agri-fresh produce supply chain management- a state-of-the-art literature review

Agri-fresh produce supply chainmanagement: a state-of-the-art

literature reviewManish Shukla and Sanjay Jharkharia

Quantitative Methods & Operations Management Area,Indian Institute of Management Kozhikode, Kozhikode, India

Abstract

Purpose – The purpose of this paper is to present a literature review of the fresh produce supplychain management (FSCM). FSCM includes the processes from the production to consumption of freshproduce (fruits, flowers and vegetables).

Design/methodology/approach – Literature review is done by systematically collecting theexisting literature over a period of 20 years (1989-2009) and classifying it on the basis of structuralattributes such as problem context, methodology and the product under consideration. The literature isalso categorized according to the geographic region and year of publication.

Findings – There is an increase in interest towards FSCM still there is an absence of a journal with theprime attention towards FSCM. The key finding of this review is that the main interest is towards consumersatisfaction and revenue maximization with post-harvest waste reduction being a secondary objective. It isrevealed from the review that most of the literature is fragmented and is in silos. Lack of demand forecasting,demand and supply mismatch, lesser integrated approach etc are the major causes of concerns.

Research limitations/implications – The authors have taken only the fresh produce (fruits,flowers and vegetables), authors may also look at other perishable items such as milk, meat, etc.

Practical implications – Result shows a product-problem-methodology mapping which may serveas a framework for the managers addressing issues in FSCM.

Originality/value – Most of the prior literature reviews are focused on a specific issue such asproduction planning or inventory management and ignore the broader perspective. There exists a needof having a detailed literature review covering all the operational issues in FSCM. This review fills thisgap in the FSCM literature.

Keywords Supply chain management, Literature review, Agri-fresh produce, Fruits, Vegetables

Paper type Literature review

1. IntroductionSupply chain management (SCM) may be defined as:

[. . .] a set of approaches utilized to efficiently integrate suppliers, manufacturers, warehouses,and stores, so that merchandise is produced and distributed at the right quantities, to theright locations, and at the right time, in order to minimize system-wide costs while satisfyingservice level requirements (Simchi-Levi et al., 2008).

Over the years, the definitions have changed and broadened the scope of SCM, but, thesedefinitions are still limited to manufactured products and services with little attentionbeing paid to agriculture. Agricultural produce constitutes a major part of the worldeconomy and is the raw material for many industries. Among the agricultural produce,

The current issue and full text archive of this journal is available at

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The authors would like to place on record their appreciation to three anonymous referees for theirvaluable suggestions, which have enhanced the quality of the paper over its earlier version.

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Received 26 May 2010Revised 12 November 201025 May 201130 September 201118 January 201214 March 2012Accepted 5 May 2012

International Journal of Operations &Production ManagementVol. 33 No. 2, 2013pp. 114-158q Emerald Group Publishing Limited0144-3577DOI 10.1108/01443571311295608

agri-fresh produce have got the least attention. The SCM of agri-fresh produce, hereinafter referred to as agri-fresh supply chain management (FSCM), constitutes theprocesses from production to delivery of the agri-fresh produce, i.e. from the farmer tothe customer. FSCM is complex as compared to other SCMs due to the perishable natureof the produce, high fluctuations in demand and prices, increasing consumer concernsfor food safety (Van der Vorst and Beulens, 2002), and dependence on climate conditions(Salin, 1998).

It is found that there is a huge amount of inconsistency and confusion in segmentingfruits, flowers and vegetables. Some of the authors grouped these within food products(Salin and Nayga, 2003; Faulin, 2003; Alfaro and Rabade, 2009) without giving specificattention to the product characteristics, whereas others define these as agri-food(Aramyan et al., 2007; Ahumada and Villalobos, 2009b), short shelf-life food products(Doganis et al., 2006), fresh produce (Fearne and Hughes, 1999; Zuurbier, 1999), perishables(Broekmeulen and Von Donselaar, 2009; Chen et al., 2009), deteriorating products(Lodree Jr and Uzochukwu, 2008), etc. Moreover, a few authors (Cadilhon et al., 2005;Bertail and Caillavet, 2008) have preferred to use the commonly accepted names such asfruits and vegetables. Others being more concentrated on a specific product discussedproducts such as oranges (Caixeta-Filho, 2006), grapes (Ferrer et al., 2008), lily-flower(Caixeta-Filho et al., 2002), etc. The research seems independent and orientedtowards problem solving rather theory developing. There seems a lack of an acceptednomenclature for defining the produce. This vacuum is mainly due to the inheritheterogeneity in the produce characteristics. In future, there is an acute need forcategorization of the produce to enhance the scalability of the developed models.

The objective of this paper is to address the major operational issues causing thepost-harvest waste in fruits, flowers and vegetables. Therefore, we define these asagri-fresh produce to clearly differentiate these from other agri-produce and non-agriproduce. Figure 1 shows a detailed differentiation of various products to enhance theunderstanding of agri-fresh produce.

1.1 Factors affecting the agri-fresh produce supply chainsThe main factors that have recently attracted the attention of researchers andpractitioners towards the FSCM include:

. globalization;

. technological innovations;

. trade agreements;

. consumer awareness; and

. environmental concerns, etc.

Increasing globalization has brought the inflow of capital, technology, and informationto enhance vertical integration in FSCM (Reardon and Barrett, 2000). Globalizationhas provided farmers altogether different market channels and facilities. It has helpedthe farmers to look beyond the traditional spot market and sell their produce in aglobal market at a competitive price. Globalization brought in funds which triggeredthe consolidation of the food organization (processers, retailers, etc.) and farms. Thisgave rise to funding in research and development, automation and development ofinnovative farm and processing practices. This in turn supported globalization and

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FSCM by the technological innovations in seeds, fertilizers, pesticides, and farmmachinery. These technological innovations have increased the yield and decreasedthe dependency on external labor and weather. The trade agreements such asGeneral Agreement on Tariffs and Trade (GATT), World Trade Organization (WTO),and North American Free Trade Agreement (NAFTA) reduced the trade barriers, andhence increased the competition and co-operation across borders (Reardon and Barrett,2000). Consumers demand for healthy, fresh, and cheap produce is another criticalfactor. Changing eating habits and increasing awareness towards food safety haveincreased the challenges for FSCM (Cook, 1990, 1999; Reardon and Barrett, 2000).Environmental concerns due to heavy use of fertilizers, pesticides and geneticallymodified seeds have also been expressed by several parts of the society (Cook, 1999;Pinstrup-Andersen, 2002). Recently, there is also an increasing consumer concernregarding food miles. “Food miles” is a term used to describe the total distance foodtravels to reach the consumer (Rajkumar, 2010). It has been found that in the USA,processed food travels around 1,300 miles and fresh produce travels around 1,500 mileson an average (Hill, 2008). Food miles are used as an indicator for the financial, social,and environmental impact of food transportation. Lesser food miles result in lesser fuelusage, lesser carbon emission, lesser pollution, lesser environmental degradation andlesser global warming (Rajkumar, 2010).

Another concern arises from the increased fuel price coupled with the purchasingpower of consumers. It is believed that with the increase in purchasing power, consumerswill prefer more animal protein and meat as part of their daily diet (De Boer and Pandey,1997). This trend in turn will increase the demand for animal feed and thus, willoverburden the already constrained agricultural land and water resources. On the otherhand, increasing fuel prices have a two-fold impact on agriculture. First, increasinginput costs such as transportation and refrigeration leads to higher consumer prices,

Figure 1.Product differentiation

Agricultural Produce Animals/ Birds and their produce(Milk, Eggs etc)

Long shelf life(Grains, Pulses, Spices etc)

Eatables

PerishableDurable

Products

Non-Eatables

Obsolete/Out datedDeteriorate/Decay

Processed Produce(Meals, Sauces etc)

Fresh Produce(Flowers, Fruits and Vegetables)

Feed

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and second, the increasing use of agri-produce as fuel is leading to lesser availability offood for consumption. It is being reported that around 100 million tons of grains areconverted from food to fuel every year (Kim, 2009). Therefore, it is a serious concern forthe consumers and policy makers to decide on the use of available resources. Thesefactors raise serious economical, environmental, and social concerns that impact oneveryone in the world. Because of these factors, research on FSCM has gained pace in therecent years and there is a significant amount of ongoing research on various aspects.

1.2 Post-harvest wasteThe major concern for FSCM is the post-harvest wastage. It has been reported that ahuge amount of agri-fresh produce is wasted in various operational stages of the FSCM(Murthy et al., 2009). The amount of post-harvest waste ranges from 20 to 60 percent ofthe total production across the countries (Widodo et al., 2006). In comparison to increasingproduction, waste reduction can be a better way to increase the returns and reduce theconsumer prices (Kader, 2005). Major operational causes of waste are inefficiencies instorage, handling, and transportation (Murthy et al., 2009). Furthermore, there is a lack ofan efficient universally accepted method to predict and estimate the waste in variousoperations. It is also difficult to estimate the exact amount of waste due to theheterogeneity in crops, countries, climates, etc. (Mazaud, 1997). Another significant reasonfor this waste is the lack of adequate infrastructure for processing, cold-storage andtransportation in developing countries such as India (Viswanadham, 2006). A largenumber of intermediaries supplement the lack of infrastructure, but add to the waste andincrease the per unit consumption price (De Boer and Pandey, 1997). The other majoroperational cause is the lack of proper planning and management practices in theFSCM. This is because majority of the farmers are small land holders and share croppersand have little knowledge of technology, market demand, and financial incentives.

The huge quantum of post-harvest waste and lack of an acceptable and reliableestimate across produce, regions, and climates make the problem quite severe. Thispost-harvest waste reduces the farmer’s share in the final price and results in a loss ofrevenue. There is low motivation for farmers in agri-fresh produce as it results inlowering the bargaining power for the farmers. On the consumer side, post-harvestwaste results in lesser availability and thus a higher price. It also severely reduces thequality of the available produce and the options available for the consumers. Hence, itbecomes necessary to critically analyze the status of FSCM and identify potential areasof improvements. Thrust should be on reducing the post-harvest wastage by adoptingefficient practices.

To meet this objective, we defined the agri-fresh produce SCM in this section. Thenext section portrays the details of the review process. Based on the analysis of theliterature, an overview and segmentation are presented in Section 3. Section 4 presentsthe classification of the literature based on the problem context. The classificationbased on methodology is presented in Section 5 whereas the classification based on theproducts is addressed in Section 6. The paper is concluded in Section 7 with a detaileddescription of the scope for future research.

2. Review processMeredith (1993) defined a literature review as a summary of the existing literature byfinding research focus, trends, and issues. Fink (1998) further modified the definition


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and defines a literature review as a “systematic, explicit, and reproducible design foridentifying, evaluating, and interpreting the existing body of recorded documents”. Thisdefinition has given emphasis to the review process as well as the desired results.Brewerton and Millward (2001) define a literature review as content analysis, wherequalitative and quantitative techniques are used to find the structural and contentcriteria. Harland et al. (2006) argued that a literature review identifies the conceptualcontent of the domain and may even contribute to theory development. The criticalanalysis of the research papers reveals several un-noticed trends in the literature. But,the challenge is in analyzing the whole literature which keeps increasing with thedevelopment of the domain. Therefore, we have to put some delimiting criteria to makeit possible to provide comprehensive reviews within the defined boundary. Mayring(2003) has proposed a four-step process for performing a literature review. A detaileddescription of the process suggested by Mayring (2003) is presented in Section 2.2.

Lately, there is a great emphasis on the structured and systematic review process.A systematic review process is an evidence-based literature review process, originatedfrom medical science literature. Over the years, the systematic review has found acceptancein several other scientific disciplines for reviewing the existing body of literature such aseducation (Oakley, 2003), nursing (Evans and Pearson, 2001), housing policy (Daviesand Nutley, 1999), criminal justice (Laycock, 2000), social science (Tranfield et al., 2003), etc.This trend was mainly motivated due to the structured approach of systematic reviewsand support of government agencies due to the need for evidence-based knowledge formaking public policies. Tranfield et al. (2003) defined a systematic review as a processof “synthesizing research in a systematic, transparent, and reproducible manner withthe twin aim of enhancing the knowledge base and informing policymaking and practice”.They defined a three stage approach for systematic review as consisting of:

(1) planning the review;

(2) conducting the review; and

(3) reporting and dissemination.

Oakley (2003) has discussed the status of a systematic review in knowledgemanagement and education practice. The author has highlighted the challenges facedby a systematic review, that includes:

. political challenges;

. technical challenges due to lack of skills and procedures expertise, reliance onrelatively undeveloped electronic databases, relatively low yield of usablestudies, and the time investment in the systematic review; and

. the conceptual/intellectual challenges due to the difficulty in defining the initialresearch question, deciding how to define sound studies, etc.

Rousseau et al. (2008) also advocated the systematic review and proposed a four stepapproach. This includes:

(1) question formulation: reflection, debate and reformulation;

(2) comprehensive identification of relevant research;

(3) organizing and interpreting; and

(4) synthesis.

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They differentiated synthesis into four categories namely aggregation, integration,interpretation, and explanation.

On the other hand, there also exists a growing body of literature that puts a questionmark on the credibility of the systematic review process as a scientific approach for aliterature review. This is due to the relative infancy of systematic reviews, confusingguidelines, significant difference between medical science and other disciplines such associal science. According to Petticrew (2001), systematic reviews are not even fullyaccepted in medical research so there cannot be a consensus on their acceptability insocial science. It is also found that a few authors (Evans and Benefield, 2001; MacLure,2005) have highly criticised the systematic review for being a mechanistic process withabsence of creativity.

The objective of this paper is to present a literature review and not to indulge in thedebate of evaluating the review processes adopted by the researchers. Therefore, webuild this paper on the procedure suggested by Mayring (2003). The approachproposed by Mayring (2003) though similar to systematic review process may lack afew characteristics. This can be taken care to make the review process more robust.Nonetheless, we do not stick to all the nuances expressed by the respective definitionsof review processes. Thus, the approach adopted in this paper can be defined as thefit-for-the-purpose systematic review process.

In order to enhance the level of understanding, we introduced the explicit researchquestion to guide this review process. The objective is: “what is the current status ofliterature addressing the major operational issues causing post-harvest waste in theagri-fresh produce supply chains?” Here major operational issues refer to the maincauses of waste at the operational level (from the production to delivery) of theagri-fresh produce. These operational issues are covered in more detail in Section 4 ofthe paper.

Ahumada and Villalobos (2009b) have differentiated the major issues for agri-freshproduce into strategic, tactical and operational issues. They defined that strategic issuesincludes decisions such as financial planning, supply network design, selection ofcapacity, and technology, etc. the tactical decisions cover harvest planning, schedulingof crops, selection of labor, capacity and crops, etc. The operational decisions includeproduction scheduling activities, harvesting, storage, etc. It is slightly difficult todifferentiate between strategic and tactical issues as well as tactical and operationalissue, thus we made an effort to differentiate the issues into strategic and operational.Later the research papers segmented into operational issues are analyzed and it wasfound that there are few major causes of post-harvest waste. The quantum of the causewas decided by the inputs from theory as well as practice. In theory there are few paperssuch as Murthy et al. (2009) which differentiate the post-harvest waste according to theoperational issues. To get the practitioner’s view, newspapers and magazine articleswere analyzed, and semi-structured interviews were conducted with farmers, retailers,wholesalers, and transporters. From this exercise it was evident that transportation isthe biggest cause of post-harvest waste followed by inventory management. In additionto that, a major portion of the agri-fresh produce is wasted at the farmer’s end. Thus,there exists a need for an efficient tool for production planning and harvest scheduling.One of the major causes of all this waste was the lack of information regarding demand.Though there is an absence of studies quantifying the effect of the lack of demand onpost-harvest waste, this factor (lack of information regarding demand) was considered


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as a major reason of waste by the researchers (Viswanadham, 2006) as well as bypractitioners. Therefore, the target of this paper is to analyze the literature addressingthe major operational issues (demand forecasting, harvest scheduling, inventorymanagement and transportation) for post-harvest waste reduction in agri-fresh producesupply chains.

2.1 Literature collection and boundary identificationWe started with a keyword/phrase search and then delimited the literature thereafter.Thus, it is a combination of deductive and inductive approaches. Initially, a huge amountof literature was collected, including research papers from peer-reviewed journals,conference proceedings, white papers, presentations from the industry, and MS/PhDtheses. Over a period of two years, more than 1,000 such documents were collected fromvarious sources. Detailed analyzes of this material facilitated the focus on major causesof post-harvest waste. Simultaneously it was also realized that there is a huge amount ofrepetition of the research material itself, such as conference proceedings are laterconverted into journal papers and MS/PhD theses led to journal publications. Therefore,to reduce the repetition and to enhance the reliability, papers published in peer-reviewedjournals only were considered. These published papers have used other papers in theliterature so a cross referencing approach was also adopted to find the other relevantpapers. The fact that a citation analysis (Appendix 1) is also performed ensures thatalmost all of the relevant papers are included in this review. Moreover, it also ensures thatthe highly cited papers (even from non-peer-reviewed journals) were not omitted, thoughit may not be included in the sample considered for a descriptive analysis. The delimitingboundaries were also developed as we collected more literature. Initially we collectedall kinds of published materials available from various sources and later by means ofcross referencing. We developed some delimiting conditions which are given as:

(1) papers published only in peer-reviewed journals were considered;

(2) papers were collected for a period of 20 years (1991-2011);

(3) papers addressing agri-fresh produce were considered; and

(4) papers addressing the major operational issues were considered.

A paper which satisfies all these four delimiting conditions was included in the study.For example, a paper “Orange harvesting scheduling management: a case study” ispublished in a peer-reviewed journal in year 2006. This paper is addressing anoperational issue (harvest scheduling) for an agri-fresh produce (orange). This paper hasbeen included in this study. On the other hand “Improved supply chain managementbased on hybrid demand forecasts” is not included into the sample because it discussesa processed product (vegetal oil). Papers were collected applying a structured search,using phrases such as “fresh produce”, “agriculture supply chain”. Later, vegetablesupply chain, fruit supply chain, fresh supply chain, perishable agriculture products,food products, potato, tomato, mango, grapes, banana, etc. were also included. Citationsof papers related to agri-fresh produce were referred to find more related papers.Research databases such as Emerald (www.emeraldinsight.com), Elsevier (www.sciencedirect.com), Springer (www.springerlink.com), Wiley (www.wiley.com), andEbsco (www.ebsco.com) were searched for relevant papers. The papers were eitherselected or rejected after performing a content check.

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Papers addressing the issues for perishables though not food item were excludedfrom the study as shown in Figure 1. This reduced the number of papers to just a fewhundred. After this initial exercise, the papers other than those in peer-reviewed journalswere excluded and this reduced the total number of papers to around 150. After excludingthe papers discussing non-agri-fresh produce the total number of papers in the samplewas 86. The quality of the research papers included for the analysis may be judged bythe citation criteria presented in Appendix 1. Considering the relative infancy of the topic,it is not deemed appropriate to exclude papers with fewer citations. Moreover, there is alsoa dilemma regarding the correlation between years elapsed since publication and thecitations as new papers get less opportunities to be cited. Therefore, though the citationanalysis is presented for all the papers, this is not exercised as criteria to exclude papers.In order to increase the reliability of this process, the same exercise was performedseparately by the authors of this paper.

2.2 Content analysisThis section presents the content analysis of the collected literature. The four-stepprocess suggested by Mayring (2003) is followed to perform the analysis (Seuring andMuller, 2008):

. Step 1. Material collection: the material is collected within the boundary bydefining the unit of analysis (here, a research paper). The papers were collectedfrom all sources and later subjected to delimiting criteria defined in Section 2.1.Papers that satisfied all the four delimiting criteria only were selected.

. Step 2. Descriptive analysis: formal aspects of the collected material are analyzedto provide the base for theoretical analysis (e.g. the number of publicationsper year). Selected papers are sorted according to the year of publication,publication outlet, etc.

. Step 3. Category selection: structural attributes and corresponding analyticcategories are selected to categorize the collected material. Structural attributesconstitute the analytical categories to form the major topics of analysis. Paperswere classified according to the structural attributes namely geographies,problem context, methodology, and product. The selection of attribute is mainlybased on the literature and inputs from practitioners.

. Step 4. Material evaluation: the collected papers are analyzed based on thestructural attributes to find relevant issues and trends in the literature. Detailedanalysis of the papers is performed within the structural attributes.

Figure 2 shows a detailed representation of the analysis process (steps 3 and 4).A feedback loop is shown for the analysis purpose, but such loops shall also be usedfor the overall processes.

One can adopt a deductive or an inductive approach to define structural attributes andthe corresponding analytical categories. In the case of a deductive approach, structuralattributes are defined first and then the material is collected, whereas in an inductiveapproach, structural attributes are identified by means of a generalization (Mayring,2003). But, in a literature review it is better to use both the approaches iteratively. In thiscase we have taken inputs from the literature as well as practitioners to form thestructural attributes by adopting a combination of deductive and inductive approach.


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There are several generalizations to be made to comprehend all the papers. To avoiderrors, one needs to follow steps 3 and 4 iteratively. This is more so as the attributes andcategories get revised in the processes of analysis (Mayring, 2002).

It was found that in management research very few papers have the same researchquestion and study the same events. Moreover, it is the fragmented nature of themanagement research itself (Whitely, 2000) that makes synthesis a difficult process.It is also observed that less integration in management literature is due to the low levelof motivation shown by the management journals for replication (Kilduff, 2007).The discipline itself is more oriented towards novelty (Mone and McKinley, 1993). In casethe field of study comprises of semi-independent fields, then, synthesis becomes evenmore complex. In such cases, synthesis is achieved through summarizing the findingsof a group of studies. The heterogeneity in data makes it highly difficult to performa meta-analysis and rarely possible in management research (Tranfield et al., 2003).The advocates of meta-analysis for literature synthesis suggest that some differences canbe accounted for by identifying mediators and moderators. But, the analysis of theliterature clearly reveals that this is also an infeasible option in the current scenario.Therefore, synthesis can be improved by the categorization of literature in order to havean enhanced understanding of the literature within the category.

Considering these constraints, the attributes were generalized to form several categoriessuch as geographies based on the economies that are characterized as developed, anddeveloping economies. Methodologies are categorized as modeling (linear programming,goal programming, dynamic programming, and stochastic programming), simulation(simulation, systems dynamics), case study, and empirical research (field research,econometric models). Problem contexts are categorized into demand forecasting (demandforecasting, demand-price elasticity), production planning (production, harvestscheduling), inventory management, transportation (transportation, vehicle routing,distribution), and others (introduction, co-ordination, and integration). It is possible that

Figure 2.Step wise representationof a structured contentanalysis

Theory-driven selection of structuraldimension and analytic categories

Determining definitions andcoding for each category

Analyzing the material: Denotationof relevant place of finding

Analyzing the material: Editing andextraction of place of finding

Preparation of results

Source: Mayring (2003), Seuring and Müller (2008)

Revision of structuraldimensions and analytic

categories

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a research paper may be categorized under two categories of the same attribute, but thebest suited category is considered to avoid any kind of possible duplication.

2.3 Rigor of the research processAll the research processes and corresponding methodologies have some advantagesand disadvantages. Objectivity of the current process is maintained by adopting astructured and fit-for-purpose systematic process. Guidelines in Kassarjan (1997) werethe base for validity of this research. Reviews of other topics were referred to form theconstructs. Reliability was ensured by following the citation analysis and takingthe help of other independent researchers. Hence, the current research meets therequirement of being a rigorous research process.

3. Research segmentation and overviewThe research papers that finally qualified the delimitation criteria were analyzed for theresearch outlets. This exercise was performed to evaluate the effort of researchers andpractitioners from various disciplines to shape the current status of agri-fresh producesupply chain literature. Table I presents the list of the journals that published theresearch addressing the operational issues for post-harvest waste in agri-fresh producesupply chain. From this table, it is evident that most of the journals are either fromoperations management (OM) or agriculture, with few from other scientific disciplinessuch as information technology and economics.

3.1 OM-journalsThese journals specifically address the OM issues, though the problem may berelated to agri-fresh produce. Among the total 86 papers reviewed, 46 papers werepublished in these journals addressing the FSCM issues from an operationsperspective. This shows that a large number of researchers have attempted to solve anOM problem where the concerned product is an agri-fresh produce. Here the producecharacteristics may or may not be of much concern as the main objective isoperational excellence. In such a scenario, the chances of theory development,specifically for agri-fresh produce are very less. It more likely that already existingtheories may be applied to solve the problems at hand. Thus, the exclusiveness foragri-fresh produce may not be addressed in most of the papers. The percentage ofpapers published in the journal addressing agri-fresh produce as compared to the totalpublications in these journals is negligible. In a period of 20 years there are only46 papers published out of which 20 are published in just two journals. This showsthat, though there is an interest in addressing the agri-fresh produce but the totalresearch is very less.

3.2 Agricultural journalsThese journals specifically address the agri-fresh produce related issues which mayalso cover OM issues. It is to be noted that 30 of the 86 papers are published in thesejournals addressing the operational issues of FSCM. It indicates that there is very lessinterest in addressing the post-harvest waste due to operational issues by theagricultural researchers. Though there exists a huge need to reduce the waste but thefact that there are 30 publications in 20 years shows that on an average there are lessthan two papers published in a year. Moreover, more than one-third (11 out of 30) papers


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appeared in a specific journal (British Food Journal (BFJ)) shows that there is an overallgap in the agricultural journal to address the problem.

3.3 Other journalsThese journals though not focused on agri-fresh produce or OM have published papersaddressing the post-harvest waste due to the operational issues. The fact that ten papersout of 86 are published in these journals shows that there are few papers addressed out ofthe OM and agricultural journals. But on the other hand, the fact that none of the journalshave published more than one paper over 20 years shows that the issue of post-harvest

Name of journal Number of papers published

OM-journalsInternational Journal of Production Economics 11Supply Chain Management: An International Journal 9European Journal of Operational Research 5International Journal of Physical Distribution & Logistics Management 4Journal of the Operational Research Society 3Interfaces 2Production and Operations Management 2The International Journal of Logistics Management 2Computers & Operations Research 2International Transactions in Operational Research 2Total Quality Management & Business Excellence 1Manufacturing & Service Operations Management 1International Journal of Logistics Research and Applications 1International Journal of Operational Research 1Agricultural journalsBritish Food Journal 11Agricultural Economics 3American Journal of Agricultural Economics 3Journal of Food Engineering 2Agribusiness 2Computers and Electronics in Agriculture 2Food Policy 1Agricultural Systems 1Sri Lankan Journal of Agricultural Economics 1Renewable Agriculture and Food Systems 1International Food and Agribusiness Management Review 1Indian Journal of Agricultural Economics 1Journal of Food Distribution Research 1Other journalsJournal of Cleaner Production 1International Journal of Retail & Distribution Management 1Scientific Research and Essays 1Biosystems Engineering 1Journal of Business and Public Policy 1China Economic Review 1Knowledge and Process Management 1Computational Statistics and Data Analysis 1Applied Economics 1International Journal of Emerging Markets 1

Table I.List of journals reviewedand papers published byjournal for the period1991-2011

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waste due to operational issues is not included in the objective of the these journals. Thepublication of papers in these journals may be attributed to the broad boundaries of someof the journals or to the lack of a dedicated journal for agri-fresh produce.

From the list of journals, it is seen that despite the increasing importance of the topicno journal deals exclusively with agri-fresh produce supply chain. Though there exist fewjournals such asBFJ, Computers and Electronics in Agriculture (CEA), etc. the majority ofthe research is published through established OM/SCM journals such as InternationalJournal of Production Economics (IJPE), Supply Chain Management: An InternationalJournal (SCM:IJ), and European Journal of Operational Research (EJOR), etc. The smallnumber of publications over a period of 20 years in more than 30 journals shows thatoverall research addressing this issue is negligible. Three journals (IJPE,BFJ, andSCM:IJ )account for more than 30 percent of the total papers. This shows that this issue is promotedby only few of the journals. Out of the total journals, about 20 have published only onepaper and another eight journals have published only two papers in the last 20 years. Thisshows that this issue is not at all in the main agenda of these journals.

Figure 3 shows the trend of FSCM literature across the last 20 years. It presentsthe annual publication frequency of the total papers combining all the issues addressed.It is evident from Figure 3, that lately there is an increasing interest in addressing theissues in FSCM. As the graph is not linearly increasing over the years so this growth maynot be fully credited to the increase in number of total publications every year. Moreover,the sudden increase in the number of papers from the year 2006 can be attributed to theglobal factors that attracted the attention of researchers and practitioners to this fieldwhich includes the food and fuel crises. Since the year 2006, the world price of variousfood items as well as crude oil has shown a drastic increase. This increase was even morethan 200 percent for some items such as rice (Kim, 2009). The increase of fuel price hada double impact on the agri-fresh produce, first through the increase of cost intransportation and energy inputs, and second, through use of vegetables oils as biodiesel.Therefore, the increase in the number of research papers seems to be a reflectionof these critical factors. These factors did not seem significant in the period 1991-2005 asthe maximum number of papers published in any given year is four which is quite less.It is interesting to note that though post-harvest waste was always a major concern butonly recently gained the attention of researchers. With the increasing population thereis a high possibility that this issue will get more attention in future.

Figure 3.Trend of FSCM literature

across the years


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The volume and continuity of research papers as shown in Figure 3 will surely attract theattention of publishers and editors towards the lack of a journal addressing the agri-freshsupply chain issues. The effect of lack of a specific journal shall reflect in future specialissues addressing FSCM. It is also found that a lot of papers have addressed a countryspecific issue. In these country-specific publications, the UK, China, The Netherlands, andthe USA gained a majority of the attention, with a limited thrust on Brazil, India, Vietnam,and Spain. One of the reasons for this may be the organized food sector in the developedcountries which can fund the research in FSCM as compared to the fragmented foodsectors in developing countries. In developing countries, the major research concern is forproducing enough food grains to feed the population rather on FSCM. Table II presents thedetails of the country-specific studies. The studies which are not very specific to anyparticular country or region are categorized as generic. This is not at all surprising as it isquite similar to the country rating according to SCImago (www.scimagojr.com). The littledeviation is explicit due to the economical orientation of the few countries towardsagriculture. Besides, the global nature of the agri-fresh produce supply chains has resultedin most of the studies as “generic”, i.e. addressing no specific country.

Based on the level of economic development, countries are classified as developedand developing. Developed countries include the USA, Western European nations,Singapore, etc. developing countries include Brazil, Russia, China, India, etc. Figure 4shows the distribution of the number of published papers by developed and developingcountries. This shows that research during the last two decades focused much of itsattention on developed countries, with limited attention to developing countries. Theexisting advanced infrastructure, availability of funds, customer awareness and mostimportant consolidation of food organization has given the desired research environment

Country Number of studies

USA 9UK 8China 5The Netherlands 5India 4Chile 3Brazil 3Vietnam 2Spain 2Australia 2Russia 2Ukraine 1Turkey 1Greece 1Finland 1Canada 1France 1Philippines 1Sri Lanka 1Thailand 1Slovenia 1Generic 31

Table II.Number of studiesacross countries

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in developed countries which seriously lacks in the developing countries. There is anabsence of customer oriented research funded by food organizations in developingcountries. Most of the research is state funded with the objective to increase food grainproduction to feed the maximum percentage of the population.

4. Classification based on problem contextWe now consider the papers addressing the agri-fresh produce to classify and reviewthe research based on major operational issues causing post-harvest waste. Accordingto the categorization by the practitioners and academicians, the major issues areclassified into four categories of:

(1) demand forecasting;

(2) production planning;

(3) inventory management; and

(4) transportation related issues.

In addition to these four major categories of operational issues there are certain otherissues which are operational in nature but do not fit into any of these four categories.The examples of such are buyer-supplier relationship to reduce wastes, e-commerceactivities in FSCM, case studies on FSCM, etc. These issues have been put together in afifth category called “others”. A review of literature on these issues is presented in thefollowing sub sections.

4.1 Demand forecastingDemand forecasting is one of the most researched topics in OM. It gainedimportance due to the time lag in the knowledge of a future event and its occurrence(Makridakis et al., 1983). It becomes even more important in case of agri-fresh producedue to the short planning and selling horizon. This section classifies and reviews theliterature on demand forecasting for agri-fresh produce. Key findings from the demandforecasting literature are presented later in this section.

Figure 4.Research studies on

specific countries by levelof economic development


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Agriculture has not received the required attention in the demand forecasting literatureas compared to manufacturing and service sectors. Within the agriculture sector, priorityis given to food grains with insignificant attention towards agri-fresh produce.For example, Zou et al. (2007) addressed the price forecasting of wheat in the Chinesewholesale market, Co and Boosarawongse (2007) studied Thailand’s rice forecast,Jumah and Kunst (2008) studied the seasonal prediction of cereal (barley and wheat)prices in European market. Several other authors also have studied the problem ofdemand/price forecasting for food grains such as wheat and rice. The obvious reason isthat the major concern is to produce enough food grains to feed the population. It is more soin the Asian countries where a huge population of the world poor live. The second majorconcern is the food price and inflation as increasing prices make the food unaffordable fora large number of people.

There exist a few studies addressing the role of elasticity on agri-fresh producedemand. Authors such as, Fan et al. (1994), Halbrendt et al. (1994) and Wu et al. (1995)evaluated the effect of income elasticity on the demand of agri-fresh produce. It wasobserved by Chern and Wang (1994) that the agri-fresh produce demand is extremelyincome inelastic. Studies also compared the role of income elasticity, own-price,and cross-price elasticity of agri-fresh produce in the rural and urban areas(Ahmadi-Esfahani and Stanmore, 1997; Mutuc et al., 2007). This is because changingeating habits and rising income levels have changed the demand pattern in urban areas.

Studies focusing particularly on agri-fresh produce demand forecasting are veryfew. This is mainly because of the low attention to FSCM by state agencies and privatefirms. Moreover, the nature of agri-fresh produce that it can be substituted by otheragri-fresh produce and non-agri produce reduces its importance. For example, the roleof a fruit in any individual’s diet can be fulfilled by another fruit or dairy products butsame is not true for food grains such as wheat or rice. Liu et al. (2001) addressed thedaily demand forecast of a perishable ingredient in a fast-food franchise. They usedARIMA models to forecast the daily demand of the ingredient and also analyzed theeffect of outliers on the forecast accuracy. Doganis et al. (2006) studied the demand formilk on a daily basis. Most of the other papers addressing the demand of a fresh producehave focused on the sales in retail shops/supermarkets (Aburto and Weber, 2007;Ali et al., 2009; Chen and Ou, 2009). In such cases the retailer can control the demand andsupply. But, it is beyond the control of the wholesaler in the wholesale markets.Therefore, it is expected to have higher forecasting errors for agri-fresh produce in thewholesale markets (Adebanjo, 2009). Due to small market share the threshold volumeswhere never present to attract the attention of the policy makers. Moreover, thesepapers are also not necessarily focused on agri-fresh produce.

In the literature, the key variables used in modeling by several papers include sales,price, day of the week, holiday, and special discounts. This is mainly because thedemand is highly dependent on the price and consumption patterns. The effect offestivals and harvesting season shall be incorporated to enhance the forecast accuracy.It is also found that among all the techniques such as, root mean-squared error (RMSE),mean absolute error (MAE), and mean square error (MSE) mean absolute percentageerror (MAPE) is mostly used for judging forecasting accuracy. Key forecastingtechniques applied in literature include auto-regressive integrated moving average(ARIMA) (Liu et al., 2001), artificial neural network (ANN) (Aburto and Weber, 2007),genetic algorithms (GA) (Doganis et al., 2006), etc. It is also found that a combination

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of two methods taking best of both performs better than the methods individually(Aburto and Weber, 2007; Zou et al., 2007). Most of these techniques have been adoptedfrom the existing literature for electricity load determination or share market priceforecasting with very limited modification for FSCM. Aburto and Weber (2007) reportedthat based on the MAPE values the forecasting error ranges from 56.83 to 28.8 percentfor vegetable oil. This value seems quite high but there seems a lack of a method toforecast the demand more efficiently.

It is found that very few papers have addressed demand forecasting on a daily basisas mostly demand is forecasted on a weekly or monthly basis (Zou et al., 2007). It is alsofound that demand for agri-fresh produce is forecasted on aggregate level and not on adisaggregate level. Very few author(s) showed interest in the demand forecasting ofagri-fresh produce at disaggregate level. Shukla and Jharkharia (2011) presented anARIMA model to forecast daily demand of onions in an Indian wholesales market.Therefore, from this review it is evident that there is a lack of a research, forecasting dailydemand of agri-fresh produce on a disaggregate level, especially in a spot market. Thechallenge for the researchers is to find out how to forecast the demand of an agri-freshproduce on a daily basis in a spot market. The associated hurdles are in deciding theagri-fresh produce for which the demand is to be forecasted, the source of data,the appropriate methodology, tools, etc. Additionally, the validation and generalizationof the proposed model across products and geographies will pose a real challenge.Biggest challenge will be the communication of the results with the farmers. In thewholesale market there seems no mechanism to update the daily demand/price forthe farmers. There is also a lack of ownership in the unorganised sector as compared tothe manufacturer/retailer in the organized sector. There also exists a vacuum of interestin demand forecasting and information sharing to increase the overall profits. Therefore,it is required to quantify the overall effect of accurate demand forecasting on the revenuesof the shareholders in FSCM.

With the information of the market demand on time farmers also need to plan theplanting and harvesting activities. The next section classifies and reviews the literatureon the issue of production planning. It also presents the key findings from the productionplanning literature.

4.2 Production planningResearchers have presented several models for farm location, crop plantation, andharvesting analysis. Several reviews present the status of research from differentperspectives. For example, Glen (1987) focused on farm planning, Lucas and Chhajed(2004) on location analysis applied to agriculture, Lowe and Preckel (2004) on cropplanning, and, in a recent review, Ahumada and Villalobos (2009b) addressed theproduction and distribution of crops.

Over the years, there is a significant rise in research on production planningdecisions pertaining to agri-fresh produce. This is mainly because of the significant rise inthe demand of the produce. However, the literature is still in its infancy (Ahumada andVillalobos, 2009b). Traditionally, the decisions were based on experience or intuition(France and Thornley, 1984), but the use of operations research techniques has givenconsiderable benefits to organizations. Some researchers (Caixeta-Filho et al., 2002;Caixeta-Filho, 2006) have used mathematical modeling as a solution technique toenhance the performance. One of the key areas for the application of mathematical


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modeling is to find the optimal harvesting pattern. Factors influencing the harvestingpattern include sales price (Stokes et al., 1997), taxes (Stokes et al., 1997), plant maturation(Darby-Dowman et al., 2000; Widodo et al., 2006), agri-fresh produce deterioration(Widodo et al., 2006; Caixeta-Filho, 2006; Ferrer et al., 2008; Lodree Jr and Uzochukwu,2008), and weather conditions (Darby-Dowman et al., 2000). Caixeta-Filho et al. (2002)incorporated customer demand as a factor for deciding the harvesting pattern. Stokes et al.(1997) introduced a variable sales price for the different levels of plant maturity toincorporate customer demand. Most of these factors are relevant for revenuemaximization and not directly for waste reduction. Interestingly the price and incomeelasticity are of interest in demand forecasting but not so much in harvest scheduling.This reveals the hidden assumption that there exists a large enough demand.

The agri-fresh produce studied in the literature are ornamental plants (Stokes et al.,1997), flowers (Widodo et al., 2006; Caixeta-Filho et al., 2002), fruits (Caixeta-Filho, 2006;Ferrer et al., 2008; Arnaout and Maatouk, 2010; Bohle et al., 2010; Devadoss andLuckstead, 2010), and vegetables (Hamer, 1994; Darby-Dowman et al., 2000; Lodree Jrand Uzochukwu, 2008; Ahumada and Villalobos, 2011). Hence, it is observed thatresearch is concentrated on the flowers and fruits of the plants with only a limitedattention to roots and stems of the plants. It can also be interpreted that the researchinterest is in expensive produce such as ornamental plants and fruits. This again showsthe orientation of research towards the primary objective of revenue maximization andnot waste reduction. Moreover, it is produce specific and cannot be exactly replicatedfor another produce. The research is mainly limited to America (Caixeta-Filho et al.,2002; Caixeta-Filho, 2006; Ferrer et al., 2008; Lodree Jr and Uzochukwu, 2008; Arnaoutand Maatouk, 2010; Bohle et al., 2010; Devadoss and Luckstead, 2010; Ahumada andVillalobos, 2011) and Europe (Darby-Dowman et al., 2000), with limited attention to thedeveloping countries. One of the major reasons for this is the huge percentage of foodgetting processed in developed countries as compared to developing countries. Thus, indeveloped countries the harvest is scheduled for satisfying the demand of the foodprocessing factories which can be controlled as compared to the wholesale demand indeveloping countries.

It is evident from this review that, the majority of the research papers have focusedonly on the supply side assuming a large enough demand. It is also found thatmathematical modeling is the most prominently applied technique in productionplanning. Most of the farm decisions earlier were related to the use of farm labor, capital,and farm location (Glen, 1987). But, little attention is given to the operational issues such asplanting and harvesting activities. It was further found from this review that, developingcountries are yet to get attention of the researchers though being one of the largestproducers of agri-fresh produce. The gap exists in proposing an efficient harvestingschedule for agri-fresh produce considering the stochastic nature of demand, maturation,and deterioration characteristics and other factors such as the transportation time, etc.It is hard to find a universal model applicable for all the agri-fresh produce acrossgeographies. But, the thrust should be on proposing a flexible model that canaccommodate the local factors.

The next section classifies and reviews the literature on the issue of inventorymanagement. It also presents the key findings from the inventory management literature.

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4.3 Inventory managementOver the past several decades, various models dealing with the inventory of agri-freshproduce supply chains have been studied by the researchers. These researchers tried tomodel the deteriorating characteristic of the agri-fresh produce for inventory management.A comprehensive literature survey of inventory models was first given by Nahmias (1982).Raafat (1991) reviewed the literature on continuously deteriorating inventory models.The latest review of the deteriorating inventory models is given by Goyal and Giri (2001).These reviews partially addressed the agri-fresh produce taking various other productsin the deteriorating category such as blood platelets. Most of the publications in this fielddeal with pricing, the effect of inflation, and delay of payments. Apart from pricing relatedfactors, other factors that are significant in modeling the deteriorating products inventoryinclude demand, deterioration rate, transportation lead time, and backlogging/shortage.It was found that the capacity of the production plants or warehouses is also consideredby some of the authors.

The inventory models can be categorized as deterministic or stochastic based onthe demand characteristics. In the deterministic models, demand is assumed to be afunction of time and is known in advance. There are a large number of papersconsidering the demand to be time dependent such as Teng and Yang (2004), Chern et al.(2005), Yang (2005, 2006) and Hsieh et al. (2008). Some authors assumed demand to bea log-concave function of time such as Teng et al. (2002) and Dye et al. (2006). Fewassumed demand to be constant such as Mandal et al. (2006), Li et al. (2007) and Lin(2007). Some of the authors assumed the demand to be stock-dependent such as Balkhiand Benkherouf (2004), Wu et al. (2006) and Dye et al. (2007). In stochastic models, thedemand is assumed to be a function with a known mean and standard deviation. Tekin(2001) and Kopach et al. (2008) considered demand as a Poisson distribution with aknown mean whereas Kanchanasuntorn and Techanitisawad (2006) modeled thedemand as a function with a known mean and standard deviation. Studies alsoconsidered the demand to be a function of the available stock. Some authors haveconsidered demand as following a trapezoidal type (Cheng and Wang, 2009) or a ramptype (Manna and Chaudhuri, 2006; Deng et al., 2007; Skouri et al., 2009).

The deterioration rate is the most important factor in the inventory models fordeteriorating products. Most of the studies have considered that the inventorydeteriorates at a constant rate. A few authors such as Lin et al. (2006) and Huang andLiao (2008) have considered the exponential deterioration rate. The deterioratingpattern following Weibull distribution is also studied by the authors (Covert andPhilip, 1973; Wu et al., 1999; Wu, 2001; Giri et al., 2003; Skouri et al., 2009). Some of thestudies (Kopach et al., 2008; Broekmeulen and Von Donselaar, 2009) assumed thatproducts such as blood platelets and packaged food expires rather then decay after acertain period of time.

Transportation lead time (from producer to retail stores/warehouse) is assumedto be zero/negligible by most of the studies, but some assumed it to be positive (Tekin,2001; Teng et al., 2003; Kanchanasuntorn and Techanitisawad, 2006; Kopach et al., 2008;Skouri et al., 2009). Shortage and backlogging were considered either as allowed, notallowed or partially allowed. Apart from these factors, some authors (Goyal, 2003;Sana et al., 2004; Manna and Chaudhuri, 2006; He et al., 2010) modeled the inventoryconsidering the capacity constraint. They included the production capacity or warehousecapacity in the model. Very little literature exists on the retrieval policy adopted


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by the consumers. Generally, the studies have assumed a first-in-first-out (FIFO) retrievalpolicy, with limited attention on last-in-first-out (LIFO) policy (Broekmeulen andVon Donselaar, 2009). Some of the studies (Cohen and Prastacos, 1981; Broekmeulenand Von Donselaar, 2009) also compared FIFO and LIFO retrieval policies.

From this review, it is found that, very few papers have addressed the inventoryissues for agri-fresh produce. It is also evident that most of the papers addressingthe deteriorating products inventory problem are focused on the cost/price relateddecisions. Very few papers addressed the operational decisions of the inventorysystem. The factors that are considered in the modeling include demand, deteriorationrate, transportation lead time, shortage/backlogging, capacity, and retrieval policy.The models as well as the factors such as demand variations, deterioration rate, etc. arelargely studied for manufactured products with little attention for agri-fresh produce.The literature seems more concerned for revenue maximization with waste reductionas a secondary objective. The social and environmental importance of agri-fresh produceis neglected and the waste reduction decisions are left on the sole discretion of theretailers. Agri-fresh produce is assumed similar to any other product withoutconsidering the factors such as seasonal demand, effect of weather, etc. The majorquestion in front of the researchers and practitioners is in building an inventory modelby incorporating the characteristics of the agri-fresh produce. The optimal orderingpolicy, retrieval policy and economic order quantity policy though well studied formanufactured products needs to be tailored according to the needs of agri-fresh produce.The other major issue is the flexibility required for generalization of the proposedmodel across the diverse category of produce and geography. Incorporating thedeteriorating nature, stochastic and seasonal demand, and variety of substitutableproduce in the model in order to find a solution in real time will be a complex issue evenin the future. Thus, there exists a need for as in-depth study of the inventory policy foragri-fresh produce.

The next section classifies and reviews the literature on the issue of transportation.It also presents the key findings from the transportation literature.

4.4 TransportationThis section discusses the transportation related issues in the agri-fresh producesupply chains. It has been observed that, waste in transportation is one of the highest inthe FSCM (Murthy et al., 2009). This waste is generally due to handling and deteriorationof the product. Thus, vehicle routing decisions are of high importance. Time being acritical factor the vehicle routing problem with time windows (VRPTW) gained theattention of researchers addressing transportation issues in FSCM. VRPTW assumesthe location, demand and time window to be known for each customer (Osvald and Stirn,2008). The objective is to satisfy the customer demand with minimum time, distancetraveled and vehicles used. Therefore, the aim is to find the routes for each vehiclecovering the customers. Other assumptions are that customers are assigned to onlyone vehicle and the total load of all the assigned customers cannot exceed the capacityof the vehicle.

In the case of agri-fresh produce, maintaining the delivery window with the earliestand latest delivery time for each customer becomes even more complex. As for agri-freshproduce there are losses due to the natural deterioration additional to the penalty for thedelayed delivery. The early application of VRP/VRPTW is generally for meat and

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milk transportation. A detailed review of the literature for refrigerated vehicletransportation can be found in James et al. (2006). They discuss the historic evidence ofagricultural produce transportation in refrigerated vehicles. The main focus was ontransportation through sea and modeling the temperature control system whiletransportation.

The literature discussing the delivery of agricultural produce within a city isvery scarce. The milk collection and delivery system is studied by few researchers.Tarantilis and Kiranoudis (2001) studied the milk runs of a heterogeneous fixed fleetvehicle routing problem (VRP). They applied a threshold-acceptable based algorithm toschedule the distribution of milk. Du et al. (2007) studied the parameters setting of areal-time VRP for milk runs. They proposed a two phase solution to decide an initialvehicle dispatch module and the other for deciding an inter route improvement module.They also proposed a best-fit algorithm and two-exchange algorithm for both themodules, respectively. Claassen and Hendriks (2007) focused on the milk collectionproblem. They found that the application of special ordering set type (SOST) isbeneficial for decision making in milk collection. It seems that the researchers haveaddressed the transportation of milk but not agri-fresh produce. It is to be noted thatthe same research cannot be replication as there exists a significant difference in thedemand pattern and natural characteristics of the agri-fresh produce.

There also exist a few papers discussing the trade-off of factors such astransportation cost, price, and perishability. Such as, Wilmsmeier and Sanchez (2009)discussed the effect of transportation cost on food price in shipping by performing anempirical analysis. Vanek and Sun (2008) discussed an energy consumption model toinvestigate the relation between transportation and perishability of temperaturecontrolled food products, considering the environmental impact of both the factors.Madadi et al. (2010) discussed the multi-level inventory management decision coupledwith the transportation cost. They proposed a centralised and decentralised model to findthe effect of the total retail orders on the inventory cost. Cai et al. (2010) focused on theefforts by the producer and distributor to keep the produce fresh. They studied thedecisions to be taken by the producer and distributor and the co-ordination between themin decentralised and centralised system. Broekmeulen (1998) proposed a model,incorporating factors such as seasonality and perishability, to enhance the efficiency of afruit and vegetable distribution centre. Ahumada and Villalobos (2009a) presentedan integrated MILP model for production and distribution of fresh produce, incorporatingthe produce characteristics and business constraints. Most of the research is focused onco-ordination of transportation with other functions such as production or inventorydecisions. There seems an effort to address a lot of issues in transportation of food butseems a lack in presenting a holistic view. The papers are trying to propose a solution tothe problem at hand by adopting standard methods from already developed theory.There is a lack of theory developed for agri-fresh produce transportation.

Tarantilis and Kiranoudis (2002) proposed a solution for the fresh meat distributionsystem by applying a special meta-heuristic algorithm. Faulin (2003) studiedthe application of mixed algorithm procedure to optimize the food products delivery.Hu et al. (2009) presented the distribution of food products from the wholesaler to theretailers in Beijing, China. They proposed a two stage model for VRP taking severalconstraints into consideration. They solved the proposed model using “left cutting”algorithm and compared the results with the improved ant colony algorithm (IACA).


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Hsu et al. (2007) presented a heuristic to solve VRPTW with soft time window constraints.The problem considered is the delivery of lunch boxes to a number of customers, withstochastic and deterministic demands. The model considers the fixed cost, travel cost,inventory cost, energy cost, and deterioration of food with respect to time. Very fewresearch papers have considered the deterioration of the products while transportation.One of the early applications of deterioration in modeling was considered byAmponsah and Salhi (2004). They studied the transportation of garbage and consideredthe smell of garbage (which increases with time) and the total cost into the model.Researchers have mostly adopted heuristics from the literature to address the problem athand. The key addition is the deterioration function for the agri-fresh producedeterioration. Still the main objective is revenue maximization and not waste reduction.

Chen et al. (2009) presented a non-linear programming model for productionscheduling and vehicle routing for distribution of food products. Very few researchpapers discussed the distribution of agri-fresh produce from a central depot (wholesaler)to the retailers in a city. Osvald and Stirn (2008) proposed a multi-objective VRPTWmodel for the distribution of fresh vegetables in a city. The main objectives were tominimize the time, distance traveled, number of vehicles used and penalty by thecustomers for delayed delivery. Waste reduction may be an outcome but was notthe primary objective. The problem was converted to a single objective by taking theweighted average. They applied the Tabu search algorithm to find the solution forthe proposed model. It was assumed that the produce remains stable over a period andstarts deteriorating linearly till it reaches the end of life (or becomes rotten). But, the realsituation is different as the agri-fresh produce starts deteriorating just after harvestingespecially due to handling, poor packaging, and transportation. Rong et al. (2011)proposed a mixed-integer linear programming model for production and distributionof fresh produce incorporating food quality as a key factor. They presented a case studyof bell peppers supply chain to explain the implementation of the proposed model.

From this review it is evident that most of the research in transportation isconcentrated towards transportation of meat and milk only. The issues that got attentionare the maintenance of temperature and the handling of cargo, with little attention on wastedue to deterioration. Very few research papers have addressed the issues of agri-freshproduce, especially the waste due to transportation and handling. Manikas and Terry(2010) highlighted that there is a lack of literature addressing the distribution of freshfruits and vegetables. It is also been found that time is a critical factor due to strict deliverywindow of the customers and continuous deterioration of the agri-fresh produce. Hence,there exists a need to address the VRP for agri-fresh produce considering the cost andtime factors. The major complexity is in incorporating the agri-fresh producenatural characteristics along with the practical and business constraints. Due to theNP-hard nature of the VRPTW, finding a near optimal solution in real time will also be adifficult task. The focus needs to be shifted from revenue maximization to post-harvestwaste reduction.

4.5 OthersThis section discusses the issues which come in the operational domain but could notbe included in any of the four categories as described above. For example, the issues suchas case studies, fresh produce supply chains, buyer-supplier relations, e-commerce infresh produce supply chain have been included in this section. Wilson (1996a, b),

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Grimsdell (1996) and Zuurbier (1999) discussed production and distribution of agri-freshproduce as SCM. Corbett (1993) discussed the experiments conducted to improvevegetable production. Authors such as Maia et al. (1997), Van der Vorst et al. (2000) andAramyan et al. (2007) applied advanced OR techniques to solve complex problems inagri-fresh produce. Reiner and Trcka (2004) emphasised that the study of agri-freshproduce is highly produce and company dependent and hence, further increased thecomplexity of the already complex agri-fresh produce supply chains. These paperscontributed in developing the understanding of a supply chain in the production todistribution of the agri-fresh produce.

The research on FSCM has got the attention of developed as well as developingcountries. For example, Fearne and Hughes (1999), Van der Vorst and Beulens (2002)and Taylor (2005) have conducted study on FSCM in the context of developed countries.On the other hand, there are evidences of FSCM studies in developing countries aswell. Joshi et al. (2009) and Sagheer et al. (2009) have conducted their studies in theIndian context whereas Perera et al. (2004) has done the study in Sri Lankan context.Swinnen and Maertens (2007) studied the effect of globalization on agri-fresh producesupply chain in the developing countries. In the recent years there is a significantincrease in collaboration and globalization related issues in FSCM. Accordingly, theresearch work on these topics by Matopoulos et al. (2007), Dunne (2008), Mikkola (2008)and Van Donk et al. (2008) are also found in the literature. There are several other issuessuch as quality, strategy (Blackburn and Scudder, 2009), technology implementation(Salin, 1998), etc. addressed in the literature in order to understand the concept ofsupply chain for agri-fresh produce.

The next section presents the classification of the literature based on themethodology applied to address the problems.

5. Classification based on methodologyThe literature on FSCM may also be classified on the basis of methodology used in:mathematical modeling, simulation, empirical studies, case research, action researchand general. This helps us to understand FSCM from a different perspective ofmethodological point-of-view. This section maps the methodology used vis-a-vis theproblem context of the FSCM. Table III presents the mapping of the papers addressingany problem using the corresponding technique. It is to be noted that any methodologyused to solve a problem depends on the problem itself, the availability of data and

Demandforecasting

Productionplanning

Inventorymanagement Transportation Others Total

Modeling 15 2 7 3 27Simulation 1 1 3 5Empiricalstudies 11 1 1 1 8 22Case study 3 1 3 14 21Actionresearch 2 2General 1 2 1 5 9Total 16 19 4 12 35 86

Table III.The number of papersaddressing any issues

using the correspondingtechnique


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computational resources, and the comfort levels of the researcher in using thatparticular methodology. Thus, the mapping is done to find the relation of themethodology to the problem.

Linear programming (Saedt et al., 1991; Van Berlo, 1993; Hamer, 1994), non-linearprogramming (Allen and Schuster, 2004) and MILP (Maia et al., 1997) have been used toa great extent in production planning, inventory management and transportation.Lately, LP, MILP (Ferrer et al., 2008) formulations and software packages(Caixeta-Filho et al., 2002; Ferrer et al., 2008) for solution have been used. Dynamicprogramming (Stokes et al., 1997; Widodo et al., 2006) and stochastic programming(Darby-Dowman et al., 2000) are also used in some of the papers. This shows thatmathematical programming is the most preferred methodology especially forproduction planning. The common trend is the proposing LP, MILP models andadopting standard software to solve it. It is found that not very complex models areintroduced. Thus, the use of heuristics and meta-heuristics is very limited. The overallunderstanding is that, with the progress in research more complex problems will beaddressed and tailored solution approaches will be required.

Empirical studies are mostly found focusing on demand forecasting. This is mainly dueto the availability of huge volumes of data for a large number of attributes. Researchershave used moving average, exponential smoothing, and other traditional methods,whereas some reported use of ARIMA models (Liu et al., 2001) to predict the demand in ashorter time horizon. A number of studies have shown a comparison or fusion of two ormore forecasting techniques such as ARIMA, ANN, etc. These methods are generallyadopted from literature without much modification for FSCM. This shows a need for arobust method to increase the forecast efficiency in FSCM.

Case studies have found special place in the FSCM research (Wilson, 1996a;Van der Vorst et al., 2000; Aramyan et al., 2007; Alfaro and Rabade, 2009) as a tool forgeneral awareness and defining the boundary of the domain. It has also been found thatmost of the case studies were coupled with system dynamic modeling to find a solution.This is mainly due to the novelty of the topic which encourages the researchers to usecase study to introduce as well as define the boundaries of FSCM. A simulation approachis also applied in a few papers (Reiner and Trcka, 2004), mostly systems dynamicssimulation. Other traditional tools and techniques such as algebraic equations,heuristics, meta-heuristics and regression have also been used. Computer programmingand software packages have been used for input, interface, and computations.

6. Classification based on productThe literature of FSCM can also be classified based on the produce studied. As theproduce are geography dependent, so the FSCM is affected by the environmental, social,and infrastructural conditions of the produce’s origin. Table IV represent the number ofpapers addressing an issue for a particular product. It is evident from Table IV that,mostly agri-fresh produce is considered as a single product and the problems areaddressed on an aggregate level, taking either all products or all fruits/vegetables asa single product. This is mainly because the studies were addressing the effect of a factoron the overall demand/production of the agri-fresh produce and were not muchconcerned about any specific produce. There are few cases where individual producesuch as fruits have been considered. This may be attributed to the demand of specificproduce such as potatoes and grapes by food processing companies.

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Introduction to FSCM, production planning and demand forecasting have gained most of theattention, with less attention towards inventory management and transportation.The co-ordination and integration related issues are discussed generally in casestudies, addressing produce at the individual and aggregate level. Production planningof the fresh-products also have been studied on an aggregate, and in some cases ona disaggregate level. Papers discussing demand forecasting generally tried to find theprice elasticity of various products. But, papers also considered fruits/vegetables as a singleproduct in most of the cases. Other issues such as inventory management andtransportation have also been discussed, but only on an aggregate level.

The FSCM for all kinds of produce cannot follow the same strategy, as theagri-fresh produce has a high level of heterogeneity. The rate of deterioration and otherbiological conditions also vary to a high degree across produce, so storage andtransportation conditions should also be different for all the produce. Table V shows theproducts studied vis-a-vis geographical location. It is to be noted that most of the researchhas taken place in developed countries and agri-business oriented countries such asThe Netherlands.

It is also to be noted that a large number of research papers have addressedissues independent of the geography, generally taking all vegetables/fruits as a singleproduct. This was the introductory phase of research when there was a need toestablish awareness for an efficient FSCM. Countries such as India, though being one ofthe biggest agri-fresh produce producers, have paid the least attention to the issuesrelated to FSCM. Countries like the UK, the USA, China, and The Netherlands have paidthe highest attention on FSCM related research. A lot of other relevant interpretationscan be drawn from the analysis presented in Sections 4-6 discussing the classificationof the literature. Moreover, the common themes within the categories are very wellexplained though the analysis across the categories is less evident. We presented ananalysis in Appendix 2 to elaborate more on the synthesis across the categories. Thisanalysis is a snap-shot of the total literature discussed in this review. The interesting factsthat are revealed will be further taken up in the conclusion and discussion section.

Demandforecasting

Productionplanning

Inventorymanagement Transportation Others Total

All 12 5 4 8 19 48Apple 1 1Banana 2 2Bellpeppers 1 1Broccoli 1 1Flowers 2 2Fruits 1 3 4Grapes 4 4Mango 1 1Onion 1 1Orange 1 1Potato 1 1Tomato 2 1 3Vegetables 3 4 1 8 16Total 16 19 4 12 35 86

Table IV.The numbers of articles

addressing an issue for aparticular product


137

All

Ap

ple

Ban

ana

Bel

lp

epp

ers

Bro

ccol

iF

low

ers

Fru

its

Gra

pes

Man

go

On

ion

Ora

ng

eP

otat

oT

omat

oV

eget

able

sT

otal

US

A2

11

12

29

UK

61

18

Ch

ina

41

5T

heN

eth

erla

nd

s3

25

Ind

ia2

11

4C

hil

e3

3B

razi

l1

11

3V

ietn

am1

12

Sp

ain

11

2A

ust

rali

a1

12

Ru

ssia

22

Uk

rain

e1

1T

urk

ey1

1G

reec

e1

1F

inla

nd

11

Can

ada

11

Fra

nce

11

Ph

ilip

pin

es1

1S

riL

ank

a1

1T

hai

lan

d1

1S

lov

enia

11

Gen

eric

231

13

12

31T

otal

481

21

12

44

11

11

316

86

Table V.The products studiedvis-a-vis geographicallocation

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7. Discussion and conclusionThis paper presents a state-of-the-art literature review of FSCM, discussing majoroperational issues accountable for post-harvest waste. The aim is to highlight thetrends and opportunities in research addressing the FSCM. To meet this objective,literature is collected from various databases over a period of 20 years (1991-2011).The literature is systematically reviewed and classified to provide a betterunderstanding of the research in the last two decades. In order to maintain the rigorof the overall process, a structured and fit-for-purpose systematic research process isfollowed in both the collection and content analysis of the literature. The review processis inspired and guided by the principles of Mayring (2003), Tranfield et al. (2003) andRousseau et al. (2008), considering the advantages and ignoring the disadvantages of therespective processes.

A two-step process is followed for literature segmentation and synthesis. First theliterature is segmented according to the journals, publications per year, and countriesto get an overview. The journals are categorized into OM-journals, agriculture journals,and other journals. It was found that out of 86 papers, 46 were published inOM-journals and 30 were published in agriculture journals. The papers in OM-journalshave addressed the problem by applying the existing tools/techniques with very littleconsideration to the specific product characteristics, whereas the papers in agriculturejournals are more focused on the product characteristics. The result of this analysisshows the absence of a journal with the prime attention towards FSCM. It is found that,recently the number of publications per year has increased as more than 50 percent ofthe papers are published in the last five years. This trend may be contributed to theglobal factors in the last five years such as increased food and fuel prices, vegetable oilconsumption as a fuel, and the breakout of diseases such as bird flu and swine flu.These factors attracted the attention of consumers, policy makers, researchers, andpractitioners towards the lack of research in agri-fresh produce.

The literature is also segmented according to the countries and it is found thatmost of the research is in the USA, China, The Netherlands, and the UK, with only alimited attention paid to the developing countries. This is also evident from the fact that60 percent of the papers are addressing issues of developed countries while only40 percent are focused on developing countries. This is quite similar to the research trendin other disciplines with few exceptions. But, it is believed that in the coming years,there will be huge change in this trend, with a majority of research publications fromAsian countries with growing economies such as India and China. These countriesare one of the largest producers and consumers of agri-fresh produce. It is also to benoted that the highest proportion of the world’s poor are in Asia. It is expected that in thefuture there will be several changes in the consumption pattern of the populationespecially in these countries which will trigger the need for an efficient FSCM. It is foundthat the post-harvest waste reduction is a secondary objective with the primary concerntowards revenue increment in almost all of the papers. Therefore, unless the directbenefits of post-harvest waste reduction is shared among the stakeholders such asfarmers, wholesalers, and retailers it will be very difficult to implement the proposedmodels. The current trend may also be attributed to the lack of government policiesand consumer awareness to reduce post-harvest waste in developing countries.So, governments and private organizations have to put in the effort necessary to reducethe post-harvest waste to reduce the levels of poverty, hunger, and malnutrition.


139

In the second step, the literature is classified according to the problem context,methodology, and product and geographical region for an in-depth analysis of the same.The research in FSCM is in the nascent stage and is disintegrated into problem contexts.The studies have addressed the problems in isolation with little attention to theinterdependence of the problems. This phenomenon can be attributed to the fragmentednature of the FSCs itself. The lack of an integrated transportation and informationinfrastructure adds to this fragmentation. Figure 5 shows the status of FSCM is severaldeveloping countries where a large number of intermediaries exist between the farmersand consumers.

Most of the problems are generally well studied and explored for manufacturingproducts but have recently gained attention for agri-fresh produce. It is also implicit thatthe research in specific problem contexts will continue to increase in the short term.Nevertheless, with the increase in efforts, the requirement for integrative contributionswill emerge to understand the overall phenomena of FSCM. It is seen that the level ofinterest varies across the problem contexts. There are a lot of context specific papers,especially in demand forecasting and production planning. Other problems such asinventory management and transportation have been explored to some extent but veryfew studies considered the agri-fresh produce. Therefore, a huge potential exists forexploring these problems in FSCM.

In the current scenario, there is almost no information sharing among the variousstakeholders of FSCM. This leads to the mismatch of demand and supply. The lack ofefficient demand forecast is another factor contributing to this mismatch. Figure 6shows the typical scenario of agri-fresh supply chain with the missing demand andsupply link. In such cases, the transactions are through the commission agents whichtake the maximum benefits without adding any value (De Boer and Pandey, 1997).The missing information sharing infrastructure results in concealing of informationand a huge lag between consumer demand and farmers reaction to that demand.Buyukbay et al. (2011) attributed lack of demand information as one of the main reasonsfor waste. The spot market acts as an auction market where the agents of consolidators

Figure 5.FSCM representing thefragmented transportationand information sharing

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and retailers bargain and make transactions. Therefore, there is a lack of ownershipwithin the chain. All the players are concerned with their own revenue maximizationwith limited attention towards the overall profit of the chain. This lack of a holistic viewof a supply chain is leading to the post-harvest waste.

The demand forecasting literature has addressed the issues for manufacturingproducts with very less attention to agri-fresh produce. Generally, the studies haveconsidered the fresh produce as a single commodity and have tried to forecast the demand.But, there is a need to forecast the demand of agri-fresh produce on a disaggregate level.Considering the perishable and seasonal nature of the agri-fresh produce it is necessaryto study it at disaggregate level. Therefore, the challenge for researchers is to extract theinformation from various sources and to decide on an efficient forecasting techniquebuilding on the existing forecasting literature. The demand forecasting in unorganisedwholesales or spot market will be extremely complex as compared to demand forecastingin the supermarket or organized retail sector. Shukla and Jharkharia (2011) studied anARIMA model to forecast demand of onions on a daily basis. But the literature addressingdemand forecasting for agri-fresh produce is still in nascent stage. Further, thegeneralization of the proposed model across the agri-fresh produce category will beanother challenge.

As there is either an absence or a delayed input of the consumer demand to the farmers,it is found that farmers are generally following the traditional product mix despite thechange in consumption patterns. This is also evident from the analysis of the literature.The consumer demand is generally not considered in the modeling for the productionplanning decisions. Moreover, it is found that the production planning decisions aremostly focused on the use of farm land, allocation of resources, etc. (Glen, 1987) and verylittle attention is given to the losses due to inefficient harvesting. The harvest schedulingliterature for agri-fresh produce is still in its infancy with only a few papers addressingthe problem. But, there is a huge amount of literature addressing forest harvesting(Bredstrom et al., 2004), sugar cane harvesting (Grunowa et al., 2007), and food grains suchas rice harvesting (Deris and Ohta, 1990). The agri-fresh produce harvesting problemmay be attempted by incorporating the produce characteristics to the literature of foodgrain harvesting. It is also found that most of the papers have proposed mathematicalmodels but there is a low utilization of the models in other situations. These models aregenerally solved by software such as GAMS and AMPL using Cplex solver. Given thecurrent complexity of the models, it may be possible. But, the model incorporating reallife and business constraints will present a major difficulty and may need heuristicsand meta-heuristics to solve.

Figure 6.Current status

of agri-fresh supply chainsin developing countries


141

There is also very less integration between the production planning and inventorymanagement literature for agri-fresh produce. The inventory literature, strives to reducethe waste, but the main focus is on consumer satisfaction and revenue maximization.The literature on agri-fresh produce can gain heavily from the deteriorating productinventory management literature. It is also found that there is a low integration of theinventory literature to the demand forecasting and transportation literature even thoughthe mathematical models in inventory management use factors such as consumerdemand and transportation lead time. But, to reduce the complexity of the models, thesefactors are assumed as either constant or as stochastic. There is almost no use of real lifevalues of consumer demand and transportation lead times in modeling the inventoryproblem. Therefore, there is a need for mathematical models using these factors andsolution techniques that can produce real time results for complex models.

Transportation is one of the most developed and research topics in OM/research.Till now there are a lot of established heuristics for a large number of transportationproblems. But, the use of these in the transportation of agri-fresh produce is very little.Manikas and Terry (2010) emphasised that there exists a need for research in thedistribution of fresh fruits and vegetables. Generally, the transportation literatureaddresses milk or meat transportation with little attention to the characteristics ofagri-fresh produce. It is also observed that only few researchers attempted the deliveryof processed food by using the VRP literature. Researchers and practitioners can makeuse of the existing literature from these examples and can propose robust models fortransportation of agri-fresh produce. Moreover, there is a very little application ofadvanced solution techniques to the transportation problem for agri-fresh produce. It isrequired to apply the artificial intelligence techniques to find real time solutions forthese problems. Though, the transportation literature strives to reduce the total cost,it is not much concerned about reducing post-harvest waste. This is a very significantfactor and shall be incorporated with the other factors such as the distance traveled,and the time taken into mathematical modeling.

The literature was classified according to the applied methodologies to find out thetheoretical orientation of the field as a whole. From this analysis, it is revealed that mostof the problems were solved using mathematical modeling and simulation. Methodssuch as case studies and empirical analysis are confined to areas such as problemidentification and forecasting. Taking into account the relative infancy of the field, it isexpected that in the future there will be an increase in use of other methodologies.Even cross methodological approaches are expected considering the fragmented natureof the problems. Best practices from the practitioners have not emerged in the papers.This shows the lack of universally accepted practices and the complex nature of theproblem. With the increase in literature it is also assumed that best practices andadvanced techniques will emerge as in the manufacturing literature.

The classification according to the produce shows that, in a majority of the casesall the agri-fresh produce is assumed as a single commodity, with only limited attentionto the individual product characteristics. It is very important to study the produce atthe individual level given its perishable and seasonal nature. The consumption habitsand climate of any geographical location plays an important role in deciding the relativeimportance of the agri-fresh produce to that particular region. Produce that have aninternational demand such as banana, grapes, oranges, potato, tomato, etc. have gotattention on an individual level. Most of the vegetables are generally treated in groups

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due to their high level of substitutability and low profit margins. The study of literatureacross the geographies reveals that there is an increase in FSCM research but it is mainlylimited to a few countries. It is interesting to note that these countries have almost thesame ranking for research in all other disciplines (www.scimagojr.com). There are afew exceptions such as The Netherlands due to its commitment towards agriculture.There is a need for research to be replicated and studied in the developing nations whichare among the biggest suppliers and consumers of agri-fresh produce.

The classification provided in this paper may be useful in understanding theFSCM from a holistic perspective. The mapping of problem context to methodology andto product shows a clear picture of the link of product-problem-methodology. Thismapping may be used as a framework to facilitate the work of managers and researchersaddressing the FSCM. It may serve as a frame of reference to decide a suitablemethodology for a given problem context. The mapping of products to geographiesgives the insights about the real-life problems. The problem-methodology mappinghelps in understanding the way in which these problems are addressed in other parts ofthe world. This can help the practitioners analyze the similarities and differences fromother contexts and guide them to build, modify, and practice new solutions.

FSCM is an emerging area and offers a lot of opportunities for applying theestablished methodologies to new problems. The problems are different, complex,and challenging, due to the large number of associated variables and parameters.It is also suggested to formulate a combination of various tool and techniques toaddress problems. It is for sure that in the coming years a large number of changes willbe seen in the concepts, technologies, and management practices of the agri-freshproduce SCM.

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Further reading

Adebanjo, D. and Mann, R. (2000), “Identifying problems in forecasting consumer demand in thefast moving consumer goods sector”, Benchmarking: An International Journal, Vol. 7 No. 3,pp. 223-30.

Carbonneau, R., Laframboise, K. and Vahidov, R. (2008), “Application of machine learningtechniques for supply chain demand forecasting”, European Journal of OperationalResearch, Vol. 184 No. 3, pp. 1140-54.

Church, K.B. and Curram, S.P. (1996), “Forecasting consumers’ expenditure: a comparisonbetween econometric and neural network models”, International Journal of Forecasting,Vol. 12 No. 2, pp. 255-67.

De Gooijer, J.G. and Hyndman, R.J. (2006), “25 years of time series forecasting”, InternationalJournal of Forecasting, Vol. 22 No. 3, pp. 443-73.

Fildes, R., Nikolopoulos, K., Crone, S.F. and Syntetos, A.A. (2008), “Forecasting andoperational research: a review”, Journal of the Operational Research Society, Vol. 59No. 9, pp. 1150-72.

Gaur, V., Giloni, A. and Seshadri, S. (2005), “Information sharing in a supply chain under ARMAdemand”, Management Science, Vol. 51 No. 6, pp. 961-9.

Gilbert, K.C. (2005), “An ARIMA supply chain model”,Management Science, Vol. 51 No. 2, pp. 305-10.

Gilbert, K.C. and Chatpattananan, V. (2006), “An ARIMA supply chain model with a generalizedordering policy”, Journal of Modeling in Management, Vol. 1 No. 1, pp. 33-51.

Liang, W. and Huang, C. (2006), “Agent-based demand forecast in multi-echelon supply chain”,Decision Support Systems, Vol. 42 No. 1, pp. 390-407.


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Rodriguez, R.R., Escoto, R.P., Bru, J.M. and Bas, A.O. (2008), “Collaborative forecastingmanagement: fostering creativity within the meta-value chain context”, Supply ChainManagement: An International Journal, Vol. 13 No. 5, pp. 366-74.

Sivillo, B.J. and Reilly, D.P. (2004), “Forecasting consumer product demand with weather information:a case study”, The Journal of Business Forecasting and Systems, Vol. 23 No. 4, pp. 7-10.

Staff of World Resources Program (1998), “Disappearing food: how big are postharvest losses”,Earth Trends: Featured Topic.

Zhang, X. (2004), “Evolution of ARMA demand in supply chains”, Manufacturing & ServiceOperations Management, Vol. 6 No. 2, pp. 195-8.

About the authorsManish Shukla is a Doctoral Student in Operations Management area at Indian Institute ofManagement Kozhikode. He has done his Bachelor’s in Manufacturing Engineering fromNational Institute of Foundry and Forge Technology Ranchi (India). He is currently working onfresh supply chain management. His papers have been published in the International Journal ofProduction Research and International Journal of Operations Research.

Sanjay Jharkharia is Associate Professor in Operations Management at Indian Institute ofManagement Kozhikode (India). His Master’s is in Mechanical Engineering from Banaras HinduUniversity, Varanasi and PhD in Management from Indian Institute of Technology Delhi. Hisresearch interests include operations management, supply chain management, fresh supplychain, Six Sigma, etc. He has published papers in International Journals such as Omega, SupplyChain Management: An International Journal, Enterprise Information Management,International Journal of Operations Research, etc. Sanjay Jharkharia is the correspondingauthor and can be contacted at: [email protected]

To purchase reprints of this article please e-mail: [email protected] visit our web site for further details: www.emeraldinsight.com/reprints

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Ch

enet

al.

(200

9)G

ener

icA

llT

ran

sM

od4

No

Ch

ern

and

Wan

g(1

994)

Ch

ina

All

DF

ES

24N

oC

onn

eret

al.

(200

9)U

SA

All

Oth

ers

ES

8N

oC

orb

ett

(199

3)U

kra

ine

Veg

etab

les

Oth

ers

AR

0N

oD

arb

y-D

owm

anet

al.

(200

0)G

ener

icV

eget

able

sP

PM

od17

Yes

Dev

ados

san

dL

uck

stea

d(2

010)

US

AA

pp

leP

PM

od0

No

Duet

al.

(200

9)G

ener

icA

llO

ther

sC

S6

No

Du

nn

e(2

008)

Au

stra

lia

All

Oth

ers

CS

5N

oE

cker

t(2

007)

Gen

eric

All

IME

S0

Yes

Fan

etal.

(199

4)C

hin

aA

llD

FE

S53

Yes

Fea

rne

and

Hu

gh

es(1

999)

UK

All

Oth

ers

ES

96N

oF

erre

ret

al.

(200

8)C

hil

eG

rap

esP

PM

od7

No

Gri

msd

ell

(199

6)U

KB

rocc

oli

Oth

ers

CS

14Y

esH

alb

ren

dtet

al.

(199

4)C

hin

aA

llD

FE

S55

No

Ham

er(1

994)

Gen

eric

Veg

etab

les

PP

Mod

5Y

es

(continued

)

Table AI.


155

Au

thor

(s)

Geo

gra

ph

yP

rod

uct

Pro

ble

mM

eth

odol

ogy

Cit

atio

ns

Au

thor

(s)

wit

hp

rofe

ssio

nal

affi

liat

ion

(s)

Hig

gin

set

al.

(201

0)G

ener

icA

llO

ther

sG

ener

al0

No

Hsu

etal.

(200

7)G

ener

icA

llT

ran

sM

od18

No

Jon

es(1

993)

Ru

ssia

All

Tra

ns

Gen

eral

4N

oJo

shiet

al.

(200

9)In

dia

All

Oth

ers

ES

3N

oK

anch

anas

un

torn

and

Tec

han

itis

awad

(200

6)T

hai

lan

dA

llIM

Sim

13N

oK

lein

and

Pet

ti(2

006)

Gen

eric

All

Oth

ers

Mod

18N

oK

um

ar(2

008)

Gen

eric

All

Tra

ns

ES

5N

oL

iet

al.

(200

7)G

ener

icA

llIM

Mod

8N

oL

iuet

al.

(200

1)G

ener

icA

llD

FE

S30

Yes

Lod

ree

Jran

dU

zoch

uk

wu

(200

8)U

SA

Veg

etab

les

PP

Mod

2N

oL

oren

tz(2

008)

Ru

ssia

All

Tra

ns

Mod

8N

oL

oren

tzet

al.

(200

7)G

ener

icA

llD

FC

S14

No

Mai

aet

al.

(199

7)B

razi

lB

anan

aO

ther

sM

od5

Yes

Mal

agaet

al.

(200

1)U

SA

Veg

etab

les

DF

ES

29N

oM

anik

asan

dT

erry

(201

0)U

KA

llT

ran

sC

S2

No

Mat

opou

loset

al.

(200

7)G

reec

eV

eget

able

sO

ther

sC

S22

No

Mer

gen

thal

eret

al.

(200

9)V

ietn

amA

llD

FE

S10

Yes

Mik

kol

a(2

008)

Fin

lan

dV

eget

able

sO

ther

sC

S11

No

Mil

leret

al.

(199

7)U

SA

Tom

ato

PP

Mod

16Y

esM

urt

hyet

al.

(200

9)In

dia

Fru

its

Tra

ns

CS

0N

oM

utu

cet

al.

(200

7)P

hil

ipp

ines

Veg

etab

les

DF

ES

2N

oO

sval

dan

dS

tirn

(200

8)S

lov

enia

Veg

etab

les

Tra

ns

Mod

10N

oP

erer

aet

al.

(200

4)S

riL

ank

aV

eget

able

sO

ther

sC

S0

No

Pra

do-

Pra

do

(200

9)S

pai

nA

llO

ther

sC

S0

No

Rei

ner

and

Trc

ka

(200

4)G

ener

icA

llO

ther

sS

im40

No

Rid

outtet

al.

(201

0)A

ust

rali

aM

ang

oO

ther

sE

S2

No

Ron

get

al.

(201

1)G

ener

icB

ell

pep

per

sT

ran

sM

od4

No

Sae

dtet

al.

(199

1)T

he

Net

her

lan

ds

All

PP

Mod

3Y

esS

agh

eeret

al.

(200

9)In

dia

All

Oth

ers

Gen

eral

0N

oS

alin

(199

8)G

ener

icA

llO

ther

sG

ener

al38

No

Sal

inan

dN

ayg

a(2

003)

US

AP

otat

oT

ran

sC

S7

No

Sh

uk

laan

dJh

ark

har

ia(2

011)

Ind

iaO

nio

nD

FE

S0

No

Sør

ense

nan

dB

och

tis

(201

0)G

ener

icA

llP

PE

S10

No (c

ontinued

)

Table AI.

IJOPM33,2

156

Au

thor

(s)

Geo

gra

ph

yP

rod

uct

Pro

ble

mM

eth

odol

ogy

Cit

atio

ns

Au

thor

(s)

wit

hp

rofe

ssio

nal

affi

liat

ion

(s)

Sto

kes

etal.

(199

7)U

SA

All

PP

Mod

0N

oS

win

nen

and

Mae

rten

s(2

007)

Gen

eric

All

Oth

ers

ES

22N

oT

aylo

r(2

005)

UK

All

Oth

ers

AR

27N

oT

aylo

r(2

006)

UK

All

DF

CS

5N

oT

aylo

ran

dF

earn

e(2

006)

UK

All

DF

CS

18N

oT

hro

net

al.

(200

7)G

ener

icA

llO

ther

sS

im2

No

Tri

enek

enset

al.

(200

8)G

ener

icF

ruit

sO

ther

sE

S7

Yes

Van

Ber

lo(1

993)

Th

eN

eth

erla

nd

sV

eget

able

sP

PM

od4

No

Van

der

Vor

stan

dB

eule

ns

(199

9)T

he

Net

her

lan

ds

All

Tra

ns

Mod

8N

oV

and

erV

orst

and

Beu

len

s(2

002)

Gen

eric

All

Oth

ers

CS

94N

oV

and

erV

orst

etal.

(199

8)T

he

Net

her

lan

ds

All

DF

Sim

68N

oV

and

erV

orst

etal.

(200

0)T

he

Net

her

lan

ds

Veg

etab

les

Oth

ers

Sim

88N

oV

anD

onket

al.

(200

8)G

ener

icA

llO

ther

sC

S9

No

Ver

dou

wet

al.

(201

0)G

ener

icF

ruit

sO

ther

sE

S1

Yes

Wei

ntr

aub

and

Rom

ero

(200

6)G

ener

icA

llP

PG

ener

al18

No

Wid

odoet

al.

(200

6)G

ener

icF

low

ers

PP

Mod

3N

oW

ilso

n(1

996a

)U

KB

anan

aO

ther

sC

S37

No

Wil

son

(199

6b)

Gen

eric

All

Oth

ers

Gen

eral

22N

oW

uet

al.

(199

5)C

hin

aA

llD

FE

S33

No

Zu

urb

ier

(199

9)G

ener

icA

llO

ther

sC

S13

No

Notes:

DF

,d

eman

dfo

reca

stin

g;

Mod

,m

odel

ing

;P

P,

pro

du

ctio

np

lan

nin

g;

Sim

,si

mu

lati

on;

IM,

inv

ento

rym

anag

emen

t;E

S,

emp

iric

alst

ud

ies;

Tra

ns,

tran

spor

tati

on;

CS

,ca

sest

ud

y;

AR

,ac

tion

rese

arch

Table AI.


157

Appendix 2

Pro

ble

mco

nte

xt

Dem

and

man

agem

ent

Pro

du

ctio

np

lan

nin

gIn

ven

tory

man

agem

ent

Tra

nsp

orta

tion

Issu

eF

orec

asti

ng

Har

ves

tsc

hed

uli

ng

Ord

erin

g/r

etri

eval

pol

icy

Veh

icle

rou

tin

gp

rob

lem

(VR

P)

Th

eore

tica

lb

ase

For

ecas

tin

gli

tera

ture

Sch

edu

lin

gli

tera

ture

Det

erio

rati

ng

inv

ento

ryli

tera

ture

VR

Pli

tera

ture

Pro

du

cest

ud

ied

Ind

ivid

ual

/gro

up

Ind

ivid

ual

Ind

ivid

ual

Ind

ivid

ual

Met

hod

Em

pir

ical

stu

die

sM

ath

emat

ical

mod

elin

g/

sim

ula

tion

Mat

hem

atic

alm

odel

ing

Mat

hem

atic

alm

odel

ing

Too

lA

IDS

/AR

IMA

/AN

NL

P,

MIL

P,

IP,

SP

,D

P,

SD

LP

,M

ILP

LP

,IP

Sol

uti

onS

PS

S/L

IND

EP

Heu

rist

ic,

CP

LE

X/G

AM

S/A

MP

LH

euri

stic

,C

PL

EX

,G

AM

SH

euri

stic

,m

eta

heu

rist

ics,

CP

LE

XM

ajor

fact

ors

Sal

es,h

olid

ays,

pri

ce,w

eath

er,

spec

ial

dis

cou

nt

Sal

esp

rice

,dem

and

,mat

ura

tion

,d

eter

iora

tion

,le

adti

me,

wea

ther

Dem

and

,det

erio

rati

on,l

ead

tim

e,h

old

ing

cost

,or

der

ing

cost

,ca

pac

ity

Dem

and

,d

eter

iora

tion

,p

enal

ty,

dis

tan

ce,

trav

elco

st,

nu

mb

erof

veh

icle

sD

ata

Rea

lli

feR

eal

life

/sim

ula

ted

/lit

erat

ure

Rea

lli

fe/s

imu

late

dR

eal

life

/ben

chm

ark

pro

ble

ms

Loc

atio

nD

evel

oped

/dev

elop

ing

cou

ntr

ies

Mos

tly

dev

elop

edco

un

trie

sM

ostl

yd

evel

oped

cou

ntr

ies

Mos

tly

dev

elop

edco

un

trie

s

Des

ired

outc

ome

Op

tim

alfo

reca

stH

arv

est

sch

edu

leR

etri

eval

pol

icy

/ord

erin

gp

olic

yR

oute

sfo

rv

ehic

les

Per

form

ance

eval

uat

ion

MA

PE

,R

MS

E,

MM

EB

ench

mar

kfr

omli

tera

ture

Ben

chm

ark

from

lite

ratu

reB

ench

mar

kfr

omli

tera

ture

Mai

nC

once

ntr

atio

nP

olic

ym

akin

gR

even

ue

Rev

enu

e/cu

stom

ersa

tisf

acti

onR

even

ue/

pen

alty

cost

Au

die

nce

Pu

bli

cp

olic

ym

aker

sF

arm

ers

Ret

aile

rs/w

hol

esal

ers

Wh

oles

aler

s/tr

ansp

orte

rsL

imit

atio

nG

rou

pin

gan

dag

gre

gat

efo

reca

stIm

por

tan

tfa

ctor

ssu

chas

dem

and

,m

atu

rati

on,

etc.

are

mis

sin

g.

Lac

kof

effi

cien

tso

luti

onte

chn

iqu

e

No

con

sen

sus

onin

ven

tory

pol

icy

,re

trie

val

pol

icy

,d

eter

iora

tion

rate

not

pro

per

lyad

dre

ssed

Det

erio

rati

onra

ten

otin

clu

ded

,ot

her

var

iab

les

not

incl

ud

ed

Pu

bli

cati

onfo

cus

Eco

nom

ics

jou

rnal

sO

R/p

rod

uct

ion

jou

rnal

sO

M/O

Rjo

urn

als

Tra

nsp

orta

tion

jou

rnal

s

Table AII.

IJOPM33,2

158

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