr Production across most recent Production Harvest Data years No. Product three years (MT) (MT) Seasons considered District: Thiruvallur FRUITS Whole 2009, 2010, 1. Banana 75048 47846 29489 50794 1 Year 2013 June, July, 2. 14500 10200000 7500000 5904833 August, 2009, 2010, Coconut 2 September 2013 March, 3.Mango 71255 33114 126646 77005 April, 2002, 2003, 3 May, June 2013 2006, 2007, 4.Guava 800 (average of 3 years) 800 6 months 4 2008 VEGET ABLES 1. Sweet January, 2007, 2008, 27 292 73 131 1 Potato Febuary 2009 July, 2. Tapioca 3516 3201 1436 2718 August, 2009, 2011, 2 September 2013 Febuary, 3. March, 3086 2790 14457 6778 Eggplant July, 2003, 2006, 3 August 2013 March, July, 4. Okra 1401 1337 1369 August, 4 September 2002, 2003 5.Tomato 76 76 5 * 2006 District: Kancheepuram FRUITS June, July, 1. 33700 25900000 33900000 19944567 August, 2009, 2011, Coconut 1 September 2013 188 Whole 2006, 2009, 2. Banana 16352 12340 11273 13322 2 Year 2011 March, 3. Mango 5902 18917 24122 16314 April, 2003, 2006, 3 May, June 2013 1356 4.Guava 3696 2526 4 (estd.) 2006, 2013 5.Waterm n/a 5 elon VEGET ABLES July, 1. Tapioca 551 3465 3980 2665 August, 2009, 2011, 1 September 2013 2. Sweet January, 2006, 2008, 480 199 106 262 2 Potato Febuary 2013 March, July, 3. okra 1851 5175 3582 3536 August, 2002, 2003, 3 September 2006 Febuary, 4. March, 2388 9185 6994 6189 Eggplant July, 2002, 2003, 4 August 2006 September 2002, 2003, 5. Tomato 296 599 417 437 , October 2006 Table 120 CEZ VCIC North Sr Average No Production across most Production Data years . Product recent three years (MT) (MT) Harvest Seasons considered District: Guntur FRUITS 2010, 2011, 1.Banana 151597 216308 211734 193213 Whole year 1 2013 2.Coconu 201600 184000 242800 2012, 2013, 2094667 Whole year 2 t 0 0 0 2014 October, 2012, 2013, 3.Lemon 35068 36743 37512 36441 3 November 2014 2012, 2013, 4.Papaya 35076 29369 13856 26100 Whole year 4 2014 October, 2012, 2013, 5.Sapota 12933 11833 10826 11864 5 November 2014 VEGET ABLES 1. Beans July, August 2348 3505 2927 1 & Mutter September 2002, 2003 189 April , May, 2.Okra 10018 14402 12210 September, 2 October 2002, 2003 September, 3.Brinjal 11265 7076 22995 13779 October, Febuary, 2012, 2013, 3 March 2014 October , 5.Tomat 35211 26002 13742 24985 November, March, 2012, 2013, o 4 April 2014 October , 5.Tomat 35211 26002 13742 24985 November, March, 2012, 2013, o 5 April 2014 District: Krishna FRUITS 2010, 2011, 1.Banana 33847 35937 45738 38507 Whole year 1 2013 2.Coconu 343090 340290 396530 2012, 2013, 35997000 Whole year 2 t 00 00 00 2014 October , 3.Mango 436226 484781 663468 528158 November, March, 2012, 2013, 3 April 2014 2012, 2013, 4.Papaya 8715 6853 9526 8365 Whole year 4 2014 October, 2012, 2013, 5. Sapota 6112 4873 4526 5170 5 November 2014 VEGET ABLES 1. Beans July, August 922 1985 1454 1 & Mutter September 2002, 2003 April , May, 2. Okra 5093 5673 5383 September, 2 October 2002, 2003 September, 3. Brinjal 10945 7392 10537 9625 October, Febuary, 2012, 2013, 3 March 2014 October, 4.Cabbag 3317 2891 382 2197 November,March, 2012, 2013, e 4 April 2014 October , 5.Tomat 20554 16380 13601 16845 November, March, 2012, 2013, o 5 April 2014 District: West Godavari FRUITS 2010, 2011, 1.Banana 212967 348290 205367 255541 Whole year 1 2013 2.Coconu 405213 448904 718991 2012, 2013, 524369333 Whole year 2 t 000 000 000 2014 190 October, 2012, 2013, 3.Lemon 30138 26829 29885 28951 3 November 2014 October , 4.Mango 101832 62600 104022 89485 November, March, 2012, 2013, 4 April 2014 2012, 2013, 5.Papaya 33442 32012 18034 27829 Whole year 5 2014 VEGET ABLES 1. Beans July, August 2212 1615 1914 1 & Mutter September 2002, 2003 April , May, 2.Okra 1511 1667 1589 September, 2 October 2002, 2003, September, 3.Brinjal 16736 7663 17276 13892 October, Febuary, 2012, 2013, 3 March 2014 2011, 2012, 4.Tapioca 840 1528 1381 1250 Whole year 4 2013 October , 5.Tomat 5388 6349 6083 5940 November, March, 2012, 2013, o 5 April 2014 District: East Godavari FRUITS 2010, 2011, 1.Banana 392291 302514 153871 282892 Whole year 1 2013 2.Coconu 729965 720895 780162 2012, 2013, 743674000 Whole year 2 t 000 000 000 2014 October , 3.Mango 157496 162814 191027 170446 November, March, 2012, 2013, 3 April 2014 4 4.Orange 17315 15401 16358 Whole year 2002, 2003 2012, 2013, 5.Papaya 25164 26530 24011 25235 Whole year 5 2014 VEGET ABLES April , May, 1.Okra 13947 11486 12717 September, 1 October 2002, 2002 September, 2.Brinjal 29399 16447 15032 20293 October, Febuary, 2012, 2013, 2 March 2014 October , 3.Onion 10773 11463 10602 10946 November, March, 2012, 2013, 3 April 2014 2011, 2012, 4.Tapioca 124228 208870 191193 174764 Whole year 4 2013 191 October , 5.Tomat 15939 15245 14360 15181 November, March, 2012, 2013, o 5 April 2014 District: Visakhapatnam FRUITS 2010, 2011, 1.Banana 18459 22833 27661 22984 Whole year 1 2013 2.Coconu 998380 767210 160779 2012, 2013, 112446000 Whole year 2 t 00 00 000 2014 October , 3.Mango 127051 153771 37229 106017 November, March, 2012, 2013, 3 April 2014 2012, 2013, 4.Papaya 10349 9594 7164 9036 Whole year 4 2014 October, 2012, 2013, 5.Sapota 713 348 711 591 5 November 2014 VEGET ABLES 1.Beans July, August 8162 13135 10648.5 1 & Peas September 2002, 2003 September, 2.Brinjal 13638 9397 8150 10395 October, Febuary, 2012, 2013, 2 March 2014 October , 3.Onion 3757 4167 3558 3827 November, March, 2012, 2013, 3 April 2014 2011, 2012, 4.Tapioca 2251 2302 1639 2064 Whole year 4 2013 October , 5.Tomat 17949 19441 13210 16867 November, March, 2012, 2013, o 5 April 2014 District: Vizianagaram FRUITS 2010, 2011, 1.Banana 167277 133645 77367 126096 Whole year 1 2013 2.Coconu 435520 435920 141900 2012, 2013, 33778000 Whole year 2 t 00 00 00 2014 October , 3.Mango 100555 120554 131536 117548 November, March, 2012, 2013, 3 April 2014 2012, 2013, 4.Papaya 41606 28540 59961 43369 Whole year 4 2014 October, 2012, 2013, 5.Sapota 1690 1204 1096 1330 5 November 2014 VEGET ABLES 192 April , May, 1.Okra 3243 3898 3571 September, 1 October 2002, 2003 September, 2.Brinjal 7460 6076 8087 7208 October, Febuary, 2012, 2013, 2 March 2014 October , 3.Cabbag 4950 3097 1910 3319 November, March, 2012, 2013, e 3 April 2014 October , 4.Onion 6300 4695 4967 5321 November, March, 2012, 2013, 4 April 2014 October , 5.Tomat 12624 12007 12355 12329 November, March, 2012, 2013, o 5 April 2014 District: Srikakulam FRUITS 2010, 2011, 1.Banana 21745 54319 47549 41204 Whole year 1 2012 2.Coconu 306951 249859 328149 2012, 2013, 294986333 Whole year 2 t 000 000 000 2014 October , 3.Mango 115045 57047 57731 76608 November, March, 2012, 2013, 3 April 2014 2012, 2013, 4.Papaya 4902 4405 4015 4441 Whole year 4 2014 October, 2012, 2013, 5.Sapota 436 310 192 313 5 November 2014 VEGET ABLES April , May, 1.Okra 1911 1901 1906 September, 2010, 2011, 1 October 2012 September, 2.Brinjal 8313 5805 8568 7562 October, Febuary, 2012, 2013, 2 March 2014 October , 3.Cabbag 5713 4073 2337 4041 November, March, 2012, 2013, e 3 April 2014 October , 4.Onion 21064 18446 11398 16969 November, March, 2012, 2013, 4 April 2014 October , 5.Tomat 6861 8445 10131 8479 November, March, 2012, 2013, o 5 April 2014 193 Appendix 15: Perishability of Fruits and Vegetables Table 121 Perishability of Fruits Source: UCDAVIS* S.NO. FRUITS Reefer/Cold Chain Minimum (in Maximum (in Average no of Available no. of days for days) days) days(a) logistics (a-5) 1 Papaya 7 21 14 9 2 Date 180 360 270 265 3 Mango 14 21 17 12 4 Pomegranate 60 90 75 70 5 Banana 7 28 17 12 6 Sapota 14 14 14 9 7 Indian Jujube 28 14 9 8 Grapes 30 180 105 100 9 Coconut 30 60 45 40 10 Pineapple 14 28 21 16 11 Jackfruit 14 28 21 16 Pomefruit 12 (apple/pear) 30 60 45 40 13 Guava 14 21 17 12 14 Watermelon 14 21 17 12 15 Lemon 14 14 14 9 Table 122 Perishability of Vegetables Source: UCDAVIS S.No. VEGETABLES Reefer/Cold Chain Average no of Available no. of days for (in days) Minimum Maximum days(a) logistics (a-5) 1 Onion 30 240 135 130 2 Tomato 14 35 24 19 3 Brinjal/eggplant 7 14 10 5 4 Cabbage 21 42 31 26 5 Ladyfinger/okra 7 10 8 3 6 Cucurbits 10 14 12 7 7 Cauliflower 21 28 24 19 8 Tapioca Data not available 9 Drumstick Data not available 10 Ginger 180 180 180 175 11 Sweet potato 120 210 165 160 194 Appendix 16: Oversupply calculations for each of the selected CEZs. Table 123 OVERSUPPLY TABLE- (VEGETABLES & FRUITS) IN CEZ - KUTCH Production Consumption formula Harvest Production Population result S.No. Vegetable/Fruit kg/month/person (kgs)/month/person for over Oversupply Months (in kgs.) (2011) (2b) (a) (b) (2011-12) supply 1 Banana 12 21300000 2,092,371 0.85 1.07 2.14 No 2 Onion 8 61233330 2,092,371 3.66 0.95 1.90 Yes 3 Potato 3 4766670 2,092,371 0.76 1.61 3.22 No a>=2b 4 Tomato 4 25692000 2,092,371 3.07 0.81 1.61 Yes 5 Date 4 102778000 2,092,371 12.28 0.02 0.03 Yes 6 Papaya 12 174601000 2,092,371 6.95 0.08 0.16 yes 7 Mango 3 50998000 2,092,371 8.12 0.20 0.40 yes 8 Cabbage 2 8715000 2,092,371 2.08 0.271 0.542 Yes Table 124 OVERSUPPLY TABLE- (VEGETABLES & FRUITS) IN CEZ - SURYAPUR Production Consumption formula Harvest Production Population result S.No. Vegetable/Fruit kg/month/person (kgs)/month/person for over Oversupply Months (in kgs) (2011) (2b) (a) (b) (2011-12) supply 1 Mango 3 299568000 30,219 3304.41 0.20 0.40 Yes 2 Banana 12 1710119000 30,219 4715.90 1.07 2.14 Yes a>=2b 3 Papaya 12 101240000 30,219 279.18 0.08 0.16 Yes 4 Tomato 4 61629000 30,219 509.85 0.81 1.61 Yes 5 Cauliflower 2 22917000 30,219 379.18 0.33 0.65 Yes Table 125 OVERSUPPLY TABLE- (VEGETABLES & FRUITS) IN CEZ - NORTH KONKAN Production Consumption formula Harvest Production Population result S.No. Vegetable/Fruit kg/month/person (kgs)/month/person for over Oversupply Months (in kgs) (2011) (2b) (a) (b) (2011-12) supply 1 Grapes 3 449061000 32,316,354 4.63 0.08 0.17 Yes a>=2b 2 Banana 12 75223000 32,316,354 0.19 1.07 2.14 No 195 3 Mango 3 47053000 32,316,354 0.49 0.20 0.40 Yes 4 Onion 3 2111549000 32,316,354 21.78 0.95 1.90 Yes 5 Tomato 3 219718000 32,316,354 2.27 0.81 1.61 Yes Table 126 OVERSUPPLY TABLE- (VEGETABLES & FRUITS) IN CEZ - MALABAR Production Consumption formula Harvest Production (in Population result S.No. Vegetable/Fruit kg/month/ (kgs)/month/person for over Oversupply Months kgs) (2011) (2b) person (a) (b) (2011-12) supply 1 Banana 12 541406000 11,346,979 3.98 1.07 2.14 Yes 2 Coconut 12 1438666667000.00 11,346,979 10565.71 0.76 1.51 Yes 3 Mango 2 121886000 11,346,979 5.37 0.20 0.40 Yes 4 Papaya 4 27916000 11,346,979 0.62 0.08 a>=2b 0.16 Yes 5 Pineapple 4 60578000 11,346,979 1.33 0.03 0.05 Yes 6 Ginger 3 1543000 11,346,979 0.05 0.07 0.15 No 7 Sweet Potato 4 220000 11,346,979 0.00 0.01 0.02 No Table 127 OVERSUPPLY TABLE- (VEGETABLES & FRUITS) IN CEZ - MANNAR Production Consumption formula Harvest Production (in Population result S.No. Vegetable/Fruit kg/month/person (kgs)/month/person for over Oversupply Months kgs) (2011) (2b) (a) (b) (2011-12) supply 1 Coconut 4 243798567000.00 6,697,783 9099.97 0.76 1.51 Yes 2 Banana 12 413495000 6,697,783 5.14 1.07 2.14 Yes 3 Mango 4 23150000 6,697,783 0.86 0.20 0.40 Yes 4 Jackfruit 12 8144000 6,697,783 0.10 0.01 a>=2b 0.02 Yes 5 Pomefruits 3 2287000 6,697,783 0.11 0.20 0.39 No 6 Onion 2 20155000 6,697,783 1.50 0.95 1.90 No 7 Tomato 2 6931000 6,697,783 0.52 0.81 1.61 No Table 128 OVERSUPPLY TABLE- (VEGETABLES & FRUITS) IN CEZ - VCIC SOUTH Consumption Production formula Vegetable/ Harvest Production Population (kgs)/month/ result S.No. kg/month/ for over Oversupply Fruit Months (in kgs) (2011) person (b) (2b) person (a) supply (2011-12) 196 1 Coconut 4 25849401000 12373088 522.29 0.76 1.51 Yes 2 Banana 12 143682000 12373088 0.97 1.07 2.14 No 3 Mango 4 597601000 12373088 12.07 0.20 0.40 Yes a>=2b 4 Guava 6 3326000 12373088 0.04 0.09 0.18 No Sweet 12373088 5 Potato 2 260000 0.01 0.01 0.02 No 6 Tomato 2 513000 12373088 0.02 0.81 1.61 No Table 129 OVERSUPPLY TABLE- (VEGETABLES & FRUITS) IN CEZ - VCIC NORTH Consumption Production formula Vegetable/ Harvest Production (in Population (kgs)/month/ result S.No. kg/month/ for over Oversupply Fruit Months kgs) (2011) person (b) (2b) person (a) supply (2011-12) 1 Coconut 4 2051347333000.00 27,834,650 18424.40 0.76 1.51 Yes 2 Banana 12 960437000 27,834,650 2.88 1.07 2.14 Yes 3 Mango 4 1778262000 27,834,650 15.97 0.20 0.40 Yes a>=2b 4 Papaya 12 144375000 27,834,650 0.43 0.08 0.16 Yes 5 Lemon 3 65392000 27,834,650 0.78 0.12 0.25 Yes 6 Onion 2 38222000 27,834,650 0.69 0.95 1.90 No 7 Tomato 2 100626000 27,834,650 1.81 0.81 1.61 Yes 197 Appendix 17: Average monthly prices derived across 12 months from three most recent years for viable CEZs. Table 130 CEZ VCIC North (Visakhapatnam) – CEZ Suryapur (Vadodara): Banana CEZ VCIC North Visakhapatnam Banana In Rs/Quintal Month 2014 2015 2016 AVG JAN 123.95 123.95 FEB 113.69 113.69 MAR 162.78 110 136.39 APR 118.76 118.76 MAY 128.76 128.76 JUN 1060 1060 JUL 220 930 575 AUG 190.57 190.57 SEP 190 170 180 OCT 180 150.51 153.09 161.2 NOV 113.87 157.55 135.71 DEC 144.89 144.89 2878.35 Avg price 239.86 Table 131 CEZ Suryapur Vadodara Banana In Rs/Quintal In Rs/Quintal Month 2014 2015 2016 AVG Difference in Price JAN 850 1876.08 1300 1342.03 1218.08 FEB 850 1250.17 1261.69 1120.62 1006.93 MAR 823.77 1317.34 1070.56 934.17 APR 863.14 1436.16 1149.65 1030.89 MAY 825.39 1405.55 1151.69 1127.54 998.78 JUN 841.87 906.06 1096.74 948.22 111.78 JUL 939.1 1404.68 1166.92 1170.23 595.23 AUG 920.01 1198.54 1196.02 1104.86 914.29 SEP 790.61 991.09 1185.59 989.1 809.1 OCT 808.51 1176.55 1192.59 1059.22 898.02 NOV 1066.24 1366.72 1427.96 1286.97 1151.26 DEC 1336.43 1346.43 1404.74 1362.53 1217.64 10886.17 198 Avg Price 907.18 Table 132 CEZ VCIC North (Visakhapatnam) – CEZ Suryapur (Vadodara): Tomato CEZ VCIC North Visakhapatnam Tomato In Rs/Quintal Month 2014 2015 2016 AVG JAN 820.96 820.96 FEB MAR 850 850 APR 850 850 MAY 1088.71 1088.71 JUN 1419.55 1419.55 JUL 1056.81 1056.81 AUG 678.84 678.84 SEP 802.3 802.3 OCT 806 806 NOV 666 666 DEC 958.91 800 879.46 9918.63 Avg Price 901.69 Table 133 CEZ Suryapur Vadodara Tomato In Rs/Quintal In Rs/Quintal Month 2014 2015 2016 AVG Difference in Price JAN 744.74 1337.8 726.61 936.38 115.42 FEB 426.24 1157.4 741.49 775.04 775.04 MAR 366.23 1120.64 531.62 672.83 177.17 APR 491.7 1135.49 722.47 783.22 66.78 MAY 825.39 1390.08 1744 1319.82 231.11 JUN 841.87 1520.97 3337.7 1900.18 480.63 JUL 939.1 1840.86 3160.7 1980.22 923.41 AUG 920.01 1119.79 1264.45 1101.42 422.58 SEP 790.61 1113.13 817.7 907.15 104.85 OCT 808.51 1232.28 657.73 899.51 93.51 NOV 1066.24 2355.85 810.27 1410.79 744.79 DEC 1336.43 1079.94 456.3 957.56 78.1 4213.39 Avg Price 351.12 CEZ Malabar (Coimbatore) - CEZ Suryapur (Surat): Coconut 199 Table 134 CEZ Malabar Coimbatore Coconut In Rs/Quintal Month 2014 2015 2016 AVG JAN 686.68 1342.01 1021.31 1016.67 FEB 934.08 1374.3 1018.64 1109.01 MAR 878.98 1350.33 1024.82 1084.71 APR 923.28 1393.43 1012.27 1109.66 MAY 899.55 1392.93 1317.51 1203.33 JUN 864.02 1408.38 1250.61 1174.34 JUL 851.39 1319.36 1414.71 1195.15 AUG 852.22 1400 1400 1217.41 SEP 869.33 1400 1405.12 1224.82 OCT 1241.6 1404.03 1322.82 NOV 655.83 1689.6 1172.72 DEC 989.5 1093.72 1737.47 1273.56 14104.2 Avg Price 1175.35 Table 135 CEZ Suryapur Surat Coconut In Rs/Quintal In Rs/Quintal Month 2007 2008 2010 AVG Difference in Price JAN 17500 17500 16483.33 FEB MAR 15000 15000 13915.29 APR 15998.55 15998.55 14888.89 MAY 4380.24 4380.24 3176.91 JUN JUL 21749.21 21749.21 20554.06 AUG 22500 22500 21282.59 SEP OCT 20892.57 20892.57 19569.75 NOV 19276.93 19276.93 18104.21 DEC 127975.03 Avg Price 15996.88 200 CEZ Mannar (Madurai) – CEZ Suryapur (Surat): Coconut Table 136 CEZ Mannar Madurai Coconut In Rs/Quintal Month 2002 2004 2005 AVG JAN FEB MAR 5601.89 7000 6300.95 APR 6283.33 6250 6266.67 MAY 5487.5 5700 5593.75 JUN 6058.33 5619.05 5838.69 JUL 6025 5250 5637.5 AUG 6250 5290 5770 SEP 6333.52 5000 5666.76 OCT 6375.03 6375.03 NOV 415 6874.91 3644.96 DEC 2007.5 6687.5 4347.5 55441.81 Avg Price 5544.18 Table 137 CEZ Suryapur Surat Coconut In Rs/Quintal In Rs/Quintal Month 2007 2008 2010 AVG Difference in Price JAN 17500 17500 FEB MAR 15000 15000 8699.05 APR 15998.55 15998.55 9731.88 MAY 4380.24 4380.24 -1213.51 JUN JUL 21749.21 21749.21 16111.71 AUG 22500 22500 16730 SEP 201 OCT 20892.57 20892.57 14517.54 NOV 19276.93 19276.93 15631.97 DEC 80208.64 Avg Price 11458.38 202 Appendix 18: Wholesale market rates for Coconut in CEZ Suryapur Interview no.: 017072017 Ref.: page 79 Interviewer: Anjali Anit Interviewee: Mr Parekh, Agricultural Produce Market Committee-Ahmedabad Date: 12th July 2017 | Time: 12:45 pm Q What is the current wholesale price of coconut in the APMC market? A The current rate for coconuts is 24 INR per piece. 203 Appendix 19: Total cost of transport from Farm in Malabar to Buyer in Suryapur via short sea shipping Table 138 Amount A. First Mile cost Pre-cooling cost (Plug-in charges) 3500 Transportation cost (Empty container to farm and loaded container) 30000 Sorting and loading at farm Port THC, Port Custom and other documentation at Kochi port 12980 Shipping Freight Cost 81900 Lift on – Lift off charges (Shipping charges) 2000 B. Last Mile Cost Plug-in charges at Mundra port 3500 Port THC, Port Customs and other documentation at Mundra port 12980 Transportation cost for container delivery to Rajkot 40000 Total 186860 204 Appendix 20: Capacity 40-ft Cargo Container for Coconut Interview no.: 012072017 Ref.: page 32, para 2 / line 3 Interviewer: Anjali Anit Interviewee: P. Deshmukh (SCA Logistics Pvt. Ltd.) Mumbai Date: 12th July 2017 Time: 2:30 pm Q – How many tons of coconut can be carried in a 40-ft cargo container? A – The maximum payload (capacity) of a 40-ft REF container is 26,240 kg, but Coconuts don’t cube out and so the most quantity that can fit is about 19 MT cargo in the 40-ft REF due to the shape as well as packaging. In the 40-ft container category, there are two types of trailers available in the market in which cargo may be carried. Table 139 Trailer Type 3519 4019 Carrying Capacity MT 19 25 205 Appendix 21: Pilot design The pilot design as described in section 4.4 is illustrated by using coconut as an example product. Product preparation For example, coconuts are de-husked at farm level to reduce the transport weight and volume. The outer coloured skin (exocarp) plus the fibrous inner husk (mesocarp) is stripped away by striking the coconut against a sharp-pointed metal stake mounted on a platform. A few impaling strokes loosen the husk, making it easier to be removed. Machete can also be used to start the de- husking process. De-husked coconuts are oval to round in shape with the eyes showing. The discarded husks can be placed several layers deep over the de-husked coconuts to help reduce desiccation. Moreover, they can be reused as raw material for making pallets. Grading Select products that are of sufficient and uniform quality. This is also done at farm level. Take into account that there will be some quality decay during transport, implying ‘sufficient’ to be higher than the market player requirements. For example, fresh de-husked coconut is expected to be brown, free from damage, cracking, and sunken eyes and attain the required size specifications. There should not be noticeable blemishes or skin damage from insects, diseases, or physical injury. Coconuts should be free of stress cracks and not have any protruding germination tubes, leakage of water around the eyes, or surface mould. When shaken, the fruit should have a sloshing sound, indicating the presence of water in the coconut. Any fruit that does not have a sloshing sound when shaken should not be packed for market. Losses in coconut are mainly as a result of cracking due to poor handling and inappropriate storage and transport condition. Spoilage can occur from softening and disease infection of the eyes. Eventually different classes are transported if there is a market for them. After grading the coconuts are transferred to a nearby collection point. The planning of the converging flows of coconuts to this collection point is adapted to the logistic scheme of the shipping route and the port handling and throughput time. In case the coconuts arrive in significantly different time-intervals, storage or even cold storage might be necessary. The required volume is one or two 40 ft. containers (or TEU?). One 40 ft. container can carry around 22.5 MT of fresh coconuts. Pre-cooling Temperature control for long term shipment is essential. For some agricultural products field heat must be removed (precooling) before they are loaded into the container. This will be researched before the preparation of the pilot. 206 Packaging For example, coconuts are transported in containers, first on land, then at sea and then again on land. Coconuts may be sold in bulk or packed in large synthetic or mesh sacks of known fruit count per sack. Stackable cartons boxes are also used albeit for export mainly. Figure 19: some examples of coconut packaging (carton boxes, bags with fixed number of coconuts) Ventilation is important for coconuts; hence the packaging should allow for air flow to pass through. Two scenarios are the most likely in India. Either products are packed in bags with about 25 fresh coconuts or they are high end, and stackable carton boxes are used, causing less physical damage. In the first case the bags are carried into the container piece by piece by workers, and most likely the coconuts are sold loose in some outlet (or no consumer nor sales packaging is required, e.g. in case of the out of home sector). In the second case boxes can be stacked and put on pallets. Using a forklift reduces the loading time tremendously. Note that either way a lot of material is required to load the coconuts in the container in such a way that the product stays in place (e.g. tie wraps), the packaging carries the correct information and market player requirements are satisfied. Loading It should be decided which type of load carrier is used. In case pallets are used: a) Put all cartons on a pallet after packing b) Make vertical stacks and make sure that ventilation holes inside the cartons match with the cartons placed above and under c) Reinforce the cartons at the base of the pallet. Strap the whole pallet to make stable stacks d) Strap cartons on pallet e) Maintain low temperature once the pallet is pre-cooled Coconuts can be shipped successfully by sea in reefer or dry containers for up to three weeks. A refrigerated container is recommended for the transport of fresh coconut. A storage temperature of 12ºC (to be checked) will assist in quality maintenance. 207 Cargo Handling of cargo is another important factor in the chain. Coconut requires cool, dry and good ventilation. In damp weather (rain, snow), the cargo must be protected from moisture, since it may lead to mould, spoilage and self-heating as result of increased respiratory activity. No hooks should be used with bagged cargo, so as to prevent damage to the bags and loss of volume. In order to guarantee safe transport, the bags must be sowed and secured in a way that they cannot slip or shift during transport. Coconut packs can be segregated with fibre rope or/and thin fibre nets. Attention must also be paid to storage patterns which may be required as a result of special considerations, such as ventilation measures. Depending on the scenario the container will be loaded. Ventilation in reefer containers is possible only in case where big holes are available in below and/or above the packaging of the product. This is not the case if bags are stacked loose in the container. In case of pallets, the cold air enters from beneath the load. This airflow is directed through or alongside the cartons to the upper part of the container where it is directed back to the cooling equipment. In order to ensure proper temperature control in the whole load, appropriate stacking of the pallets is essential: a) Prior loading, inspect the empty container in order to remove any foreign objects b) Check if the 4 drain holes are open c) IF the container is loaded on a dock where temperature is controlled: i. YES: the empty container can be pre-cooled prior to loading. Run the container empty at temperature set-point for at least 3 hours with doors closed ii. NO: the empty container must NOT be pre-cooled d) Load of the container (cooling unit OFF) e) Loading of the container should be done within one hour f) Place the pallets inside the container in such a way to avoid any chimneys between the pallets g) Fill up the complete container with pallets, do not leave any open spaces on the t-bar floor as it will create “false air”, i.e. short-cuts air circulation between supply and return airflow h) Do not load above the red load line (to allow air flow; check of such a line is available) i) Cover the T-bar and pallet end conform pictures 208 Figure 20: examples of covering the T-floor at the doors to avoid air return through open area j) After closing the doors, the cooling unit can be switched ON k) The power supply of the reefer container should be assured by a Genset (Generator for power) or other power supply until loading onto the vessel in the port of Kochi. The recommended set points for container transport of coconuts on the road or at sea are: a) Temperature set-point: 12°C (54°F) b) Ventilation set-point: to be researched c) Drain holes open d) Humidity control: between 80-90% (if available) e) Defrost cycle: to be researched Transportation Transportation consists of 1. Inland transport 1: Transport from collection point to port is done at agreed set point conditions in either scenario. 2. Port 1: At the port the container is stacked and connected to power to maintain the conditions. Port procedures take place, like paper work, eventually some control. All this needs to be prepared with the stakeholders in the port. These could be logistic service providers at the port to load and unload, state government and the shipping company. Arrival at the port is planned in such a way that procedures are finished when the boat is leaving 209 3. Short sea shipping 4. Port 2: Similar to port 1. Planning of truck transport is optimized in relation to throughput time in port. 5. Inland transport 2: Transport at set point conditions from the port of Mundra to the drop off location(s) of the market players. Unloading at drop off point(s) market players A check is done by the market player on the load, according to the predefined requirements. 210 Appendix 22: Completed Market Player Questionnaire - 1 1. Buyer name- Sameer 2. Location(s) (city name)- Surat 3.Designation of interviewee- Owner 4. What kind of produce do you sell (or process)? a. Tender coconut b. Brown coconut with hard shell Ans. A. Tender coconut quantity 5. What is the size of produce do you sell (or process)? a. Small Coconut b. Medium Coconut c. Large Coconut Ans. B. Medium Coconut 6. what period of the year are you sourcing this produce? a. Year around b. June-July c. December-January Ans. 4 months (Aug-Nov – festival season in Gujarat) 7.From what type of stakeholder and where are you buying this produce? a. Farmer (region) b. Wholesaler (region) c. Trader (region) Ans. Bought from trader (this is an agent who sorts quality at preliminary level). 8. Do you have some kind of agreement with the supplier? a. yes b. no Ans. No 9. Does the agreement contain product requirements? a. yes (please specify like Green coconut, brown ripe coconut, no damage, Size) b.no Ans. Yes. Green coconut 10. What is the kind of price agreement with the supplier? Ans. No agreement. 211 11. Is there an agreement on the amount per week? a. yes b.no Ans. No such agreement. 12.What is the period of the agreement? a.1 year b. Monthly c. Weekly Ans. Not applicable. 13. Is it a written or oral agreement? a. written b. oral Ans. Not applicable. 14. On average how often in a week is there a delivery of produce? a. Once a week b.3 days a week c. 6-7 days a week (Daily) Ans. A. Once a week. 15. what is the way of transport used? a. Open truck b. Covered truck c. Tempo d. Part load truck Ans. C. Tempo 16.Transportation cost bared by? a. supplier b. buyer Ans. Buyer 17. How produce is packed? a. particular packaging b. Gunny bag c. loose? Ans. B. Gunny bags. 212 18. What happens if the produce is not accepted? a. Return back to supplier b. Sale it by supplier in local market Ans. Sold at discount. 19.what do you consider as the most criterion important points with respect to the supplier and in what order? a. constant satisfactory quality b. niche product c. Price /Quality d. delivery reliability e. high quality Ans. E. High Quality 20. what is agreed about the payment/how is the payment organized? a. Immediate cash b. Cash within 7 days c. Cash after 15 days d. Online within 7 days e. Online payment after 15 days f. Advance Payment Ans. Depends on the payment terms of supplier. 21.What is the current price in your shop for coconuts and how do you determine the price? Ans. Approximately 14 INR per pc, margin is of 0.30 paisa. 213 Appendix 23: Completed Market Player Questionnaire – 2 Table 140 Company name FarmFresh Function interviewee AGM Sales ad manager Question Answer Comment what kind of coconuts do you sell? Tender coconut / Brown coconut specify as used in market what period of the year are you sourcing these coconuts? variety 1: … year round - winter tender goes down a bit e.g. year-round or September-December variety 2: … what kind of stakeholders you source from? Farmer directly e.g. middlemen or farmers directly from which region and what period of the year are you sourcing coconuts? South region / year around variety 1: … e.g. Tamil Nadu (Sept-Jan) variety 2: … … Do you have some kind of agreement with the stakeholders you source the coconuts from? no Tender coconut - Category. Grade A - water content - 300-400 ml, Quality Spec - 0% to 10%scar & rest green, e.g. are there some kind of quality aspects Un-bunched pieces - max 20% involved Tender coconut - Category. Grade B - water content - Do you use product requirements when you 300-400 ml, Quality Spec - 10% to 20% scar & rest green, buy? Un-bunched pieces - max 25% What is your sales volume per week or per Is there an order on volume or an agreed month? Different per season? 5000 pcs * (8-10 loads) per month standard amount? From here the question are per sourcing area/supplier. If he has more than one supplier some answers might differ and should be registered How is the price agreement process with the Is it a fixed price for some time, or relative supplier? Do you or the supplier use to wholesale market price of the day before reference prices? Market price for every deal, ... Mumbai price Category A - Rs.16-18 /pcs yes, at winter season price goes down What is the price range for coconuts when you buy? Are there seasonal variations? Mumbai price Category B - Rs. 14 -16/pcs what does the supply chain look like? e.g. farmer (de-husking, sorting, pack in Describe the stakeholders and what activity plastic bags) - middleman (loading, transport takes place where? in small van collected from farmers to LSP, unloading) - logistic service provider (loading container for uncooled long At farm - Sorting /grading /quality check / loading distance transport, transport to market) - ... Logistics if there are more varieties involved this a. On average how often in a month is there might vary per variety a delivery of coconuts? weekly shipment for tender and bi-weekly for semi husked cooled truck, open truck, covered truck (will vary over the different parts of the supply b. Is there temperature control? open truck - chain) c. what happens if the order is not accepted or part of it is bad? Who is responsible and lower payment, only good part is accepted, pays for it? farmer totally rejected? d. what is the lead time from ordering to really getting it? 10 - 15 days 214 e. are the coconuts transported in crates, a particular kind of packaging or lose? This might differ per part in the supply chain. pp bags/jute what share (estimate as % of supply) of the coconuts that arrive are not meeting the market requirements and can only be sold at a much lower price or become waste? what is the main cause for these losses you think? quality at source itself. No so much in transit what do you consider as the two most important points with respect to the supplier and in what order? better sorting and grading at source e.g. price, supply reliability what is agreed about the payment/how is the payment organised? 7 to 10 days e.g. is payment cash or after 8 days by bank If there is one improvement in the supply e.g. better packaging to decrease mechanical chain you can apply, what would it be? quality and Faster transit time damage Anything you would like to add? Complimentary -350 pcs 215 Appendix 24: Completed Market Player Questionnaire - 3 1. Buyer name- GO2FRESH 2. Location(s) (city name)- New Mumbai 3. What kind of produce do you sell (or process)? a. Tender coconut b. Brown coconut with hard shell Ans. A. Tender coconut and B. Brown coconut 4. Do you use product requirements when you buy? a. 300-400 ml water content b. Green natural in colour & Tender c. No empty /dry nuts Ans.A. 300-400 ml water content 5. what period of the year are you sourcing this produce? a. Year around b. June-July c. December-January Ans. A .year round - winter tender goes down a bit 6. From what type of stakeholder and where are you buying this produce? a. Farmer (region) b. Wholesaler (region) c. Trader (region) Ans. Aggregator( South region ) 7. Do you have some kind of agreement with the supplier? a. yes b. no Ans. N.A 8. Does the agreement contain product requirements? a. yes (please specify like Green coconut, brown ripe coconut, no damage, Size ) b.no Ans. N.A 9. What is the kind of price agreement with the supplier? Ans. N.A 216 10. Is there an agreement on the amount per week? a. yes b.no Ans. N.A 11. What is the period of the agreement? a.1 year b. Monthly c. Weekly Ans. N.A 12. Is it a written or oral agreement? a. written b. oral Ans. N.A 13. On average how often in a week is there a delivery of produce? a.Once a week b.3 days a week c. 6-7 days a week (Daily) Ans. A. Once a week. 14. what is the way of transport used? a.Open truck b.Covered truck c.Tempo d. Part load truck Ans. Open truck 15. Transportation cost bared by? a. supplier b. buyer Ans. N.A 16. How produce is packed? a. particular packaging b.Gunny bag c. loose? Ans. N.A 17. What happens if the produce is not accepted? a. Return back to supplier b. Sale it by supplier in local market 217 Ans. N.A 18. what do you consider as the most criterion important points with respect to the supplier and in what order? a. constant satisfactory quality b. niche product c. Price /Quality d. delivery reliability e. high quality Ans. N.A 19. what is agreed about the payment/how is the payment organized? a. Immediate cash b. Cash within 7 days c. Cash after 15 days d.Online within 7 days e. Online payment after 15 days f. Advance Payment Ans. Online payment after 15 days 218 Appendix 25: Farm Aggregator Questionnaire 1. Name – AAR 2. What way can he be reached if client wants to order something? Ans. Clients call me directly for any requirements. 3. Total land holding (Acre) – under farming? Ans. I have 8 acres of land holding. 4. Area under farming (Acre) for coconut produce? Ans. About 5 acres of my farm land is dedicated to coconut farming. 5. How many variety of coconut produce does the farmer grow? A. Fresh Green Coconut B. Brown Ripe Coconut Ans. I grow the brown ripe coconut. 6. What kind of Coconut do they produce? A. Tender Coconut (Only Water) B. Tender Coconut (Thin Layer of malai with water) C. Tender Coconut (Thick Layer of malai with water) D. Brown Ripe Coconut Ans. I produce the brown ripe coconut. 7. Who is buying from them? A. Wholesaler B. APMC C. Retailer D. Local trader E. Online Retailer F. Supermarket G. Food processing Unit H. Middle Man Ans. A local trader comes to buy from my farm. 8. Is there any buyer who has an agreement with him and what is the agreement about? Is there some kind of preference (quality, size, variety, ...) or requirement for some buyer? Ans. There are no requirements defined or agreements made orally or in writing. 9. Where does the transaction take place? A. On farm 219 B. APMC C. Collection center D. Local Wholesale market E. local Retailer Ans. The transaction happens on the farm itself. 10. How is the payment arranged? A. Immediate cash B. Cash within 7 days C. Cash after 15 days D. Online within 7 days E. Online payment after 15 days. F. Advance payment Ans. Payment is made immediately in cash. 11. Is the farmer capable/willing to wait for the payment for one week, since the produce arrives at the buyer about one week later? Ans. No, I can get cash immediately, why must I wait whole week? 12. Farmer is having written agreement with current buyer(s)? Ans. I have no agreements with my current buyer 13. How do they determine the price for sale? A. By prevalent market price B. Recover cost C. Earn some profit D. Market demand at the time of harvest – 1. Stable 2. Fluctuating 3. Increasing 4. Decreasing Ans. Sale price depends on the market price, which is recorded in the newspapers every day; I also cross check it with buyers and farm owner friends. 14. What is the produce quality at the time of harvest? A. No damaged spots B. Size C. Semi ripen D. Close to ripening E. Ripened Ans. Since coconuts are largely produced for production of coconut oil, coconut milk, as food ingredient, I sell only the ripened brown variety. 220 15. Does the farmer know where the produce is going to? A. If Yes, then where? B. No Ans. Yes usually it goes to the nearest market within my state; sometimes it also goes to Tamil Nadu. 16. Does the farmer send a produce out of state? Ans. Yes some of my produce also goes to Tamil Nadu. 17. How are the produce transported from the farm to point of delivery? A. Open truck B. Covered truck C. Tempo D. Tractor E. Part truck/ tempo F. own transport (Please specify) g. If not by road? Please specify the mode. Ans. The produce is generally transported in open trucks. 18. What is the transport costs in INR per kg.? (if the farmer can’t specify then ask how they transport, how much do they transport in kg terms and then ask how much they paid for it in total and arrive at the result) A.Cost per km B.Cost as per vehicle C. Cost per bag of ... kg Ans. I don’t pay for transport. 19. Who is arranging for transport? A. Farmer B. Buyer C. Middleman Ans. Buyer arranges transport. 20. Who is paying for the transport cost? A. Farmer B. Buyer C. Middleman 221 Ans. Buyer bears cost of transport. 21. What months of the year is the farmer harvesting? Ans. The trees bear fruit all year round, which takes 60 days to ripen. I therefore harvest every 60 days. 22. How much was the average yield (kg/acre) in the last 3 years for coconut crop produced? Ans. 2014-15-16 were drought years, so the yield has been quite poor – about 20-30 kg per acre. However, we expect better results this year as the region has received ample rain. 23. Farmer provides 91/delivers produce to the buyer/market at what frequency? A. Once in 15 days B. Once a month C. One time only D. Once a week E. Twice a week F. Everyday Ans. I am able to deliver produce only every 60 days. However, bigger farmers end up harvesting and sending produce everyday as by the time they are done from one end to the other, fruits from the first belt of trees are ready for harvest. 24. Distance from farm to nearest market? A. less than 5 km B. 5-10 km C. 10-50 km D. More than 50 km Ans. My farm is about 1.5 km from the market, so less than 5 km. 25. How does the farmer sort / grad of produce? Ans. No sorting/ grading is done at the farm level. The trader opens and husks the full stock at the farm and discards any produce that is too small or rotten. This is generally no more than 0.05% of the total quantity. 26. Is there packaging for transport from farm to next link in the supply chain? Ans. No, the coconut is either put in gunny bags or sent lose in open trucks. In case it is collected 91 222 27. What kind of packaging: is it packed in A. Jute Bag B. Box C. Plastics bag D. Loose Ans. The coconut is at best packed in gunny bags, but usually sent lose in open trucks. 30. Who is arranging the packaging and doing the work? (farmer or buyer?) Ans. The buyer arranges for all the packaging and he is the one who packs and loads the produce as well. 223 Appendix 26: Measurements of the tender coconuts under test implementation Table 141 Coconuts -T(Farm A) Data External Internal colour Milk sheet Days appearance etc colour Taste Time 11:36 Dec-15 Day 1 1 1 1 2 A.M. Dec-19 Day 5 T1A1 1 1 1 1 10:57 T7P1 1 1 1 2 10:56 T4M1 1 1 1 1 10:55 Day Dec-26 12 T2A2 4 4 3 3 11:11 T5M2 1 1 1 1 11:10 T8P2 1 1 1 1 11:07 Day Jan-01 18 T3A3 4 4 3 4 11:39 T6M3 1 1 1 1 11:39 T9P3 2 2 1 2 11:40 Table 142 Coconuts -R(Farm B) External Internal colour Data sheet Days appearance etc Milk colour Taste Time 11:43 Dec-15 Day 1 2 1 1 2 A.M. Dec-19 Day 5 R1A1 2 2 1 2 11:15 R7P1 2 2 1 1 11:14 R4M1 2 2 1 1 11:13 Dec-26 Day 12 R2A2 3 3 3 3 11:24 R5M2 1 1 2 2 11:22 R8P2 1 1 2 2 11:19 Jan-01 Day 18 R3A3 3 3 3 3 11:45 R6M3 1 1 1 1 11:46 R9P3 2 1 1 2 11:47 224 Appendix 27: An Analytical Study on Agriculture in Kerala http://www.keralaagriculture.gov.in/pdf/a_s_06042016.pdf 225