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Accepted ManuscriptAgribusiness Supply Chain Risk Management: A Review ofQuantitative Decision ModelsGolnar Behzadi, Michael Justin O’Sullivan, Tava Lennon Olsen,Abraham 6/j.omega.2017.07.005OME 1804To appear in:OmegaReceived date:Revised date:Accepted date:2 June 201625 June 20178 July 2017Please cite this article as: Golnar Behzadi, Michael Justin O’Sullivan, Tava Lennon Olsen,Abraham Zhang, Agribusiness Supply Chain Risk Management: A Review of Quantitative DecisionModels, Omega (2017), doi: 10.1016/j.omega.2017.07.005This is a PDF file of an unedited manuscript that has been accepted for publication. As a serviceto our customers we are providing this early version of the manuscript. The manuscript will undergocopyediting, typesetting, and review of the resulting proof before it is published in its final form. Pleasenote that during the production process errors may be discovered which could affect the content, andall legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPTHighlights Providing the first literature review of risk management models specifically for agribusinesssupply chains. Focusing on specific sources of uncertainty in agribusiness industries. Providing new implications and further directions for developing the research in the context ofagribusiness supply chain risk management.CRIPT Providing the first literature review of risk management models specifically for agribusinessACCEPTEDMANUSsupply chains.1

ACCEPTED MANUSCRIPTAgribusiness Supply Chain Risk Management: A Review ofQuantitative Decision ModelsGolnar Behzadi*a , Michael Justin O’Sullivana , Tava Lennon Olsenb , Abraham Zhangc,daCRIPTDepartment of Engineering Science, Faculty of Engineering, The University of Auckland, Auckland 1010, NewZealandbInformation Systems and Operations Management, Business School, The University of Auckland, Auckland 1010,New ZealandcAuckland University of Technology (AUT) Business School, AUT, Auckland 1010 New ZealanddDepartment of Management Systems, University of Waikato Management School, Hamilton 3240, New ZealandAbstractSupply chain risk management is a large and growing field of research. However, within this field,mathematical models for agricultural products have received relatively little attention. This is somewhat surprising as risk management is even more important for agricultural supply chains due toANUSchallenges associated with seasonality, supply spikes, long supply lead-times, and perishability. Thispaper carries out a thorough review of the relatively limited literature on quantitative risk management models for agricultural supply chains. Specifically, we identify robustness and resilience as twokey techniques for managing risk. Since these terms are not used consistently in the literature, weMpropose clear definitions and metrics for these terms; we then use these definitions to classify theagricultural supply chain risk management literature. Implications are given for both practice andEDfuture research on agricultural supply chain risk management.1. IntroductionPTKeywords: Agribusiness supply chain, risk management, robust, resilientCEIn the past two decades, supply chain risk management (SCRM) has emerged as an important research topic [1]. Several reasons are behind this development: 1) globalization has made supply chainsAClonger and more complex; consequently, supply chains are now exposed to more risks and have become more vulnerable; 2) the lean management philosophy has become widely implemented in manyindustries; this philosophy advocates waste elimination/minimization and embraces just-in-time production/logistic; although it improves supply chain efficiency, the removal/reduction of redundancieshas resulted in greater supply chain vulnerability under adverse events; and 3) the world has paidincreasing attention to the many supply chain disruptions that have been caused by catastrophicevents (e.g., [2, 3, 4, 5]).Preprint submitted to Elsevier27th July 2017

ACCEPTED MANUSCRIPTAgribusiness plays an indispensable role in the world’s economy as a key source of food supplies.Agribusiness products have three specific characteristics that make risk management for agribusinesssupply chains (ASCs) more complicated when compared to risk management for typical manufacturing supply chains. These characteristics are seasonality, supply spikes (sometimes referred to as“bulkiness”), and perishability. Dealing with seasonality requires planning as growth is seasonalwhereas consumption is throughout the year. Further, most agricultural products have long supplylead times that cannot be easily altered against nature. Harvesting and post-harvest activities, including packing, processing, storage, and transportation, can be very demanding because of supplyCRIPTspikes. Furthermore, there is often significant time pressure on post-harvest activities as most agricultural products are perishable. Also, because of the perishability, there is a need for specific handling,storage, and inventory management. If not properly managed, a delay in transportation may causeANUSsubstantial loss of product value.In addition to product specific characteristics, risk management is important for ASCs because theyoften involve more sources of uncertainties than manufacturing supply chains [6]. In an ASC, thesupply process is related to biological production (food crops, meat, etc.), which is affected by weathervariability (e.g., droughts), disease (e.g., Psa kiwifruit disease), and pests (e.g., locusts). Such factorsMimply that both harvest levels and harvest times are subject to uncertainties. In addition, thesefactors can impact on the quality of the produce. In particular, in the processing stage, there areEDspecial risks associated with food quality and food safety (e.g., botulism risks). These uncertaintiesmake ASCs more vulnerable than typical manufacturing supply chains. Furthermore, recent practicesPTin agribusiness have added to the complexity of ASCs, thus making the application of risk management strategies more critical [7]. Such practices include the use of new marketing strategies (e.g., inCEproduct differentiation/proliferation) and the interlinked design of global supply chains [7].ACThis paper fills a gap in the literature by providing a review of quantitative models for ASC riskmanagement. Our focus is on risks at the supply chain level, and related risk management methodologies for ASCs that foster resilience and robustness, terms that we will carefully define. We reviewdifferent quantitative risk management (RM) approaches that provide resilience and robustness fora variety of agricultural products. As pointed out by [3] and [1], there are a lot of inconsistencies inthe meanings of SCRM terms. One contribution of this review is to suggest metrics for resilience androbustness.3

ACCEPTED MANUSCRIPTThe remainder of this paper is organized as follows. Section 2 outlines the scope of our reviewin Agribusiness Supply Chain Risk Management (ASCRM) and reviews related survey papers. Thekey concepts and terms in this review are defined in Section 3. Section 4 classifies the availablemodeling studies in ASCRM according to different aspects of product type, risk types, risk measures,and RM strategies (i.e., robustness and resilience). In this section, modeling approaches are furtheranalyzed for different types of agricultural products. At the end of Section 4, a specific overall summary of the section is provided that identifies gaps in the research literature. The paper is concludedCRIPTin Section 5 by proposing directions for future research.2. LiteratureApplications of quantitative models in agricultural problems date back to the 1950s and have beenaddressed widely in the literature [8, 7]. Modeling approaches in agribusiness have been predomin-ANUSantly used for problems related to transportation, distribution, harvesting, facility location, and farmplanning (e.g., [9, 10, 11, 12]), with a specific focus on farm planning problems. Key considerationsin agricultural problems (i.e., yield, harvest time, demand, etc.) are influenced by different sourcesof uncertainty such as weather conditions, animal or crop diseases, and price variability. Although,as described in the following, there are separate and extensive review studies on both quantitativeMrisk management and agribusiness models, we are not aware of any review paper thus far on SCRMEDmodels in agribusiness, which is the topic of our review.Articles [13], [14], [15], [16], [5], [1], and [17] reviewed the bulk of the quantitative SCRM liter-PTature, mostly in the context of manufacturing industries. Agribusiness decision models have beenreviewed in the areas of production, harvesting, and distribution [8, 7, 18, 19], facility locations [11],CEsupply-side resource utilization [20], ASC planning challenges [21], and operational issues that result in post-harvest waste [22]. Further, [23] reviewed quantitative ASC models in the contexts of:ACplanting, harvesting, production, distribution, and inventory; [24] extended the review of agribusinessproblems (in the context of supply chains) in considering factors of uncertainty. However, neither ofthe review papers discuss risk management strategies, so cannot be considered as reviews of SCRM.Thus, as depicted in Figure 1, to the best of our knowledge, there is no review specific to quantitativemodels in the joint area of SCRM and agribusiness. As mentioned above, this overlap will be coveredby our review paper.4

ACCEPTED MANUSCRIPTFigure 1: Venn diagram on the research gap in literature reviews/survey papers[13] [14] [15][16][5] [1][17]SCRM[8][25][11] [7][20][18][21][19][22][23] [24]1AgribusinessANUSCRIPTThe scope ofthis review paperNote that risk management is not new to agribusiness planning. For instance, [26, 27] introduced thebasic concepts of risk management in agriculture. Further, [25] reviewed farm decision-making underrisk from several aspects such as utility functions, farmer risk preferences, and response approachesMto both short-term and long-term uncertainty. However, the main concern of these aforementionedstudies was farm level risks and uncertainties, whereas we have focused on risks at the supply chainEDlevel.PTWe reviewed papers from different journals in Operations Management (OM), Operations Research(OR), Supply Chain Management (SCM), and agriculture. We searched the Scopus database usingCEcombinations of keywords including “risk management,” “quantitative risk management,” “supplychain,” “operations research,” and “agribusiness”. In addition, we went through all the papers sur-ACveyed in the review papers from Figure 1 for SCRM papers with an agribusiness application (left handside survey papers) and agribusiness papers that focused on SCRM (right-hand-side survey papers).We defined supply chain broadly as any paper that modeled multiple locations or firms.We believe [28], published in 1993, is the first quantitative study in the field of agribusiness thatconsidered risks in supply chains, although without directly referring to the term “supply chain.”1Article [24] reviews agribusiness problems in supply chain structures and includes uncertainty attributes; however,as risk management strategies have not been clearly discussed in this review, the review has not been categorized underthe SCRM section.5

ACCEPTED MANUSCRIPTBefore that, risks in agribusiness had only been discussed at the farm level (see [26]). Article [28]studied a vegetable processing supply chain problem with two echelons that consider production,trimming, and processing decisions under uncertain climatic factors. In contrast with [28], most ofthe reviewed papers after 2000 have explicitly referred to the term supply chain in their studies.As 1993 appeared to us to be a late date for a first study, we carried out further research on the timingof the field. The term “supply chain management” appears to have first been used in an interviewin the Financial Times in 1982 [29]. However, the concept of multi-echelon inventory control wasCRIPTaddressed well before the introduction of “supply chain management (SCM)” [29]. Our search on“multi-echelon” and “agribusiness” yielded no RM papers earlier than [28]. It appears that when aflurry of articles and books came out on the subject of SCM in the mid-1990s, the concept began to beused in other fields of study, such as agribusiness. However, a recent review on SCRM indicates thatANUSthe concept of risk management has still received noticeably less attention in the field of agribusinessand biological sciences compared to fields such as engineering, decision sciences, and business [1].The latter statement has been supported by the findings of our review that particularly focused onagribusiness supply chain risk management studies. Table 1 lists the main issues addressed in theliterature of quantitative SCRM modeling in agribusiness from 1993 until the present, ordered byACCEPTEDMdecreasing publication date.6

ACCEPTED MANUSCRIPTTable 1: Summary review of the literature in quantitative ASC risk managementReference[30][31, 32][33]Single-period, multi-product food production planning model (with applications incocoa/wheat/palm oil/corn/soybean supply chains) that maximizes the expectedprofit of the processing firm by determining the procurement policy under fixed proportional production.Handling model for an export-oriented Canadian wheat supply chain that providessafety and quality assurance under minimum farmers’ total cost including cost of: lossat test point, contamination penalty, and risk control effort.Supermarket-farmer coordination model in an agricultural commodity supply chainthat distributes the profit and improves its effectiveness.Supply planning model for linseed oil processor in a polymers production supply chainthat maximizes the expected profit under raw material quantity/quality and marketdemand uncertainty.Buyer-backup supplier coordination model that maximizes the expected profit bydetermining the buyer firm’s reserve quantity and the backup supplier’s installedcapacity in a single-period (short-life) food supply chain.Multi-period capacity management model that maximizes the expected revenue ofan agri-food processor (the palm oil mill) by determining processing/storage capacityinvestments for the first period and periodic inventory decisions for the followingperiods.Production, transportation, and marketing model that minimizes the expected totalcost of production by determining the delivery waiting time for the final product andthe processing time of production in a perishable fresh-crop supply chain.Post-harvest logistics management model for respiring, deteriorating fresh crops thatmaximizes the total expected inventory and shortage costs, by determining proper lotsizes for finished products in RTIs (return transport items) and selling price duringthe deter

ACCEPTED MANUSCRIPT ACCEPTED MANUSCRIPT Agribusiness Supply Chain Risk Management: A Review of Quantitative Decision Models Golnar Behzadi* a, Michael Justin O'Sullivan a, Tava Lennon Olsen b, Abraham Zhang c,d a Department of Engineering Science, Faculty of Engineering, The University of Auckland, Auckland 1010, New Zealand