an integrated approach based-structural modeling for risk prioritization in supply network...
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An integrated approach based-structural modeling for riskprioritization in supply networkmanagementWafik Hachichaab & Manel Elmsalmica Unit of Mechanic, Modeling and Production (U2MP), EngineeringSchool of Sfax, University of Sfax, Sfax, Tunisiab Higher Institute of Industrial Management of Sfax, University ofSfax, Sfax, Tunisiac Unit of Logistic, Industrial and Quality Management (LOGIQ),Higher Institute of Industrial Management of Sfax, Sfax, TunisiaPublished online: 30 Sep 2013.
To cite this article: Wafik Hachicha & Manel Elmsalmi , Journal of Risk Research (2013):An integrated approach based-structural modeling for risk prioritization in supply networkmanagement, Journal of Risk Research
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An integrated approach based-structural modeling for riskprioritization in supply network management
Wafik Hachichaa,b* and Manel Elmsalmic
aUnit of Mechanic, Modeling and Production (U2MP), Engineering School of Sfax,University of Sfax, Sfax, Tunisia; bHigher Institute of Industrial Management of Sfax,University of Sfax, Sfax, Tunisia; cUnit of Logistic, Industrial and Quality Management(LOGIQ), Higher Institute of Industrial Management of Sfax, Sfax, Tunisia
(Received 20 May 2013; final version received 6 August 2013)
Supply networks are complex and suffer always from various risks. An effectivesupply chain management requires suitable strategies to mitigate them. In previ-ous literature, there has been a range of research into risk in firms but little insupply networks. This can be explained due to the huge number of risk variablesand their direct and indirect interrelations that may suffer all supply chain part-ners (firms). Therefore, for better risk mitigation, a risk prioritization step is vital.To this end, the purpose of this paper is to propose a new integrated approachbased on two structural modeling tools. Firstly, interpretive structural modelinghas been used to present a hierarchical model showing the interrelationshipsbetween the risk sources. Secondly, MICMAC analysis has been used to quan-tify and classify the risk variables based on their mutual influence and depen-dence. The objective is to ascertain the key risk variables and theirsrelationships. These prioritized risk variables provide a useful tool to supply net-work managers to focus on those key variables that are most essential for effec-tive risk management strategies. A real case study in food industry is providedin order to illustrate the application of the proposed approach. The findings maybe useful to the practitioners in risk management and may also interest academi-cians, since the method used here can be applied in other areas of industrialmanagement as well.
Keywords: supply network; supply chain risk management; risk prioritization;integrated approach; structural modeling; interpretive structural modeling; ISM;MICMAC method
1. Introduction
Market globalization, intensifying competition and an increasing emphasis on cus-tomer orientation are regularly cited as catalyzing the surge in interest in supplychain management (SCM). In addition, an effective SCM is treated as key to build-ing a sustainable competitive edge through improved inter- and intra-firm relation-ships (Ellinger 2000). Supply chains comprise all activities associated with the flowand transformation of goods from the raw material stage through to the end-user. Arange of benefits has been attributed to SCM including reduced costs, increased mar-ket share and sales, and solid customer relations. Current business trends are leadingto complex, dynamic supply networks. One consequence is that risk is increasing,
*Corresponding author. Email: [email protected]
© 2013 Taylor & Francis
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and shifting around supply networks. However, there are a variety of definitions forrisk in the literature. Mainly, risk takes into account two aspects: the uncertaintyabout and the severity of the consequences of an activity having a value for humanbeings (Aven and Renn 2009).
For the success of supply chain, the method to find ways of mitigating supplychain risks is critical to manage supply chain in unstable environment. The supplychain risks can have significant impact on the firm’s short-term and long-term per-formance. Managers need to identify and manage risks from a more diverse range ofsources and contexts. This process has become known as supply chain risk manage-ment (SCRM).
In the past, when firms manufactured in-house, sourced locally and sold direct tothe customer, risk was less diffused and easier to manage (Harland, Brenchley, andWalker 2003). With the advent of increased supply chain complexity, and outsourcingof supply networks across international borders, risk is increasing and the location ofrisk has shifted through complex changing supply networks. Consequently, recentresearch stresses the importance of an integrated and holistic approach in SCRMbecause a narrow view on a single focal firm cannot take into consideration the manyinterrelations of global supply chains (Buhman, Kekre, and Singhal 2005). A key fea-ture of supply chain risk is that, by definition, it extends beyond the boundaries of thesingle firm and, moreover, the boundary spanning flows can become a source of sup-ply chain risk (Jüttner 2005). Thus, to assess supply chain risk exposures, companiesmust identify not only direct risks of their operations, but also the potential causes orsources of those risks at every significant link along the supply chain (Faisal 2009).
This paper has two main contributions. The first concerns the development of anintegrated approach to risk management in supply networks, which is extremelyimportant because the following reasons (Faisal 2009). (1) Examining risk variablesin isolation makes it difficult to understand their interactions. (2) There may be anincrease in risk management costs, since firms may unnecessarily hedge certain risksthat are in reality offset by others. (3) A fragmented approach to risk managementalso increases the likelihood of ignoring important risks. (4) Even for known risks,it is important to consider their overall impact to the entire organization. Otherwisemitigation attempts may only introduce new risks, or shift the risk to less visibleparts of the organization. (5) Failure to consider risk interactions can also causefirms to grossly underestimate their risk exposures. Moreover, in previous literature,there has been a range of research into risk in firms but little in supply networks.This can be explained due to the huge number of risk variables and their direct andindirect interrelations that may suffer all supply chain partners (firms). Therefore, forbetter risk mitigation, a risk prioritization step is vital. In fact, supply chain networksare progressively increasing in size and complexity. Problems concerned with theireffective management can no longer be handled by classical methods suitable forhard systems, since many problems associated with these are ill-defined and unstruc-tured. In many cases, defining system boundaries and designation of system objec-tives are itself problematic (Klir 1969).
The second contribution concerns the use of structural modeling tools in SCRM.In fact, SCRM in supply networks can be viewed as a messy situation. Such messysituation, which is originally defined by Flood and Carson (1988), can be effectivelyhandled by new methodologies which aggregate knowledge of knowledgeable per-sons. The rationale behind using such methodologies is that it stands to reason thatthe knowledge about whole problem area with respect to a complex supply network
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issue cannot be resident in a single person, however, export the person may be.Hence, methodologies such as structural modeling (Lendaris 1980), which aggregateknowledge of various experts, are more suitable. In such complex situations, onemore important issue is that the risk variables affecting the situation are not veryimportant per se. The direct and indirect interrelationships between these risk vari-ables describe the situation far more accurately than the individual risk variablestaken in isolation for a specific single firm. Thus, a methodology that developsinsights into collective understanding of these interrelationships is the one suitableto handle such issues. The methodology of choice should concentrate on direct andindirect interrelationships between the risks variables and should aim at bringing outthe aggregate picture of such interrelationships as extracted from a body of knowl-edgeable persons.
The proposed integrated approach combines two structural modeling tools, whichare interpretive structural modeling (ISM) and MICMAC (refers to their French acro-nym: Matrice d’Impacts Croisés Multiplication Appliquée á un Classement) method.The choice is set to ISM and MICMAC, because both are fully implementable andavailable for use; are general in their applicability; are cheap and less time-consum-ing; are most commonly used in literature; and permit using subjective data. ISM hasbeen used to present a hierarchical model showing the interrelationships between therisk sources. MICMAC analysis has been used to quantify and classify the risk vari-ables based on their influence and dependence on other risk variables and to highlightcounter-intuitive risk variables. The objective is to recognize influential risk variablesand dependant risk variables, and consequently, to ascertain the key risk variables andtheirs relationships. These prioritized risk variables provide a useful tool to supplynetwork managers to focus on those key variables that are most important for effec-tive risk mitigation strategies. A real case study is provided in order to illustrate theapplication of the proposed approach. It concerns the supply network of the Masmou-di pastry. In this case study, seven risk sources and thirty risk variables have beenidentified and fully analyzed according to the proposed approach.
The rest of this paper is organized as follows. Section 2 presents an overview ofstructuring tools that are ISM and MICMAC. Section 3 presents a literatureoverview that concerns SCRM and the use of structuring tools in risk management.Section 4 describes the flow chart of the proposed approach. Section 5 presents indetail the case study and the analysis phases of the proposed approach. Finally,conclusions are drawn in Section 6.
2. Structural modeling
Structural analysis is a collective process that requires the participation of multipleparticipants. It offers the team the possibility to describe a system (i.e. the competi-tive environment) with the aid of a matrix that relates the various elements foundtherein. The objective of this method is to identify the principal elements (variables)and then to determine whether each is influential or dependent vis-à-vis one another.It includes three successive phases: creating an inventory of variables, describing therelationships amongst the variables, and then identifying key variables.
Structural modeling employs graphics and words in carefully defined patterns toportray the structure of a complex issue, system, or a field of study (Warfield 1974).Mathematical quantification, as and where needed, can be added to make thisqualitative geometric representation semi-quantitative. But the process of structural
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modeling essentially highlights the geometric, rather than the algebraic features.Structural models describe form and structure rather than measure quantitative out-put. Linstone, Lendaris, and Rogers (1979) states that they provide sense of thegeography of a complex system, a rough map that can shed considerable light onthe potential consequences of links between system elements and variables.
There are various structuring tools available in the literature. Lendaris (1980) pro-vides a list of nine structuring tools and compares them on the basis of a number ofcharacteristics. Linstone, Lendaris, and Rogers (1979) have compared and contrastedthese tools. All of them are fully implementable and available for use; can be under-stood and used by persons skilled in mathematics; are general in their applicability;are cheap and less time-consuming; and permit using subjective data. Out of theseISM, ELECTRE, SPIN, IMPACT, KSIM, XIMP, QSIM are the most frequently usedstructuring tools. Linstone, Lendaris, and Rogers (1979) have also explained the pro-cess of selecting an appropriate tool for a particular use. After that, there are manyother proposed structuring tools, such as MICMAC method (Duperrin and Godet1973), Fuzzy approach (Asan, Bozdag, and Polat 2004), etc. In the following, thedescription is limited to the tools used in this research, which are ISM and MICMACmethod.
2.1. Interpretive structural model
ISM is an interactive learning process in which a set of varied but directly relatedelements is structured into a comprehensive systemic model (Warfield 1974, 1976).ISM is a system structure modeling approach to analyze and to build the elementconnection model within the complicated system. The theoretical foundation of ISMis based on discrete mathematics, graph theory, social science and collective plan-ning. The relational sequence of each element within the complex system can beanalyzed by ISM, and the graph of the relational structural hierarchy with the prop-erty of hierarchy can be built using quantitative methods.
ISM is used in complex situations in which the user employs his or her under-standing of the elements involved to make subjective judgments about existing orabsent relationships between each pair of elements. Sage (1977) described this pro-cess as one of transforming unclear, poorly articulated mental models of systemsinto well-defined and useful models. The ability of ISM to reflect the cognitiveexperience of individuals involved in complex situations is pronounced, as indicatedby Bolanos et al. (2005). The method is interpretive in that the group’s judgmentdecides whether and how the items are related, and structured in that, on the basisof relationships, it extracts an overall structure from the complex set of items, and itis modeling in that it portrays the specific relations and overall structure in a digraph(directed graph) model (Rouse and Putterill 2003). ISM is primarily intended as agroup-learning process, but it can also be used by individuals working alone(Agarwal, Shankar, and Tiwari 2007).
The various steps involved in the ISM methodology are as follows:
(1) Variables affecting the system under consideration are listed, which can beobjectives, actions, and individuals, etc.
(2) From the variables identified in first step, a contextual relationship isestablished among variables with respect to which pairs of variables wouldbe examined.
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(3) A structural self-interaction matrix (SSIM) is developed for variables, whichindicates pairwise relationships among variables of the system under consid-eration.
(4) Reachability matrix is developed from the SSIM, and the matrix is checkedfor transitivity. The transitivity of the contextual relation is a basic assump-tion made in ISM. It states that if a variable A is related to B and B is relatedto C, then A is necessarily related to C.
(5) The reachability matrix obtained in fourth step is partitioned into differentlevels.
(6) Based on the relationships given above in the reachability matrix, a directedgraph is drawn, and the transitive links are removed.
(7) The resultant digraph is converted into an ISM, by replacing variable nodeswith statements.
(8) The ISM model developed in seventh step is reviewed to check for concep-tual inconsistency and necessary modifications are made.
2.2. MICMAC Method
As mentioned above, MICMAC method refers to their French acronym: Matriced’Impacts Croisés Multiplication Appliquée á un Classement. It was developed byDuperrin and Godet (1973) to study the diffusion of impacts, the through reactionpaths and loops for developing hierarchies for members of an elements set. Twohierarchies, one based on influence (driver power) and the second based on depen-dence, are usually developed. MICMAC method is a structural analysis tool thatdescribes a system using a matrix which links up the constituent components of thesystem. This method identifies the main variables that are both influential anddependent: those that are essential to the evolution of the system.
MICMAC method assumes that the cross impact matrix has already been filled:a suitable set of impact variables is already chosen and the direct impact strength ofeach impact variable on all other impact variables has already been classified by thestrength 0 (no impact), 1 (little impact), 2 (medium impact), or 3 (strong impact).Unlike to ISM in which impact variables has been classified in: 0 (no impact), and 1(with impact).
An examination of direct relationship matrix reveals the variables having themaximum direct impact but is not able to identify the hidden variables, which attimes, greatly influence the system under consideration. The indirect inter-relation-ships between variables have an impact on the system through influence chains andreaction loops, also known as feedback. The number of these chains and loops couldbe so large that it may be difficult to interpret the relationships without the help ofcomputers. MICMAC takes into consideration all levels of transitivity, unlike theISM method that takes into consideration only one level of transitivity. To explainthis further, if in a system, variable A is related to B and B is related to C, then nec-essarily by ISM, variable A is related to C; further, if variable C is related to D, ISMdoes not take into consideration the effect of A on D, which MICMAC does. In factMICMAC incorporates all levels of transitivity. If a matrix ‘M’ is used to representthe direct relations between a set of variables, then the matrix ‘M2’ will reflect thesecond level of transitivity, and ‘M3’ will reflect the third level of transitivity and soon. In other words, if 3rd, 4th, 5th, … nth powers of the direct relationship matrix
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‘M’ are obtained, then 3rd, 4th, 5th, … nth order indirect relationships shall berevealed (Godet 2007). Each time the process is repeated, a new hierarchy amongvariables can be deduced. When raised to a certain power, if this hierarchy repeatsin the next stage of multiplication, both in the hierarchy of rows and columns, sucha stage is considered as a stable stage. This hierarchy is the MICMAC classificationand is used to study indirect relationships minutely.
MICMAC categorizes the variables into 4 clusters, namely autonomous, depend-ing, linkage and influent (Godet 2007). The first cluster consists of the ‘autonomousvariables’, known also as ‘Excluded variables’, which have weak influence andweak dependence. These variables are relatively disconnected from the system, withwhich they have only few links, which may be strong. Second cluster consists of the‘depending variables’, known also as ‘Resultant variables’, which have weak influ-ence but strong dependence. Third cluster has the ‘linkage variables’, known also as‘Intermediate variables’ and ‘Relay variables’, which that have strong influence andalso strong dependence. These variables are unstable in the fact that any action onthese variables will have an effect on others and also a feedback on themselves.Fourth cluster includes the ‘influent variables’, known also as ‘Input variables’ hav-ing strong influence but weak dependence. It is observed that a variable with a verystrong influence called the key variables falls into the category of influent or linkagevariables.
3. Literature overview of SCRM
As mentioned by Hallikas et al. (2004), many authors have observed that the use ofthe term ‘risk’ can be confusing, and argue that risk should be separated from ‘risk(and uncertainty) sources’ and ‘risk consequences’ (risk impact). Risk sources arethe environmental, organizational or supply chain – related variables that cannot bepredicted with certainty and that affect the supply chain outcome variables. Supplychain risk is defined by the distribution of the loss resulting from the variation inpossible supply chain outcomes, their likelihood, and their subjective values(Viswanadham and Gaonkar 2008). Supply chain risks comprise risks due tovariations in information, material and product flows, which originate at the originalsupplier and lead to the delivery of the final product to the end user. Otherdefinitions can be founded in Zsidisin and Ritchie (2009). Thus, supply chain risksrefer to the possibility and effect of a mismatch between supply and demand.Furthermore, risk consequences can also be associated with specific supply chainoutcomes like supply chain costs or quality.
Supply chain risks can be typed according to other different classifications in theliterature. For instance, Pfohl, Kohler, and Thomas (2010) distinguishes betweenendogenous risks emerging in a supply chain and exogenous risks whose origin islocated in the environment of the focal network. However, this taxonomy mainlyconcentrates on company’s internal risks. A common classification was introducedby Christopher and Peck (Christopher and Peck 2004), who classify supply chainrisks in five sources according to their origin. These five sources can be summarizedin three groups: company internal risks, supply chain internal risks, andenvironmental risks (Christopher and Peck 2004). Jüttner, Peck, and Christopher(2003) organized the risk sources relevant for supply chains into three categories:(1) external to the supply chain; (2) internal to the supply chain; and (3) network-related. Tang (2006b) divided risk into operational risks and disruption risks.
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Operational risks are associated with inherent uncertainties such as uncertaincustomer demand, uncertain supply, and uncertain cost, whereas disruption risks areassociated with major disruptions caused by natural and man-made disasters such asearthquakes, floods, hurricanes, terrorist attacks, or economic crises such as currencydevaluation or strikes. He finds that the business impact associated with disruptionrisks is much greater than that of the operational risks.
Tang (2006b) associated supply chain risks with four management areas, namelysupply management, demand management, product management, and informationmanagement. Supply management involves coordinating with upstream partners toensure timely delivery of supplies. Demand management involves coordinating withdownstream partners to influence demand in a beneficial manner. Product manage-ment involves modifying the product or process design so as to make it easier toensure that supply meets demand. Information management involves an effort on thepart of supply chain partners to improve their coordination, which may involve shar-ing various types of information that is available to individual supply chain partners(Peck 2007).
In general, a SCRM process consists of four components: (1) risk identification;(2) risk assessment; (3) risk management decisions and implementation; and (4) riskmonitoring. It is clear that, the success of SCRM methodology is based mostly onthe first two steps. In the risk identification step, risks facing the firm’s supply chainare identified. Exemplary research in this step may be found in Chopra and Sodhi(2004) where general risks in the supply chain are categorized and discussed.Examples of previous risk assessment work include that by Zsidisin et al. (2004)and Hallikas et al. (2004). Assessing risk is a complicated step and can help a firmto prioritize which risks will affect the vulnerability of a supply chain. In theliterature, there has been a range of research into risk in purchasing and supply, butlittle in global supply networks using integrated approach. This can be explaineddue to the huge number of risk variables and their direct and indirect interrelationsthat may suffer all the supply network partners (firms). One important constituent ofSCRM is the prioritization of risks. Prioritization helps a company to focus thedecision-making and risk management effort on the most important risks (Hallikaset al. 2004). Prioritization requires comparisons concerning the relative importanceof each of the risk variables.
It is clear that SCRM is a field of escalating importance and is aimed atdeveloping approaches to the identification, assessment, analysis and treatment ofareas of vulnerability and risk in supply chains and networks. It has been gainingconsiderable attention in the last decade as an autonomous subject in the field ofSCM (Macgillivray et al. 2007; Verbano and Venturini 2011). In previous SCRMliterature, there has been a range of research into risk in purchasing and firm, butlittle in supply networks. Nevertheless, some new researches deal with this area. Forinstance, Trkman and McCormack (2009) have presented preliminary researchconcepts regarding a new approach to the identification and prediction of supplyrisk. The findings of their approach are explained within the contingency theory.Mohd (2009) has used a multi-criteria approach to assign relative importance tovarious risks in a supply chain and develop plans accordingly to mitigate them.Tuncel and Alpan (2010) have investigated the disruption factors of the supply chainnetwork by a failure mode, effects and criticality analysis technique and haveintegrated, therefore, the most critical failure modes into the studied supply chainnetwork. Cagliano et al. (2012) proposed a framework to integrate both risk
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identification and analysis in extensively applied SCM practices, like processmapping and performance measurement. Their framework is based on data currentlyrecorded by companies for purposes other than risk investigation. In particular, foreach supply chain process of the Supply Chain Operations Reference-model (SCORmodel), risk sources are identified and connected to elementary activities through astandard framework. After that, the effects of risky events due to the defined sourcesare assessed by means of data taken from the performance measurement system ofan organization. Moreover, Elleuch, Hachicha, and Chabchoub (Forthcoming) pro-posed a combined approach for SCRM, which consists of including the following.(1) Failure Mode, Effects, and Criticality Analysis to identify risk. (2) Design ofexperiment to design risks mitigation and action scenarios. (3) Discrete event simu-lation to assess risks mitigation action scenario. (4) Analytic hierarchy process toevaluate risk management scenarios. (5) Desirability function approach to minimizethe risk.
Recently, some studies have applied structuring tools in SCM research area, asdepicted in Table 1, but very little in SCRM. For example, Faisal, Banwet andShankar (2006) present an approach based on ISM and MICMAC method toeffective supply chain risk mitigation by understanding the dynamics betweenvarious enablers that help to mitigate risk in a supply chain. Jha and Devaya (2008)present an application of ISM and MICMAC to presents the internationalconstruction risk factors from the Indian construction professionals’ viewpoint, in acomprehensive format to enable practitioners to prioritize the efforts to manage therisk factors. Diabat, Govindan, and Panicker (2012) create a model that analyses thevarious risks involved in a food supply chain with the help of ISM and MICMACanalysis. Finally, Elmsalmi and Hachicha (2013) propose an application ofMICMAC analysis to prioritize risks factors in a supply network case study.
The use of structuring tools in SCRM is very little. There are only some studiesthat apply ISM and MICMAC to the same list of variables that do not exceed fifteenvariables. Unlike the proposed approach, two structural modeling tools are appliedin a new integrated approach. Firstly, ISM has been used to present a hierarchicalmodel showing the interrelationships between the risk sources. Secondly, MICMACanalysis has been used to quantify and classify the risk variables that are based onthe risk sources, based on their mutual influence and dependence. The objective isto ascertain the key risk variables and theirs relationships. These prioritized riskvariables provide a useful tool to supply network managers to focus on those keyvariables that are most essential for effective risk management strategies.
Table 1. Recent applications of structuring tools in SCM.
Reference Application
Ravi and Shankar (2005) Barriers of reverse logisticsFaisal, Banwet, and Shankar (2006) Risk mitigation enablersAgarwal, Shankar, and Tiwari (2007) Agility of supply chainJha and Devaya (2008) Risk assessing of project constructionKannan, Pokharel, and Kumar (2009) Selection of reverse logistics provider resourcesFeng, Wu, and Chia (2010) Locating a manufacturing centers in ChinaDiabat, Govindan, and Panicker (2012) Risk mitigation in a food industryElmsalmi and Hachicha (2013) Risks prioritization in global supply networks
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4. Proposed approach
The proposed approach is described through the flow chart which is shown inFigure 1. It will be more described in the case study application.To conduct acomprehensive analysis, it is necessary to consult the SCRM literature review and totake into account the type of industry under study and the historical problemsexperience of each partners of the supply network. To further enrich the analysis,open-ended questions and brainstorming sessions are realized to list risk sources andrisk variables.
After that, the proposed approach is composed of two structuring toolsapplication. The first tool is ISM-based approach, which is applied to the identifiedrisk sources. For this purpose, it is necessary to establish all contextual relationshipsbetween risk sources, to develop the SSIM and to develop the initial and final
Risk mitigation strategies
ISM model for the risk sources
MICMAC method for the risk variables
Historical experience of the problems
SCRM literature review
Search of all possible risk sources
Open-ended questions and brainstorming sessions
Establish contextual relationship between risk sources
Develop a structural self-interaction matrix (SSIM)
Build the ISM based graph
Numerical weights of the risk factors
Indirect influence-dependence map
Identify the risk levels
Develop initial and final reachability matrix
Specific characteristics of the supply network under study
Influential risk variables and dependant risk variables
Develop the reachability matrix, as the MICMAC software input
Search of all possible risk variables
Displacement map
Final hierarchical map and relationship of key risk variables
Figure 1. Flow chart of the proposed approach.
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reachability matrix. At the end, the objective of the ISM is to identify the risk levelsfor building the ISM graph.
The second tool used in the proposed approach is the MICMAC method. Theobjective of the MICMAC analysis is to recognize influential risk variables anddependant risk variables. After the identification of all risk variables, the first step isto develop the reachability matrix that is considered as the main input of theMICMAC software. The second step consists on the execution of the software andthe collection of the direct and indirect influence-dependence map, the numericalweights of each risk variables, the displacement map, and finally, the influential riskvariables and dependant risk variables.
The combination between ISM and MICMAC method allows a final hierarchicalmap thatgives the key risk variables and their mutual relationship. These key riskvariables are the most prioritized risk variables. Indeed, risk prioritization provides auseful tool to supply network managers to differentiate between independent anddependent risk variables and their mutual relationships that would help them topropose effective risk mitigation strategies.
5. Case study
5.1. Overview of the supply network
The supply network under study, Masmoudi, is one of the leading producers ofpastry products in Tunisia. The company manufactures and distributes manyproducts that mix almond, pistachios and pine nuts, with the benefits of olive oil.Masmoudi is famous for the quality and the nobility of the components used in themanufacturing of its cakes, and so took advantage from its localization in Sfax, thecity that is famous for the specificities of its olive trees, almond trees, and pistachiotrees.
The Manufacturer obtains the required raw materials from around sevensuppliers. The manufactured packed food product is distributed through a wholesalerin and around the state through a network of two main distributors. The productreaches the customer with the help of 10 retailers. An overview of Masmoudi’ssupply chain is shown in Figure 2. The application of the proposed approach to thesupply network under study was detailed in the next subsections.
5.2. ISM model development for risk sources
5.2.1. Risk sources identification
To achieve risk sources identification step and to build all reachability matrix, theRisk Analysis Group was created (our acronym RAG). This group, or RAG, wasmade up of nine members including one supplier manager, four main managers ofMasmoudi’s company (manufacturing, engineering, procurement, delivery), adistributor manager, two retailer managers, and one university professor. Thespecific methodology adopted was based on open-ended questions, carried out fromthe influencing risk sources in the supply network under study. It is clear that thesources of risk coincide with the partners that make up the supply network. Hence,seven risk sources were identified: Environment (RS1), Supplier (RS2),Manufacturer (RS3), Wholesaler (RS4), Distributor (RS5), Retailer (RS6), andConsumer (RS7).
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5.2.2. Structural self-interaction matrix
ISM methodology suggests the use of the expert opinions based on various manage-ment techniques in developing the contextual relationship among the variables suchas brainstorming, nominal group technique, synectics, clinical interviewing, Delphi,etc. Thus, in this research for identifying the contextual relationship among the risksources in a global supply network, the RAG was consulted for the same. For ana-lyzing the risk sources, a contextual relationship of ‘aggravate’ type is chosen. Thismeans that one source risk helps to aggravate another source risk. Four symbols arecommonly used to denote the direction of relationship between the sources (i and j):
� V: risk source i will aggravate risk source j;� A: risk source i will be aggravated by risk source j;� X: risk source i and j will aggravate each other; and� O: risk sources i and j are unrelated.
The SSIM which is shown in Table 2 presents the use of the symbols V, A, X,and O. For instance, there are no relationship seems to exists between RS2 (supplier)and RS7 (consumer) so the relationship is O. In addition, RS2 (Supplier) and RS4(wholesaler) aggravate each other so the relationship is X.
5.2.3. Reachability matrix for risk sources
The SSIM is transformed into a binary matrix, called the reachability matrix bysubstituting, A, X, O by 1 and 0 as per the case. The rules for the substitution of 1’sand 0’s are the following:
� if the (i, j) entry in the SSIM is V, then the (i, j) entry in the reachabilitymatrix becomes 1 and the ( j, i) entry becomes 0;
� if the (i, j) entry in the SSIM is A, then the (i, j) entry in the reachabilitymatrix becomes 0 and the ( j, i) entry becomes 1;
Information flow
Material flow
. . . Supplier 1 Supplier 7Supplier 2
Manufacturer of pastry products (Masmoudi Indus)
Wholesaler of pastry products (Masmoudi com)
DistributorCompany 1
DistributorCompany 2
. . . Retailer 1 Retailer 10Retailer 2
Customers
Figure 2. Overview of Masmoudi’s supply network.
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� if the (i, j) entry in the SSIM is X, then the (i, j) entry in the reachabilitymatrix becomes 1 and the ( j, i) entry also becomes 1; and
� if the (i, j) entry in the SSIM is O, then the (i, j) entry in the reachabilitymatrix becomes 0 and the ( j, i) entry also becomes 0.
� Each diagonal element becomes 1.
Following the above rules the reachability matrix shown in Table 3 is made andthe transitivities are removed to get the final reachability matrix, shown in Table 4.The transitivity of the contextual relationship is a basic assumption made in ISM. Itstates that if a risk source A is related to B and B is related to C, then A is necessarilyrelated to C.
5.2.4. Level partitions
From the final reachability matrix (Table 4), the reachability and antecedent set(Warfield 1974) for each risk source are found. The reachability set consists of theelement itself and the other elements which it may impact, whereas the antecedentset consists of the element itself and the other elements which may impact it. There-after, the intersection of these sets is derived for all the risk sources. The risk sourcesfor whom the reachability and the intersection sets are the same occupy the top levelin the ISM hierarchy. The top-level element in the hierarchy would not help achieveany other element above its own level. Once the top-level element is identified, it isseparated out from the other elements (Table 5). Then, the same process is repeatedto find out the elements in the next level. This process is continued until the level ofeach element is found. The first, second and third iterations are shown in Table 5.These levels help in building the ISM model.
Table 2. Structural self-interaction matrix.
Code Risk source (i)
Risk source (j)
RS7 RS6 RS5 RS4 RS3 RS2
RS1 Environment V V V V V VRS2 Supplier O O O X VRS3 Manufacturer O O O VRS4 Wholesaler O X VRS5 Distributor O VRS6 Retailer VRS7 Consumer
Table 3. Initial reachability matrix for risk sources.
RS1 RS2 RS3 RS4 RS5 RS6 RS7
Environment RS1 1 1 1 1 1 1 1Supplier RS2 0 1 1 1 0 0 0Manufacturer RS3 0 0 1 1 0 0 0Wholesaler RS4 0 1 0 1 1 1 0Distributor RS5 0 0 0 0 1 1 0Retailer RS6 0 0 0 1 0 1 1Consumer RS7 0 0 0 0 0 0 1
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5.2.5. Building the ISM based graph
From the final reachability matrix (Table 4), the structural model is generated bymeans of vertices or nodes and lines of edges. If there is a relationship between therisk sources j and i this is shown by an arrow that points from i to j. This graph iscalled a directed graph or digraph. After removing the transitivities as described inISM methodology, the digraph is finally converted into ISM as shown in Figure 3.
The ISM model sets out the risk sources in a hierarchical manner, with sourceshaving least influence at the top. It also shows the relationships between the risksources. The ISM model for risk sources shows that RS1 (Environment) is at thebottom, implying that this source can influence all other risk sources directly or indi-rectly, while it cannot be influenced by any other source. Hence it can be concludedthat the ‘Environment’ source is an important risk source that merits attention. Thenext level risk sources are: RS2 (Supplier), RS3 (Manufacturer), and RS4 (Whole-saler). These too exert influence on other sources and aggravate them, while theythemselves cannot be influenced by any source within the control of the supply
Table 4. Final reachability matrix for risk sources.
RS1 RS2 RS3 RS4 RS5 RS6 RS7
Environment RS1 1 1 1 1 1 1 1Supplier RS2 0 1 1 1 1 1 0Manufacturer RS3 0 1 1 1 1 0 0Wholesaler RS4 0 1 1 1 1 1 1Distributor RS5 0 0 0 1 1 1 1Retailer RS6 0 0 0 1 1 1 1Consumer RS7 0 0 0 0 0 0 1
Table 5. Identification of levels.
Risk source Reachability set Antecedent set Intersection set Level
Iteration 1RS1 1, 2, 3, 4, 5, 6, 7 1 1RS2 2, 3, 4, 5, 6 1, 2, 3, 4 2, 3, 4RS3 2, 3, 4, 5 1, 2, 3, 4 2, 3, 4RS4 2, 3, 4, 5, 6, 7 1, 2, 3, 4, 5, 6 2, 3, 4, 5, 6RS5 4, 5, 6, 7 1, 2, 3, 4, 5, 6 4, 5, 6RS6 4, 5, 6, 7 1, 2, 4, 5, 6 4, 5, 6,RS7 7 1, 4, 5, 6, 7 7 IIteration 2RS1 1, 2, 3, 4, 5, 6 1 1RS2 2, 3, 4, 5, 6 1, 2, 3, 4 2, 3, 4RS3 2, 3, 4, 5 1, 2, 3, 4 2, 3, 4RS4 2, 3, 4, 5, 6 1, 2, 3, 4, 5, 6 2, 3, 4, 5, 6RS5 4, 5, 6 1, 2, 3, 4, 5, 6 4, 5, 6 IIRS6 4, 5, 6 1, 2, 4, 5, 6 4, 5, 6, IIIteration 3RS1 1, 2, 3, 4 1 1 IVRS2 2, 3, 4 1, 2, 3, 4 2, 3, 4 IIIRS3 2, 3, 4 1, 2, 3, 4 2, 3, 4 IIIRS4 2, 3, 4 1, 2, 3, 4 2, 3, 4 III
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chain partners. RS5 (Distributor) and RS6 (Retailer) are in the next level risk source.It can be influenced by the previously discussed sources, and at the same time itdirectly affects RS7 (Consumer). It should be noted that RS2, RS3, RS4, RS5, andRS6 form the hub of the supply network. They are mutually influenced by them-selves. Hence, it can be concluded that ISM model cannot provides a detail interpre-tation. In fact, there is a composite direct and indirect correlation between all risksources. It is necessary to zoom each source and redo an advanced treatment usingMICMAC method. This advanced treatment will be presented in the nextsubsection.
5.3. MICMAC analysis.
5.3.1. Risk variables identification
The first stage of the MICMAC method is to identify all risk variables. To achievethis stage, and to develop the reachability matrix, a brainstorming session was per-formed within the RAG. After that, an extension of variables list is made based on areview of the literature and consultation with other industry experts. As there were52 risk variables to start with, they were grouped into units of 30 for the final analy-sis, which are listed in Table 6 according to their risk source. It should be noted thatsome other risk variables seem also be important in the literature, such as storagecapacity, transportation cost fluctuation, etc. are not selected in this case study. Infact, the RAG is the only one which is responsible in risk variables identification.
5.3.2. Reachability matrix for risk variables
The second stage of the MICMAC method was to cross, in a matrix 30 × 30 (Table 7,MICMAC matrix of converging influences), the influence of each risk variable, inrelation to the other 29 variables, in a scale of 0, 1, 2, and 3 to the influence of the
RS1 Environment
RS7 Consumer
Level I
Level II
Level III
Level IV
RS3 Manufacture
RS2 Supplier
RS4 Wholesaler
RS6 Retailer
RS5 Distributor
Figure 3. ISM-based model of risk sources.
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risk variable, and if such influence was respectively null, weak, average or strong(and potential). Each one of the 870 crossings between variables allowed for a trulymultifaceted reflection of the same reality, and led not only to intense debates withinthe RAG on the risk variables of the supply network under study. In this stage, thedirect impact strength of each risk variable on all other risk variables has been clas-sified by the strength 0 (no impact), 1 (little impact), 2 (medium impact), or 3(strong impact) as mentioned in Table 7.
From this reachability matrix (Table 7), it was possible to deduce a hierarchy ofthe influence of the risk variables and the values of each line i of the matrix wereadded, thus obtaining a classification of the direct influences of each risk variable.The last columns in Table 7 contain the total direct influence of each risk variables.Consequently, E5 (Seasonal production) is founded as the most direct influent riskvariable, because it has the largest value, which is 40. In the same way, it was possi-ble to deduce a hierarchy of the dependence of the risk variables and the values ofeach column j of the matrix were added, thus obtaining a classification of the directdependences of each variable risk. The last rows in Table 7 contain the total directdependence for each risk variables. As shown in Table 7, M4 (Productivity and qual-ity failure) and M7 (Inventory and stock failure) are the most direct dependents riskvariables, because they have the largest values, which are 27 and 26, respectively.
Table 6. Summary of risk variables related to their source.
Risk sources Id. Brief risk description
RS1: Environment E1 Political instability and unrestE2 Change in government regulation (legal system)E3 Economic imbalances and social inequality riskE4 Epidemical disasterE5 Seasonal productionE6 Poor harvest (agriculture)
RS2: Supplier S1 Dependency on single (a few) supplierS2 Rise in supplier pricesS3 Inability of supplyS4 Failure in raw materials quality
RS3: Manufacturer M1 Internal labor strikesM2 Shortage of skilled employeesM3 Production procedures not respected by employeesM4 Productivity and quality failureM5 Failure in healthy and hygienic productM6 Craft and manual productionM7 Inventory and stock failure (outage or Excessive)
RS4: Wholesaler W1 Error in forecastingW2 Volatility of retailer demandW3 Insufficient relationship confidence with retailersW4 Unpredictable substitute products and new product
RS5: Distributor D1 External labor strikesD2 Lack of transport resourcesD3 Error in logistics distribution between retailers
RS6: Retailer R1 Difference in balance measuresR2 Retailer order partially deliveredR3 Delayed delivery to retailer
RS7: Consumer C1 Changes in consumer tastesC2 Failure to communicate with consumerC3 Delayed delivery to consumer
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Table
7.Reachability
matrixforrisk
variables.
E1
E2
E3
E4
E5
E6
S1
S2
S3
S4
M1
M2
M3
M4
M5
M6
M7
W1
W2
W3
W4
D1
D2
D3
R1
R2
R3
C1
C2
C3
Total
E1
03
30
00
01
00
11
10
00
00
00
02
10
00
00
00
13E2
00
10
00
00
00
00
10
00
00
00
00
00
00
00
00
2E3
31
00
00
00
00
20
00
00
00
00
02
00
00
00
00
8E4
11
00
01
00
22
20
02
30
01
00
00
00
00
01
00
16E5
00
00
00
22
12
03
32
20
33
13
00
31
03
20
22
40E6
00
00
30
13
32
00
02
10
20
00
00
00
00
00
00
17S1
00
00
00
00
31
00
02
11
30
00
00
00
00
10
00
12S2
00
00
00
00
21
00
01
00
00
00
10
00
00
01
00
6S3
00
00
00
03
00
00
00
00
20
00
00
00
01
20
00
8S4
00
00
00
31
00
00
13
32
00
10
00
00
01
00
01
16M1
01
00
00
00
00
00
12
00
11
00
00
00
00
00
00
6M2
00
00
00
00
00
00
33
20
21
00
00
00
21
00
01
15M3
00
00
00
10
00
00
02
30
00
00
00
00
20
00
00
8M4
00
00
00
00
00
00
00
10
32
00
00
00
02
20
00
10M5
00
00
00
00
00
00
01
00
21
00
00
00
01
00
00
5M6
00
00
00
00
20
03
03
20
32
20
00
00
22
20
00
23M7
00
00
00
00
00
00
03
20
00
10
00
10
00
20
00
9W1
00
00
00
00
00
00
00
00
30
00
00
20
00
20
00
7W2
00
00
00
00
00
00
00
00
02
02
00
01
01
00
00
6W3
00
00
00
00
00
00
00
00
02
10
00
00
02
20
00
7W4
00
00
00
00
00
00
00
00
23
00
00
00
00
00
00
5D1
00
00
00
00
00
00
00
00
00
00
00
21
00
00
00
3D2
00
00
00
00
00
00
01
00
00
00
00
00
03
20
00
6D3
00
00
00
00
00
00
00
00
00
00
00
00
02
20
00
4R1
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
0R2
00
00
00
00
00
00
00
00
00
20
00
00
00
00
12
5R3
00
00
00
00
00
00
00
00
00
10
00
00
00
00
13
5C1
00
00
00
00
00
00
00
00
00
00
30
00
00
00
10
4C2
00
00
00
00
00
00
00
01
00
00
00
00
00
00
02
3C3
00
00
00
00
00
00
00
00
00
00
00
00
00
00
10
1Total
46
40
31
710
138
57
1027
204
2618
95
44
93
619
192
611
270
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However, the MICMAC method can go further. This is a complex system, made upof those 30 risk variables, wherein certainly there are direct and potential influences.In this case, for us to take into account the indirect influences, the indirect influenceanalysis of the 5th degree was stabilized, from which point there will be no modifi-cation of the classification.
5.3.3. Indirect influence – dependence map
The MICMAC analysis has been done on the computer software also called ‘MIC-MAC’ developed by a French computer innovation institute ‘3 IE’ (Institut d’Innova-tion Informatique pour l’Entreprise) under the supervision of its conceptual creatorsLIPSOR Prospective (foresight) Strategic and Organizational Research Laboratory.This output of the MICMAC software, the indirect influence – dependence mapshown in Figure 4 presents the risk variables on an influence – dependence planewith the dependence on the X-axis and influence on the Y-axis. The risk variables areplotted on the plane according to the strength of their influence and dependence fromthe matrix of indirect influence (MII). The MII is the matrix that has resulted byincorporating all the transitivities from the original matrix of direct influence whichwas the input data for the program (Table 7). It is useful to study this map in con-junction with the numerical weights of the risk variables shown in Table 7, whichhas been derived by performing the ‘Proportions’ function of the MICMAC soft-ware. This function classifies the risk variables according to their influence and theirdependence (direct and indirect) by giving them numerical weights. The influencesand dependences are normalized and expressed in for 10,000th.
Depending risk variables
Influent risk variables
Autonomous risk variables
Relay risk variables
Dependence
Infl
uenc
e
Figure 4. Indirect influence – dependence map.
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As mentioned above, the indirect influence – dependence map categorizes allrisk variables into four clusters, namely autonomous, dependent, relay (linkage) andindependent.
5.3.3.1. Autonomous risk variables
Located in the south–west quadrant (Figure 4), autonomous risk variables are littleinfluent and little dependent and appear out of line with the system. However, a dis-tinction is made within this group between the ‘disconnected risk variables’ and the‘secondary levers’. Disconnected variables: These are situated near the origin andtheir evolution therefore seems to be excluded from the systems’ global dynamics.Change in government regulation (E2), changes in consumer tastes (C1), and differ-ence in balance measures (R1) meet this criterion. Although common intuitive think-ing of supply managers is that these risk variables are major, MICMAC analysisreveals that these risk variables cannot be influenced by the other risk variables in thesupply network, nor does it influence the other risk variables. Secondary levers: Thesevariables are located above the diagonal and are more influent than dependent. Theyare not disconnected from the system and can act as application points for possiblemeasures. S1, M2, M3, M6, E1, E3, S2, S3, W3, W4, D1, D3, fall in this category. Itcan be said that not much can be done about these risk variables except monitoring.
5.3.3.2. Influent risk variables
Influent risk variables are the variables that are located in the North-West quadrantof the map; these are very influent and little dependent (see Figure 4). These are alsoconsidered as entry variables in the system. Among them are most often environ-ment variables that strongly condition the system but cannot be controlled by it. Inthe supply network under consideration, four of the 30 variables fall within thisquadrant. Two of them, E5 (Seasonal production) and E6 (Poor harvest) are clus-tered together. These are the most important environmental risk variables and arecorrectly located since they strongly condition the system (high influence), but can-not be controlled by it (low dependence). The other two influent risk variables areE4 (Epidemical disaster) and S4 (Failure in raw materials quality). These variableshave very high influence and their dependence is also low as the two first environ-mental risk variables. These are very important risk variables and need maximummanagement attention since they can influence other risk variables to the maximumextent and are themselves amenable to be influenced.
5.3.3.3. Depending risk variables
These risk variables lie in the south-east quadrant and are very dependent but littleinfluent. Seven of the risk variables, R3 (Delayed delivery to retailer), M7 (Inven-tory and stock failure), C3 (Delayed delivery to consumer), R2 (Retailer orderpartially delivered), M4 (Productivity and quality failure), W1 (Error in forecasting),and W2 (Volatility of retailer demand) lie in this quadrant. M5 (Healthy and hygie-nic product failure) can also considered in this category. In fact, from Table 8, it isclear to see that M5 is ranked third in direct dependence and 8th in indirect depen-dence. MICMAC result forces the management to give greater (consideration tothese risk variables and carry out a deeper analysis of these risk variables.
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5.3.3.4. Relay risk variables
These risk variables lie in the north-east quadrant and are very influent and verydependent. These are the most important risk variables in the supply network andwould require maximum management attention. In the system under study, thereare no variables falling under this quadrant. It means that all risk variables of thesupply network are stables. However, it would not be appropriate to leave it atthat. Managers would need to identify risk variables to which to devote maxi-mum attention. By studying Table 8 and Figures 4 and 5, it can be concludedthat the most important risk variables which could be considered as relay vari-ables are E5 (Seasonal production), E6 (Poor harvest), M6 (Craft and manualproduction), M7 (Inventory and stock failure), M4 (Productivity and quality fail-ure), W1 (Error in forecasting), R2 (Retailer order partially delivered), and R3(Delayed delivery to retailer). These have actually been pushed out of this quad-rant because of lower weights relative to the other risk variables, and if viewedin absolute terms, these are the most important variables. Among these, M7, M4,W1, R2, and R3 can be classified as ‘target risk variable’; these are more depen-dent than influent and hence can be considered as resulting from the networkevolution.
Table 8. Numerical weights of the risk variables.
Rank LabelDirect
influence LabelDirect
dependence LabelIndirectinfluence Label
Indirectdependence
1 E5 1481 M4 1000 E6 1563 R3 13642 M6 851 M7 962 E5 1496 M7 12473 E6 629 M5 740 E4 955 C3 11714 E4 592 R2 703 S4 848 R2 9945 S4 592 R3 703 M6 697 M4 9516 M2 555 W1 666 S1 601 W1 8417 E1 481 S3 481 M2 411 W2 8248 S1 444 C3 407 E1 400 M5 6719 M4 370 S2 370 S2 302 C2 56610 M7 333 M3 370 E3 286 D2 48711 E3 296 W2 333 M3 253 W3 26412 S3 296 D2 333 S3 249 D3 13413 M3 296 S4 296 M4 228 M6 10014 W1 259 S1 259 M7 218 S3 6815 W3 259 M2 259 M1 198 R1 6216 S2 222 E2 222 W1 158 S2 5317 M1 222 R1 222 W4 154 M2 4918 W2 222 C2 222 M5 151 M3 3519 D2 222 M1 185 C2 123 S1 2020 M5 185 W3 185 W3 109 W4 2021 W4 185 E1 148 W2 108 S4 1622 R2 185 E3 148 C1 98 C1 1123 R3 185 M6 148 E2 87 E2 1024 D3 148 W4 148 D2 87 E3 825 C1 148 D1 148 R2 64 D1 826 D1 111 E5 111 R3 48 E1 627 C2 111 D3 111 D3 37 M1 628 E2 74 C1 74 D1 37 E4 029 C3 37 E6 37 C3 20 E5 030 R1 0 E4 0 R1 0 E6 0
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Combining ISM-based model of risk sources (Figure 3) and MICMAC resultsinterpretation, the final hierarchical map of key risk variables is obtained as shownin Figure 6. This final map shows that E5 (Seasonal production), E6 (Poor harvest),M6 (Craft and manual production) are at the bottom, implying that these three riskvariables influence other key risk variables. M7 (Inventory and stock failure) andM4 (Productivity and quality failure) clearly forms the hub of the supply network.Hence, it can be concluded that these two risk variables are an extremely importantrisk variables requiring major mitigation management involvement. Examples ofstrategies for mitigating supply chain disruptions can be founded in Tang (2006a).
For instance, RAG, can apply a fishbone diagram, also known as the Ishikawadiagram to provide managers the causes and the potential factors of each prioritizedrisk variable. It is clear that each cause or reason for imperfection is a source of vari-ation. Causes are usually grouped into major categories to identify these sources ofvariation such as people, methods, machines, materials, etc. This work is being car-ried out by the group. In addition, the various causes should be assessed, for exam-ple using suitable approach such as Failure Mode, Effects, and Criticality Analysisapproach.
6. Conclusion and future researches
The aim of this paper is to propose and to check a new integrated approach basedon two structural modeling tools: ISM and MICMAC method. ISM has been usedto present a hierarchical model showing the interrelationships between the risk
Dependence
Infl
uenc
e
Figure 5. Displacement map.
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sources. MICMAC analysis has been used to quantify and classify the risk variablesbased on their influence and dependence on other risk variables and to highlightcounter-intuitive risk variables. The objective is to recognize influential risk vari-ables and dependant risk variables, and consequently to ascertain the key risk vari-ables and theirs relationships. These prioritized risk variables provide a useful toolto supply network managers to focus on those key variables that are most importantfor effective risk mitigation strategies.
A real case study, concerning Masmoudi pastry network, is provided in order toillustrate the application of the proposed approach. ISM model is applied to sevenidentified risk sources. While MICMAC is applied to 30 risk variables. It is con-cluded that: (1) 10% of the identified variables, which are ‘Seasonal production’,‘Poor harvest’, and ‘Craft and manual production’, are the most influential risk vari-able. (2) Less than 10% of the identified variables, which are ‘Inventory and stockfailure’ and ‘Productivity and quality failure’, are the most depending risk variables.These variables are an extremely important risk variables requiring major mitigationmanagement involvement. The proposed approach is applied to understand bothdirect and indirect relationships between all risk variables, and to ascertain the mostprioritized risk variables.
The proposed approach may be useful to the practitioners in risk managementand may also interest academicians, since the method used here can be applied inother areas of industrial and SCM as well. However, scoring directly some riskdimensions at the supply chain level faces two main problems: (1) The correlationof the identified dimension, even when they are related to different categories; and(2) The relevance of the scored risk at the analyzed perimeter. Globally, SCRMapproaches start by specifying more precise system perimeter and its related abstrac-tion models. For example, the SCOR model proposes extension for risk assessment.The planned perspective should delimit the supply chain main processes and give
E5 Seasonal
production
E6 Poor harvest
W1 Error in
forecasting
M7 Inventory and stock
failure
M6 Craft and manual
production
M4 Productivity and
quality failure
R3 Delayed delivery to
retailer
R2 Retailer order
partially delivered
RS1 Environment
RS6 Retailer
RS3 Manufacture
RS4 Wholesaler
RS7 Consumer
Figure 6. Final hierarchical map of key risk variables.
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the business relevance of identified risks and their exploitation model. In addition,Failure Mode, Effects, and Criticality Analysis can be applied by which supplychain is decomposed into its constituent parts, which are successively analyzed tofind all the potential failure causes and their effects. The objective is to search thebest mitigation risk management for each prioritized risk.
AcknowledgmentsThe authors warmly thank the editor and the anonymous reviewers for their detailed and con-structive comments, which were of great help in improving this manuscript.
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