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Journal of Retailing and Consumer Services 26 (2015) 153–167

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Journal of Retailing and Consumer Services

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Analysis on supply chain risks in Indian apparel retail chains andproposal of risk prioritization model using Interpretive structuralmodeling

V.G. Venkatesh a,n, Snehal Rathi b, Sriyans Patwa b

a Symbiosis Institute of Business Management, Symbiosis International University, Bengaluru Campus, #95/1, 95/2, Electronics City, Phase-1, Hosur Road,Bengaluru 560100, Karnataka, Indiab Symbiosis Institute of Operations Management, Symbiosis International University, Nashik Campus, Plot No. A-23, Shravan Sector, New Cidco, Nasik422008, Maharashtra, India

a r t i c l e i n f o

Article history:Received 8 December 2014Received in revised form6 June 2015Accepted 7 June 2015Available online 7 July 2015

Keywords:ISMFuzzy MICMACRetail risksSupply chain riskPrioritizationRisk assessmentRisk Priority Number

x.doi.org/10.1016/j.jretconser.2015.06.00189/& 2015 Elsevier Ltd. All rights reserved.

esponding author.ail addresses: [email protected] (V.G. [email protected] (S. Rathi), sriyanspatwa@gm

a b s t r a c t

Indian apparel retail industry is on a complete transformation journey and trying to evolve as an or-ganized industry. It is very common to find the disruption factors in every business and the ways tomitigate and manage them is of current research interest. The paper discusses the selective risks asso-ciated with the apparel retail supply chains in India by structural analysis of the controllable risks thatare identified. The work also reveals the use of Interpretative Structural Modeling (ISM) to establish theinterdependencies between these risks spread across various supply chain functions of retail industry.The relationships are established based on expert opinions using Delphi technique followed by ISMmodeling technique and Fuzzy MICMAC analysis. It also classifies the risk factors based on their drivingand dependence power. ISM is proved to be a useful tool to help understand the impact of risks at stagesof retail supply chain. Globalization, labor issues and security and safety of resources turns out to be thestrong drivers of other supply chain uncertainties. The domino effect of these risks leads to financialcrises for the organization.

The paper also proposes a new model for the Risk Priority Number (RPN) calculation using ISM andFuzzy MICMAC methodology for the applications in retail and various other domain risk studies. Thesample size of experts is small and to remove the biasness of opinion, the model can be further validatedusing Structural Equation Modeling (SEM) in the future. The outcome would help practicing managers toanalyze and to take actions for managing the factors by improving the bottom line of the organization byproper utilization of resources.

& 2015 Elsevier Ltd. All rights reserved.

1. Introduction

In the last two decades, supply chains of businesses have beenexperiencing rapid globalization and emerging technologicalchanges especially in the manufacturing and retail business. Today,supply chains across industries are being stretched the way it wasnever done before. The most trusted brands do only the assem-bling of components which are outsourced for manufacturing.Similarly, major apparel retailers do their business as well. Theydo product development and outsource rest of their operations.This has made supply chains more complex, fragile and prone tomany disruptions. It is an established fact that recent commercial

atesh),ail.com (S. Patwa).

chains are dynamic networks of interconnected firms and in-dustries (Hakansson and Snehorta, 2006). And, the search forbetter markets and cheaper sources of raw materials have madethe supply chains more and more complex and retailers need tosustain their business (Sahin and Robinson, 2002; Wu and Olson,2008; Ganesan et al., 2009). Many disruptions and risk factorshave threatened production and retail distribution systems. Theydirected a decline in the market share, cost escalation and dis-satisfaction amongst customers. In the last decade, supply chainrisks are studied diligently and are categorized into inherent orhigh frequent risks and disruption or infrequent risks (Kleindorferand Saad, 2005; Oke and Gopalakrishnan, 2009). These disruptionscould also be due to political, labor, market uncertainty, material,financial and information risk impacting supply chain perfor-mance (Shapira, 1995; Prater et al., 2001; Christopher and Lee,2004; Quinn, 2006; Tang, 2006a, 2006b; Poirier et al., 2007; Tangand Nurmaya Musa, 2011). How does one protect the business

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from disruption? The answer lies in the integration of supply chainrisk management as a core component in the operations of thebusiness. This intuited the studies on supply chain risks and mi-tigating strategies, which are increasingly becoming popular (Weiand Choi, 2010), eventually lead to the studies from the domainspecific risk mitigating strategies as well.

India, being a growing destination for the retail business, therisk is to be analyzed from supply chain perspective, though thesector is highly fragmented. Boston Consulting Group reports thatthe organized retail industry will achieve $260 billion business by2020 (BCG Report, 2011). In the last decade, Indian retail markethas shown the considerable growth in the Apparel business and soas food business. With many foreign apparel players eyeing toenter India through FDI, it has become a research destination. Tosupport that, though there are reports existing in this domain, thefocus on retail supply chain still attracts several problems to beexplored in the supply chain and its risk domain.

This paper will explore and analyze selective disruption factorsin the domain of study. The study also proposes a methodology toprioritize risks by analyzing the interdependencies between them.This contextual relationship is established through a techniquecalled Interpretive Structural Modeling (ISM) and followed by aMatriced’ Impacts Cruoses Multiplication Applique a un Classement(MICMAC) analysis for segregation of study variables. Thus, ourproposed model is based on a notion that each risk is associatedwith multiple ones in a way that either it drives them or is de-pendent on them. To design the mitigation strategies, the first stepis to identify and analyze the risk in terms of its frequency of oc-currence, severity in terms of cost and what other disruptions itcould lead to. The focus is to propose a methodology based onMICMAC analysis to analyze and prioritize the supply chain risksso that appropriate strategies can be designed to improve thebusiness efficiency. For prioritizing the risks, there is a new for-mula proposed based on the structural model, which is the uniquecontribution of the model.

The paper has been structured as follows: It starts with theintroduction about the supply chain risk management, followed bythe literature review on supply chain risk and Indian retail in-dustry. Then, the discussions on establishing the variables, ISMmodel formulation and MICMAC analysis. It ends with the dis-cussions on the new risk assessment framework, managerial im-plications and future scope.

Table 1Variables (risks) for ISM.

Risk no. Risk

R1 GlobalizationR2 Raw material and product quality standardsR3 Scarcity of resourcesR4 Supplier uncertaintyR5 Lack of co-ordination/alignmentR6 Behavioral aspect of employeesR7 Infrastructure risksR8 Delay in schedule/lead timeR9 Demand uncertaintyR10 Customer dissatisfactionR11 Financial riskR12 Security and safety

2. Literature review

The literature review has been done through systematic lit-erature review methodology proposed by Tranfield et al. (2003).The review process has followed the planning for the review,conducting the review exercise and reporting/dissemination pro-tocol in a systematic way. The review includes papers from variousjournals like Business Process Management, International Journal ofPhysical Distribution and Logistics Management, Journal of Opera-tions Management, Supply Chain Management: An InternationalJournal, The International Journal of Logistics Management, Journal ofManufacturing Technology Management, International Journal ofOperations and Production Management and etc. It also includesarticles and Reports from Harvard Business Review and reports onSupply chain risk management published by various prominentconsulting companies like Deloitte, PwC, Accenture, Technopak,etc. The second part covers the review on identifying the risksvariables and understanding the risk mitigating strategies from2000 to 2014.

Supply chain risk is defined as “any risk to material, productand information flow from original supplier to the delivery of thefinal product” (Christopher et al., 2003). There is a growing

importance to risks domain from supply chain perspective(Harland et al., 2003; Zsidisin and Ellram, 2003; Zsidisin et al.,2004; Khan et al., 2008; Wu and Olson, 2008; 2010; Wagner andBode, 2008; Tang and Tomlin, 2008; Rao and Schoenherr, 2008;Rao and Goldsby, 2009; Colicchia and Strozzi, 2012; Sodhi et al.,2012; Bandaly et al., 2013; Marley et al., 2014). Rao et al. (2006)gives the complete typology of various risks in supply chain sys-tem. Further, it is being identified as a function of uncertainty leveland the impact of an event (Sinha et al., 2004). However, it is thecommon belief that management within SC gathered more focusand momentum only after the 9/11 attacks in USA (Ghadge et al.,2012). This risk can be an internal element to the supply chain ordue to external factors (Goh et al., 2007). They can also be classi-fied as operations and disruptions risk (Tang, 2006a). The formerare associated with uncertainties inherent in a SC which includedemand, supply, and cost uncertainties. Disruption risks, on theother hand, are those caused by major natural and man-madedisasters such as flood, earthquake, tsunami, and major economiccrisis. Supply chains are vulnerable to disruptions due to a numberof variables. These disruptions or risks can also have significantimpact on profit margins of the businesses and such failure occursdue to one element which has an impact on both upstream anddownstream operations (Chopra and Meindl, 2001). It is not onlythe profitability, but also the reputation of firm at stake. With thecustomers' expectations becoming more, and managing lead timesof products is becoming very challenging, unless due attention isgiven to risk assessment exercise, the probability of supply chainfailure is high (Khan et al., 2008, 2012). Not only has this, but thecost of break cascaded across the businesses also and it may im-pact the other end showing the ripple effect (Ritchie and Brindley,2007a, 2007b; Braunscheidel and Suresh, 2009; Neiger et al.,2009; Yang and Yang, 2010). On the other side, Cousins et al.(2004) elucidate the consequences of failure to manage risks ef-fectively. Several papers discuss on risk identification and assess-ment methodologies (Chopra and Sodhi, 2004; Lavastre et al.,2012). Various models for supply chain risk management havebeen proposed in the recent years (Olson and Wu, 2008, 2011;Pfohl et al., 2011; Giannakis and Louis, 2011; Xia and Chen, 2011;Manuj and Sahin, 2011; Cagliano et al., 2012; Kern et al., 2012;Rossi and Pero, 2012; Zegordi and Davarzani, 2012; Klibi andMartel, 2012; Chiu and Choi, 2013; Li and Womer, 2012). Table A2gives summary of various modeling studies conducted in supplychain management field so far. Some of these models help busi-nesses to identify their risks and give a direction for continuityplan by evolving the mitigating strategies as well (Juttner, 2005).

The means of managing the risks is very unique to individualbusiness. To support that, Juttner et al. (2002, 2003) suggest in-vestigating risk management in different supply chains and de-veloping strategies based on their environments. Risk assessment

V.G. Venkatesh et al. / Journal of Retailing and Consumer Services 26 (2015) 153–167 155

process is the most imperative step in risk management domainand it starts from product development stage (Ghadge et al., 2013).Further, phases of managing these risks can vary from identifica-tion/analysis or estimation through risk assessment to variousways of managing risks (Norrman and Jansson, 2004). Never-theless, very few papers show orientation on the specific domainconcentration such as food business (Vorst et al., 1998; Diabatet al., 2012; Wang et al., 2012), manufacturing (Farooq and O’Brien2010), electronics industry (Sodhi and Lee, 2007), toy industry(Johnson, 2001), automotive and electronics domain (Craigheadet al., 2007; Blos et al., 2009; Wagner and Bode, 2009), aerospaceindustry (Haywood and Peck 2004), chemical supply chains(Kleindorfer et al., 2003, 2005), retail outsourcing (Tsai et al., 2008)In addition, many risks are studied in detail in the research works(Braithwaite, 2003; Fitzgerald, 2005; Trent and Monczka, 2005;Choi and Krause, 2006). Ritchie and Brindley (2007a, 2007b)analyzed further with respect to risk context drivers, decisionmakers, risk management responses with performance outcomesand influencers for risk management decisions. It is established byHallikas et al. (2002, 2004) and Kern et al. (2012) that structuredresearch on risk management domain is to be established withpossibilities of identifying, controlling and monitoring the riskfactors. Moreover, the organizations are not aware about the vul-nerability of their supply chain threats, irrespective of their do-main of operations and though there are more variations exists inthe form of wrong usages and misconceptions, it is due to otherfactors such as the absence of common specification, perceptionand difference in needs (Mullai et al., 2008; Wieland and Wal-lenburg, 2012). Christopher et al. (2011) argue for a good scope instudying from sourcing and design point of view. There are someresearch reports from clothing industry evaluate risk from thesourcing perspective (Masson et al., 2007; Kam et al., 2011; Vedeland Ellegaard, 2013). Lendaris (1980) has defined a way to modelthe risks and an integrated structural model has been proposed byHachicha and Elmsalmi (2014) using ISM approach. Our paper hasendorsed that methodology and tried to give a discrete approachtowards the risk factor modeling and calculations for the Indianapparel industry, which is one of the complex networks. From theabove review, it can be established that analysis of the domainspecific risk management practices is of high interest and hence,scope for researching apparel retail domain can be established.Next part of the review details about the retail industry and fol-lowed by apparel domain in India.

2.1. Indian apparel retail industry

Indian retail industry is the second largest employer afteragriculture (around 8 percent of the population) and it has thehighest number of outlets in the world. Despite that advantage,the industry is at the nascent stage (Garg, 2010). According to themarket research report study (2013), the retail market in Indiagrew at a CAGR of 12.47 percent during the period 2007–2012 andwill grow at a rate of 13.23 percent from 2012 to 2017. Increasingurban demographics, rapid development of shopping malls, rais-ing brand-conscious customers, and strong influence from theWestern world are changing catalysts of Indian retail industry(Halepete and Iyer, 2008). The paper also argues that low level oforganized retail penetration, coupled with an ineffective supplychain, characterizes the infrastructure of the Indian retail industry.Moreover, retail industry in India is becoming so adaptive andanticipative, as they flexible enough to meet the demand ofchanging customer markets in (Ramesh et al., 2008). Further, Da-bas et al. (2012), elucidate that Indian retail industry is dominatedby multi faceted tax systems and very poor infrastructure. In thecurrent scenario, studies on the current state of Indian retail alongwith strategies for growth would have immense significance for

international retailers vying to enter the Indian market (Batra andNiehm, 2009). Also, Indian retail environment is dominated by theapparel retailing through organized and unorganized formats ofretailing. Though the records for later does not exist, there are fewreports support the data for organized one (Technopak Report,2014). Early studies in Indian retail industry such as Sahay andMohan (2003) confirm that almost one-third of the Indian com-panies had no supply chain strategy. But the influence the westernfirms on the supply chains, supply chain is clearly a visible elementacross the business (Anbanandam et al., 2011). It varies fromproduct to product.

Apparel business in retail has a share of US$41 billion, which ispoised to grow around US$64 billion by 2018 (Technopak Report,2014). Currently, online retail is also booming up in India. It con-stitutes merely 5–7 percent of the apparel market in India, but it isexpected to grow at a CAGR of more than 35 percent in the next 10years (Market Research Report, 2012). Further, with FDI decisionpending in policy environment, no doubt that retail supply chaingets focus in the research, as many players are very keen enteringIndia with their complete global experience (Mann and Byun,2011). The main business points to be managed are: late deliveries,poor quality and design issues (Khan et al., 2008). However, due tothe individualistic in nature, many of the strategies to be adoptedin Indian market should be niche and unique to manage regionalsituations. Unless the companies assess the risks to be managed,more damages can be predicted in the supply chains, and it isbecoming challenging in the clothing trade as product life cyclesare getting shortened (Khan et al., 2008).

There are positive signs that Indian apparel retailers are con-sidering their supply chain management operations as a strategictool in their overall business strategy like their western counter-parts, instead of viewing it as an operational one. The studies onrisk management in clothing supply chain are also at the begin-ning stage. Moreover, organized retail business in India is domi-nated by the apparel domain (Technopak Report, 2014). Further,Indian apparel retail chains are moving through the professionalapproach in their operations and because of that uncertainties areimplicitly built in. It is very much helpful to study these risks indetail pertaining to the specific domain, as some of the supplychain risk factors are highly influential in the Indian scenario.Thus, the literature review also gives the scope for new directionfor research on risk management focus in the Indian apparel retailsegment. It has been clearly established in the review that riskstudies exist in the other domains and current one in this paper ispioneer for the Indian retail segment and possible extension of theestablished framework can also be proposed in addition to that.The next phase of the article gives overall research methodologyand ISM model building with the proposed risk prioritizationframework followed by the managerial explanations.

3. Research methodology

The main purpose of this paper is to develop contextual re-lationships to analyze the costs associated with risks and prioritizethem. The occurrence of one risk gives rise to multiple risks re-sulting into a domino effect which makes it very important for themanagers to control these risks before they occur. A group ofpractitioners have been identified to develop the ISM model toshow the relationships between various risks involved in thesupply chain. The results of ISM are further extended using thefuzzy MICMAC analysis to identify the driving and dependencepower of each of these variables. A risk calculation method hasbeen proposed further using the driving and dependence power topriorities these risk to help managers decide on the most criticalrisk to mitigate.


3.1. ISM – why and how?

Interpretive structural modeling (ISM) is a process that trans-forms unclear and poorly articulated mental models of systemsinto visible, well-defined models useful for many purposes (Sushil,2012). It is a structural relationship diagram which makes it easyto visualize the inter relationship between various elements. Inother words, it helps in presenting a complex system in a sim-plified format. It enables to make a mind map of elements whichdepend on one another to form a complex relationship. Themethod has some limitations and it has been subsequently dis-cussed. The model development is described in step by step ap-proach in the next section. ISM facilitates the identification of thestructure within a system. Following are the steps involved in theISM methodology (Sage, 1977; Jharkharia and Shankar, 2004; Fai-sal et al., 2006; Sushil, 2012):

(1)
Identification of variables: The key variables of the system areidentified using literature study and brain storming sessionswith the industry experts and academicians.
(2)
Contextual relationship: A contextual relationship is identifiedamong each variable (identified in step 1) with respect towhich the pairs of variables would be examined. The con-textual relationship is in the form of a matrix called thestructural self-interaction matrix (SSIM).Notations used to develop the SSIM:V: Risk variable i leads to variable jA: Risk variable j leads to variable iX: Risk variable i leads to variable j and vice versaO: No relationship between the variables
(3)
Initial Reachability Matrix: The SSIM is then converted into abinary matrix, called initial reachability matrix by substitutingV, A, X and O by 1 and 0 as per the following rules:Rule 1: If the (i, j) entry in the SSIM is V, then the (i, j) entry inthe reachability matrixbecomes 1 and the (j, i) entry is 0.Rule 2: If the (i, j) entry in the SSIM is A, then the (i, j) entry inthe reachability matrixis 0 and the (j, i) entry becomes 1.Rule 3: If the (i, j) entry in the SSIM is X, then the (i, j) entry inthe reachability matrixbecomes 1 and the (j, i) entry also becomes 1.Rule 4: If the (i, j) entry in the SSIM is O, then the (i, j) entry inthe reachability matrixbecomes 0 and the (j, i) entry also becomes 0.
(4)
Transitivity check: The reachability matrix is developed fromthe SSIM and the matrix is checked for transitivity. The tran-sitivity of the contextual relation is a basic assumption made inISM. It states that if variable A is related to B and B is related toC, then A is necessarily related to C.
(5)
Levels: The transitivity matrix obtained in step (4) is convertedinto the canonical matrix format by arranging the elementsaccording to their levels.
(6)
Building the ISM model: Variables in each level are thenconnected based on their relationships as defined in thestructural self-interaction matrix.
3.2. Identification of the variables

3.2.1. Delphi methodologyDelphi process is an effective empirical tool to get a consensus

from a group of experts (Linstone and Turoff, 1975; Buckley, 1995;Schmidt, 1997). The technique has been used systematically byinvolving practicing professionals to conclude opinion on thesubject to be researched. It has been used in the different areas

such as for strategic decision making, policy formulation and todraw the conclusions based on convergence in the multifacetedcomplex ideas (Czinkota and Ronkainen, 2005; Grisham, 2009).Although the survey methodology could be performed in thecurrent study, Delphi technique has been used as a tool to initiateresearch direction in the selected domain. It is because, thestructured study on Indian apparel retail supply chain is currentlyat a nascent stage. The technique aims to gather study and finalizethe facts on various barriers from in-depth query of experts andstakeholders with the practical context. The method has beenexecuted at steps; (1) Constitution of members for the expertpanel; (2) identification of the barriers and formulation of thefeedback system; (3) execution in the two rounds. In the first step,20 supply chain executives/managers having diversified back-grounds participating in the business decisions at the senior levelare chosen. This selection process was done through a structuredapproach proposed by Okoli and Pawlowski (2004) and also localpopularity in Indian apparel business environment and theirwillingness to participate in this research study. Out of 20 parti-cipants, 14 people have participated in the two round Delphiprocess. In the second step, the possible list of factors of risks wasprepared. We have finalized 12 factors based on the ranking aswell as the concurrence from the participants (from the practicepoint of view) and well supported by literature as well. The nextsection describes the factors for the present study and their lit-erature support.

A supply chain is susceptible to many types of risk. We iden-tified and categorized risks into 12 distinct types that can becontrolled and mitigated if proper steps are taken. With increasein globalization, complexity and dynamism of supply chains areleading to greater exposure to risk from political and economicevents (Ghoshal, 1987; Harland et al., 2003; Manuj and Mentzer,2008; Holweg et al., 2011). Globalization increases the number ofcross country transaction in shipping goods from one place toother with Customs and regulation risk (Manuj and Mentzer,2008), Security and International Terrorism (Sheffi, 2001; Williamset al., 2008) change in cost of resource acquisition due to frequentfluctuation in currency rates. Next category of risk to an organi-zation is the awareness of Raw material and Product qualityrequirements of the products. Supply chain can be at risk due toregulatory compliance, quality requirements and product en-vironments (Cucchiella and Gastaldi, 2006; Tse et al., 2011; Li andWomer, 2012). At present, section of customers are becomingaware about the product quality and its raw material contentssuch as residuals of hazardous chemicals in the fabric processingespecially (such as dyeing and printing) and other unfriendlycontents in the various accessories (such as lead content in theshank buttons). For example, Increase in carbon foot print andpollution by the organization impacts society and this createsnegative perception among the common masses about the com-pany's image (Bickerstaff, 2004). Risk of scarcity of resources is amajor concern for an organization. A supply chain is dependent onthe resource for its functioning (Newman et al., 1993; Jones et al.,2000; Carter and Rogers, 2008). Unavailability of skilled manpower and the right technology to perform the task can prevent anorganization from functioning better. Information scarcity is a keyfacet of uncertainty in terms of the existence (Baird and Thomas,1990). Scarcity in availability of cotton can put the apparel in-dustry at risk by loss of customer due to increase the price of thefinish goods. Also, it will force the organization to search alternateresource or supplier. Supplier uncertainty to deliver goods at theright time is the next category of risk. Suppliers' uncertainty torespond to changes in demand leads to the decline in the marketshare. Stakeholders' bankruptcy or mishap increases the un-certainty of supplier in fulfilling the demand (Krause and Hand-field, 1999; Chopra and Sodhi, 2004; Cousins et al., 2004;

Table 2Structural self relationship matrix.

R12 R11 R10 R9 R8 R7 R6 R5 R4 R3 R2

R1 V V O O V V O O V A VR2 O O V O O V O O O A –

R3 O V O O V O O V V – –

R4 O V O O V O O A – – –

R5 X V O O V V A – – – –

R6 V V O O V V – – – – –

R7 A V V O X – – – – – –

R8 A V V A – – – – – – –

R9 O V O – – – – – – – –

R10 A V – – – – – – – – –

R11 A – – – – – – – – – –

R12 – – – – – – – – – – –


Nembhard et al., 2005; Simangunsong et al., 2012; Vedel and El-legaard, 2013). Lack of Alignment/co-ordination among theplayers of supply chain is categorized as a risk in the supply chain.Misalignment, resulting from lack of transparency among theplayers or lack of communication co-ordination or interactionleads to supply chain breakdown (Cucchiella and Gastaldi, 2006;Shen et al., 2013). Information sharing among the members of thesupply chain is vital and lack of information leads to uncertainty,chaotic behavior and unnecessary costs (Childerhouse et al., 2003).Behavioral aspect of employee is a risk to an organization. Theoccurrence of frequent labor turnover affect both financial andreputation of the organization. Resistance to change and misuse oforganization's assets and employee disputes are common beha-viors observed in most industries and these behaviors can act as abottleneck to an organization. Next category of risk is due to im-proper support services and can be categorized as Infrastructurerisk. It affects the operational activity and can cause the supplychain to standstill. Lack of sufficient equipment, transportationbreakdown, warehouse or IT Breakdown (Pfohl et al., 2011) canprevent the supply chain from function smoothly. Also, Delay inschedule/lead time, next factor of risk, can prevent a supply chainin putting the product into the market at the right time. The risk ofdelay in production (Pfohl et al., 2011) or information or executioncan affect the supply chain severely. Lead time in case of in-novative products like fashion apparel should be as low as possi-ble, slight delay can increase the risk of failure for an organization(Cucchiella and Gastaldi, 2006; Pujawan and Geraldin, 2009) Delayin return process impacts the reverse supply chain of an organi-zation. Demand uncertainty due to frequent fluctuation in con-sumer demand or inaccurate forecasting can be a cause of bull-whip effect in the Supply chain. Risk due to demand uncertaintycan impact its reputation and even take it out of business (Chris-topher and Lee, 2004; Cucchiella and Gastaldi, 2006; Manuj andMentzer, 2008; Simangunsong et al., 2012). Satisfying customer'sneed is one of the goals of a supply chain and customer dis-satisfaction can be a major risk to an organization. Frequent stock-out, poor quality of product causes customer dissatisfaction lead-ing to customer complaints and product return. Delay in returnprocess (Pujawan and Geraldin, 2009), non-availability of product(Meulbrook, 2000) or less degree of customer interaction (Mitch-ell, 1995) increases the risk due to customer dissatisfaction. Inorder to keep customer happy, the assets and resources of theorganization must be protected from the misuse, mishaps andtheft (Pfohl et al., 2011). The risk due to security and safety can befatal to an organization. Security risk relates to adverse events thatthreaten human resources, operations integrity, and informationsystems; and lead to outcomes such as freight breaches, datastealing, vandalism, crime, and sabotage (Manuj and Mentzer,2008). Not only asset protection, keeping the employee safe andsecure should be a concern of the organization. Security needs ofboth core supply chain entity and outsourcing organizations arecreators of risk (Guinipero and Eltantawy, 2004). Further, Khanand Creazza (2009) advocate the new dimension of the risks withthe product design and supply chain interface which is controlledby prominently by the irregularities in product quality supply aswell. The last category of risk is the financial risk. One of theobjectives of the organization is to make profit. To stay profitable,the cash flow should be managed properly. Managing financials foran organization is the biggest challenge and risk of financial mis-management can lead to downfall of the organization. In manybusiness sectors, an industry or an organization delivers the goodsor service to its customer on credit. Debtors default (Meulbrook,2000) affects the cash flow severely thereby increasing the fi-nancial risk of the organization. Mitigating financial risk leads tosmooth flow of cash and keep organizations profitable (Kleindorferand Saad, 2001; Hendricks and Singhal, 2005; Arcelus et al., 2012).

3.3. Interpretive structural modeling (ISM)

The technique, ISM, proposed by Warfield (1974) is qualitativein its approach. Many researchers used this methodology to directorder and decompose the complexity of relationships among ele-ments (Sage, 1977; Mandal and Deshmukh, 1994; Ravi and Shan-kar, 2005; Sahney et al., 2006; Faisal et al., 2006; Faisal et al.,2007). The model uses judgment from group members and es-tablishes the connection amongst the elements (Mandal andDeshmukh, 1994; Gorane and Kant, 2013). There are other appli-cations of ISM in other areas as well. Some representative appli-cations are: world-class manufacturing (Haleem et al., 2012), de-cision making (Lee and Rhee, 2011), Value chain management(Mohammed et al., 2008), Product design (Lin et al., 2006), Wastemanagement (Sharma and Sushil, 1995), Vendor selection (Mandaland Deshmukh, 1994), Supply chain management (Agarwal et al.,2007), and so on. It is used to explore contextual relationshipwhere they are mutually related. Following are the representativeworks of ISM model including Analysis on agile factors for theproduct launch (Chang et al., 2013), Barriers of Eco-friendly man-ufacturing adoption (Mittal and Sangwan, 2011, 2013), Analyzingthe barriers for Six Sigma program implementation (Soti et al.,2011), Analyzing the barriers for energy saving in China (Wang etal., 2008), Critical factors of ERP implementation (Jharkharia andShankar, 2004).

The model is built using the industry experts' practical ex-perience and the knowledge base of academicians to decompose acomplicated system into several sub-systems and construct amultilevel structural model. The variables of the structural self-interaction matrix in this paper are the types of risks involved inthe supply chain of an organization. The risks are identified fromliterature review and expert interactions. More than 100 differenttypes of risks were identified which can have an impact on thebusiness. We include only the most common and general type ofrisks which generally occur in almost every industry. The devel-oped model would be a generic one and can be modified as per thespecific objectives of the company. Two academicians and six in-dustry experts were consulted to develop the SSIM. Table 1 showsthe various risks considered to develop the ISM.

The structural self-relationship matrix and initial reachabilitymatrix are developed as per the steps and rules discussed in ISMmethodology section. The SSIM, initial reachability matrix andtransitivity matrix for our model are as shown in Tables 2, 3, and 4respectively.

Through the Structural Self Relationship Matrix, we can havefollowing relationships established. The risk factor globalizationleads to various other risks such as supplier uncertainty, increasein the product quality standards (R2), and infrastructure risks (R7)such as the organized retailing including the technology up

Table 3Initial reachability matrix.

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12

R1 1 1 0 1 0 0 1 1 0 0 1 1R2 0 1 0 0 0 0 1 0 0 1 0 0R3 1 1 1 1 1 0 0 1 0 0 1 0R4 0 0 0 1 0 0 0 1 0 0 1 0R5 0 0 0 1 1 0 1 1 0 0 1 1R6 0 0 0 0 1 1 1 1 0 0 1 1R7 0 0 0 0 0 0 1 1 0 1 1 0R8 0 0 0 0 0 0 0 1 0 1 1 0R9 0 0 0 0 0 0 0 1 1 0 1 0R10 0 0 0 0 0 0 0 0 0 1 1 0R11 0 0 0 0 0 0 0 0 0 0 1 0R12 0 0 0 0 1 0 1 1 0 0 1 1

Table 4Transitivity matrix.

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12

R1 1 1 0 1 1n 0 1 1n 0 1n 1 1R2 0 1 0 0 0 0 1 1n 0 1 1n 0R3 1 1 1 1 1 0 1n 1 0 1n 1 1nR4 0 0 0 1 0 0 1n 1 0 1n 1 0R5 0 0 0 1 1 0 1 1 0 1n 1 1R6 0 0 0 0 1 1 1 1 0 1n 1 1R7 0 0 0 0 0 0 1 1 0 1 1 0R8 0 0 0 0 0 0 1 1 0 1 1 0R9 0 0 0 0 0 0 1n 1 1 1n 1 0R10 0 0 0 0 0 0 0 0 0 1 1 0R11 0 0 0 0 0 0 0 0 0 0 1 0R12 0 0 0 1n 1 0 1 0 0 1 1 1

Table 5Level 1 of risk variables.

Variables Reachability set Antecedent set Intersection Level

R1 1,2,4,5,7,8,10,11,12 1,3 1R2 2,7,8,10,11 1,2,3 2R3 1,2,3,4,5,7,8,10,11,12 3, 3R4 4,7,8,10,11 1,3,4,5,12 4R5 4,5,7,8,10,11,12 1,3,5,6,12 5,12R6 5,6,7,8,10,11,12 6 6R7 7,8,10,11 1,2,3,4,5,6,7,8,9,12 7,8R8 7,8,10,11 1,2,3,4,5,6,7,8,9,12 7,8R9 7,8,9,10,11 9 9R10 10,11 1,2,3,4,5,6,7,8,9,10,12 10R11 11, 1,2,3,4,5,6,7,8,9,10,11,12 11 IR12 4,5,7,8,10,11,12 1,3,5,6,12 5,12



R1 1,2,4,5,7,8,10,12 1,3 1R2 2,7,8,10 1,2,3 2R3 1,2,3,4,5,7,8,10,12 3 3R4 4,7,8,10 1,3,4,5,12 4R5 4,5,7,8,10,12 1,3,5,6,12 5,12R6 5,6,7,8,10,12 6 6R7 7,8,10 1,2,3,4,5,6,7,8.9.12 7,8R8 7,8,10 1,2,3,4,5,6,7,8,9,12 7,8R9 7,8,9,10 9 9R10 10, 1,2,3,4,5,6,7,8,9,10,12 10 IIR12 4,5,7,8,10,12 1,3,5,6,12 5,12



R1 1,2,4,5,7,8,12 1,3 1R2 2,7,8 1,2,3 2R3 1,2,3,4,5,7,8,12 3 3R4 4,8,7 1,3,4,5,12 4R5 4,5,7,8,12 1,3,5,6,12 5,12R6 5,6,7,8,12 6 6R7 7,8 1,2,3,4,5,6,7,8,9,12 7,8 IIIR8 7,8 1,2,3,4,5,6,7,8,9,12 7,8 IIIR9 8,9,7 9 9R12 4,5,7,8,12 1,3,5,6,12 5,12



R1 1,2,4,5,12 1,3 1R2 2 1,2,3 2 IVR3 1,2,3,4,5,12 3 3R4 4 1,3,4,5,12 4, IVR5 4,5,12 1,3,5,6,12 5,12R6 5,6,12 6 6R9 9 9 9 IVR12 4,5,12 1,3,5,6,12 5,12



R1 1,5,12 1,3 1R3 1,3,5,12 3 3R5 5,12 1,3,5,6,12 5,12 VR6 5,6,12 6 6R12 5,12 1,3,5,6,12 5,12 V


gradation in the supply chain system. Again, this must lead to thefinancial risks in the form finding the new investments to cope upthe pressure. Further, globalization (R1) leads to the increase in therisks of security and safety of the cargo (R12), as the material passthrough various layers in the supply chains. But the scarcity ofresources (R3) triggered the material to be sourced through theglobalized partner and increased the standard of the material,which is also a potential risk for the supply chains which work onthe strict lead time basis. Further, uncertainty in suppliers and lackof alignment amongst are the potential risks which lead to scarcityof resources as well (R3). An organization facing the supply dis-turbance risk due to non alignment may delay the shipments andgives financial risk as well. Lack of coordination (R5) may lead tothe organization to look for additional technology (like tracking),space requirement (warehousing) which are potential infra-structure risks in the apparel supply chains. Behavior aspects (R6)such as unexplained absence in the retail selling floor and dis-tribution environments may lead to develop new systems in theinfrastructure such as monitoring the movements of staff andproducts within supply chain environments and they also delaythe delivery of the order which is very prominent in the Indianretail business. People do see no prominent relationship betweenthe infrastructure and delay in the shipments (R8) as it would leadto the customer dissatisfaction indirectly and eventually lead tothe financial risks by locking the investments in the retail chain. Ithas been observed that layers of security and safety (R12) to theproducts in the supply chains impact the delay of the delivery inthe system (R8). However, there is no clear linkage establishedbetween demand uncertainty (R9) and security and safety of thematerial (R12) and also customer dissatisfaction as it has beenprominently indicating the fulfillment of the orders and experi-encing the products (R10). Customer dissatisfaction risk and



R1 1 1,3 1 VIR3 3 3 3 VIR6 6 6 6 VI

Financial Risk (R11)

Customer Dissatisfaction (R10)

Infrastructure Risk (R7)Delay in schedule / lead time (R8)

Supplier Uncertainty (R4)Demand Uncertainty (R9)

Product quality and raw materials standards (R2)

Lack of co-ordination/Alignment(R5)

Scarcity of Resources (R3)

Behavioral aspect of Employees (R6)

Globalization (R1) Security and Safety (R12)

Fig. 1. Interpretive Structural Model for risk relationship in apparel retailcompanies.

Table 12Fuzzy direct relationship matrix.

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12

R1 0 0.7 0 0.5 0 0 0.3 0.5 0 0 0.5 0.3R2 0 0 0 0 0 0 0.5 0 0 0.5 0 0R3 0.5 0.1 0 0.7 0.3 0 0 0.7 0 0 0.7 0R4 0 0 0 0 0 0 0 0.7 0 0 0.7 0R5 0 0 0 0.5 0 0 0.3 0.5 0 0 0.3 0.3R6 0 0 0 0 0.3 0 0.7 0.7 0 0 0.5 0.7R7 0 0 0 0 0 0 0 0.7 0 0.5 0.5 0R8 0 0 0 0 0 0 0.3 0 0 0.7 0.5 0R9 0 0 0 0 0 0 0 0.5 0 0 0.5 0R10 0 0 0 0 0 0 0 0 0 0 0.7 0R11 0 0 0 0 0 0 0 0 0 0 0 0R12 0 0 0 0 0.3 0 0.5 0.3 0 0.3 0.7 0


financial risks (the fear of losing the amount in the supply chainsdue to theft) together may lead to the safety and security of thegoods in the supply chains. The next step is to create the reach-ability matrix as per the procedure.

The next step is to convert the transitivity matrix to the ca-nonical matrix format by arranging the elements according totheir levels. Tables 5–10 divide the variables into ISM levels.

Based on the six levels derived, a structural model is designed.A relationship between two variables (here risks) is shown by anarrow which points from a higher level variable to a lower levelvariable. It implies that the higher level variable leads to the lowerlevel variables. Lower level variables are at a higher level in theISM hierarchy and are driven by the higher level variables. The ISMmodel for the interrelationships between the risks is shown inFig. 1.

The ISM uses SSIM to define the relationship among risks. TheInitial Reachability Matrix is a binary matrix with 0 and 1. A ‘1’denotes a relationship between the two risks and a ‘0’ denotes norelationship. It implies that we have considered only extreme

Table 11Fuzzy relationship scale.

No. Very weak Weak Moderate Strong Very strong Perfect

0 0.1 0.3 0.5 0.7 0.9 1

levels of relationships between the risks. To be more precise withthe strength of relation between the any two variables, we need toconsider the gray area between 0 and 1. We therefore analyze therisk for their driving and dependence power using fuzzy MICMACanalysis. The fuzzy direct relationship matrix is formed using ex-pert opinion on the strength of relationship between the variables.The following scale is used to define the strength of relationship(Table 11).

Here, 0 denotes no relationship and 1 denotes perfect re-lationship. Other times the variables may or may not be stronglyrelated. Sometimes a risk may lead to the other and sometimes itmay not. This gray area is defined by the above mentioned scale.The fuzzy direct relationship matrix is shown in Table 12.

The sum of all the row elements gives driving power of cor-responding risk variables and sum of all the column elementsgives the dependence power of corresponding risk variables. Thefuzzy direct relationship matrix is recursively multiplied by thebinary direct reachability matrix until a Fuzzy MICMAC stabilizedmatrix is obtained. A stabilized matrix is one for which the drivingand dependence power is constant for at least last two iterations.The binary direct reachability matrix is obtained by replacing rightdiagonal elements in the initial reachability matrix by 0. In thisparticular model, the two matrices are given in Tables 13 and 14along with the driving and dependence powers of risk variables instabilized form. The variables are further divided into autonomousand linkage variables along with driving and dependent ones.

4. Findings and discussions

ISM model establishes interactions amongst 12 risk variables.The graph in Fig. 2 derived using the results of fuzzy MICMAC. Thefirst quarter consists of 5 (R3, R6, R1, R12 and R5) risks as In-dependent variables or driving ones. The ISM endorses the in-crease in globalization (R1) the complexities of the apparel supplychain would lead to various other risks in retail business. It isexpected to play a key role as Indian customers used to see theonly two seasons normally in the fashion life cycle such as eitherSpring–Summer (SS) or Autumn–Winter (AW). Due to theawareness of customers, retail companies are in turnaround phaseto introduce more number of seasons to reduce the lifecycle oftheir merchandise. Moreover, the transfer of global designs to In-dian retail environments is also a perceived risk as Indian custo-mers are having varied demands in the market. Safety and security(R12) in the supply chains from Indian context is always a concernfor supply chain managers as Indian retail chains are prone topilferages and shrinkages across various steps in the order ex-ecution. It is appropriate by having it as a high driving power alongwith behavioral risk from the labor, as both of them will form the

Table 14Fuzzy MICMAC stabilized matrix.

Variable R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 Driving power

R1 0 0.7 0 0.5 0.3 0 0.7 0.7 0 0.7 0.7 0.3 4.6R2 0 0 0 0 0 0 0.5 0.5 0 0.5 0.5 0 2R3 0.5 0.5 0 0.7 0.5 0 0.7 0.7 0 0.7 0.7 0.5 5.5R4 0 0 0 0 0 0 0.7 0.7 0 0.7 0.7 0 2.8R5 0 0 0 0.5 0.3 0 0.5 0.5 0 0.5 0.5 0.3 3.1R6 0 0 0 0.7 0.7 0 0.7 0.7 0 0.7 0.7 0.7 4.9R7 0 0 0 0 0 0 0.7 0.7 0 0.7 0.7 0 2.8R8 0 0 0 0 0 0 0.3 0.3 0 0.7 0.7 0 2R9 0 0 0 0 0 0 0.5 0.5 0 0.5 0.5 0 2R10 0 0 0 0 0 0 0 0 0 0 0.7 0 0.7R11 0 0 0 0 0 0 0 0 0 0 0 0 0R12 0 0 0 0.3 0.3 0 0.5 0.5 0 0.5 0.7 0.3 3.1Dependence Power 0.5 1.2 0 2.7 2.1 0 5.8 5.8 0 6.2 7.1 2.1

Table 13Binary reachability matrix.

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12

R1 0 1 1 1 0 0 0 0 0 0 0 0R2 1 0 1 0 0 0 0 0 0 0 0 0R3 0 0 0 1 0 0 0 0 0 0 0 0R4 0 0 0 0 1 0 0 0 0 0 0 0R5 1 0 1 0 0 1 0 0 0 0 0 0R6 0 0 0 0 0 0 0 0 0 0 0 0R7 1 0 0 0 1 1 0 0 0 0 0 0R8 0 0 1 1 1 1 1 0 1 0 0 0R9 1 0 0 0 0 0 0 0 0 0 0 0R10 0 1 0 0 0 0 1 1 0 0 0 0R11 1 0 1 1 1 1 1 1 1 1 0 1R12 1 0 0 0 1 1 1 0 0 0 0 0


risks at the bottom level. The model also positions the scarcity ofresources in the driving quadrant as Indian retail industry stillsuffers with trained manpower and technology adoption due tothe unavailability. It may be due to untrained manpower to un-derstand the complexity of the business. The study also shows thatthere is a lack of coordination and alignment problems withinapparel industry itself. One of the participants in the study whoheads the Product Development function in the leading retailchain supports this with her quote “This problem is a perennialone for the Indian industry and it needs some maturity to adaptand to compete with the Global retail chain. Further, alignmentproblem starts at product development stage, where we need to

R1

R2

R3

R4

R6

R7

R8R9

R10

R11

R12, R5

0

1

2

3

4

5

6

0 1 2 3 4 5 6 7

Driv

ing

Pow

er

Dependence Power

Independent Variable

Linkage Variable

Autonomous Variable

Dependent Variable

Fig. 2. Cluster of risks.

convert the consumer taste and participate in designing the supplychains”. This is really impacting the business performance of In-dian retail chains.

Supplier uncertainty (R4) remains as the transient variablebetween autonomous and driving quadrant. The risk involved hereis that suppliers for apparel retailers are not consistent in handlinglow volumes and also not responding quickly to the customers dueto their innate operating conditions. Their performance also canalso be driven by various other factors. The second cluster maps“autonomous variables” that have weak dependence and drivingpowers. These barriers are highly disconnected from the systemand can lead individual effects. Raw material/product qualitystandards (R2) and uncertainty in demand management (R9) arethe members of this cluster. It really endorses the statement fromthe Delphi participants that Indian retail industry is still at thenascent stage to implement scientifically designed demand man-agement program. “Moreover, this poor demand managementcould be due to many reasons such as: No evidence of early sup-plier involvement (ESI) and vendor managed inventory (VMI) inthe retail operations citing them as the sophisticated techniques”.(Quote from One of the Delphi participants, working with LeadingRetailer in India). Indian apparel retail professionals show littlereluctance in adopting those strategies due to their conservativeoutlook. Further, they are not clear with the specification re-quirement and it is subjected to change in terms of quality. Thepoor adoption of accepted standards like American Society ofTesting Materials (ASTM) and American Association of Textile Che-mists and Colorists (AATCC) in the materials and delivery standardsalso established as a risk in the supply chain system. Both theabove factors act as autonomous effect on the business. The impactcould obstruct the growth of Indian retail in the Internationalarena also. Moreover, Indian retailers get confused with thestandards to be followed. These risks are major from the customer


point of view, and new to them, as till now, they act as a manu-facturing hub. The next quadrant, dependent variables, has3 variables as full members (R8, R10 and R11) and R7 acts as thetransient variable between dependent and linkage quadrant. In-frastructure Risks (R7) exhibits strong dependence and drivingpower and should be given more focus than the others. Infra-structure implies warehouse management, logistics network andbasic transport needs, regulatory compliance with respect totransport and etc. India does not have the well established infra-structure and regulatory framework in terms of apparel retail in-dustry with the absence of detailed standard operating proceduresfrom the controlling authorities. Though this risk is dependent onmany factors, it may cause the delay in the establishment process.

Financial risk (R11), which has high risk in cash conversioncycle; low market share; low profit margins; decreasing revenuesand etc., is the result or effect of the risks all below in the hier-archy. This is the continuing problem in Indian retail environment,as fellow industry respondents also agreed on clutching the priceat the supplier side, without knowing that slowly their efficiencyof deriving the product at the low price to the customers is alsodeclining. It will result in many suppliers to go bankrupt anddisinterest in sustaining the business. And, it has high dependencepower along with the other risks like customer dissatisfaction(R10) and delay in schedule lead time (R8). Lead time managementin apparel industry is always a challenging task as Indian designindustry does not enjoy recognized and an independent status,still has huge influence on the western and eastern counterpartsfor following up trends and forecast. Further, similar to other in-dustries, it has a close relationship with customer feedbacks.However, the impact could be, Indian retail industry could enjoythe benefits from established supply base. Retail managers parti-cipated in the discussions concurred with the fact that retailbuyers are not taking the lead time analysis should be leveled as avalue adding activity in the system. But it does not support thatand it has the side way of analysis.

This methodology will also give a true picture of the criticalityof the risks when it is analyzed from the particular company pointof view. The trend may not be always the same and it is one of thelimitations of this analysis. This modeling exercise can also beapplied to other business areas where the interdependence be-tween variables needs to be identified or the root cause of someproblem is analyzed. If the dependence power of the variable iszero, it would signify that the variable is one of the root causes ofthe problem. A higher dependence power may have to be exploredfurther to analyze for the root cause. On the other hand, a variablewith zero driving power is the effect of all other factors.

The traditional cause effect or fishbone or Ishikawa diagramwhich is being used by companies to find the root cause of afailure/problem can be replaced by Interpretative StructuralModeling (ISM). Visually, ISM becomes easier to understand therelationship between various interrelated factors leading to aparticular effect. Sometimes, those cause effect diagrams maybecome very complicated and does not give the interdependenciesbetween the variables. Also, ISM model is analogous to one of thenew quality control tool i.e. the relations diagram used to explorethe cause and effect relationship where the causes are likely to bemutually related. Further, this ISM model of risks and MICMACanalysis are helping us to propose a new calculation method,leading to be the unique contribution for ISM and Risk literaturefrom this research work.

5. Risk prioritization: proposed model

The prioritization of risk is essential for the managers to focuson a few risks which act as drivers of other risks. Various models

like the Failure Mode Effect Analysis (FMEA), Risk Benefit Analysis(RBA), and Cost Benefit Analysis (CBA) have been developed toprioritize risk based on factors such as the probability of occur-rence, severity, and the detection ease (Khan et al., 2008). Thesemodels have been accepted by many and criticized by others forremoving the element of human judgment (White, 1995). Kraljic(1983) proposes portfolio matrix using risk model as a base factorand Caniels and Gelderman (2005) gives the dependence anddriving factors perspective. Further, there has been a debate be-tween those who see risk as objective and those who argue thatrisk is subjective (Yates and Stone, 1992; Bernstein, 1996; Moore,1983; Frosdick, 1997; Spira and Page, 2003). Some of works on riskcalculation methodologies have been extensively done by Ritchieand Brindley (2007a, 2007b) and Rao et al. (2011). Our paper en-dorses the view expressed by Lupton (2005) that risk ranges be-tween the techno-scientific perspective, which sees risk as ob-jective and measurable, to the social constructionist perspective,and also sees it as being determined by the social, political andhistorical viewpoints of those concerned. The models mentionedabove do not take into account the interdependence between thevarious risks. Occurrence of any event is a chance that any eventwill occur. But the events are not independent. ISM has shown aparticular risk is a driver of multiple other risks. It may also bedriven by other variables i.e. it may be dependent on various otherfactors. This is one of the major advantages of ISM over Ishikawadiagrams. In this model, we consider inter relationship betweenthe variables to prioritize the risks along with severity or costimpact and the ease of detection of the risk.

As far as risks in supply chain it is very difficult to assign aprobability of occurrence to any particular event because of theuncertainty associated with it. Risk is measurable and can be esti-mated from the probabilities of the outcome. But, uncertainty is notquantifiable and probabilities of the outcome are not known (Knight,2012). Yates and Stone (1992) also argue that risk implies uncertaintyabout the prospective outcome and if the probability of the outcomeis known then there is no risk. Slack and Lewis (2001) discuss boththe points. With both these arguments in existence, here we con-sider occurrence of the risks to be uncertain and very difficult tomeasure in terms of probability. Thus, with the existing FMEA as abase framework, we propose a newmodel to prioritize risk using theresults of Fuzzy ISM. The driving and the dependence power of eachvariable derived from the fuzzy MICMAC analysis replace the factor“occurrence” in the current risk prioritization number formula(RPN¼Occurrence� Severity�Detection). With this existing one, itis very difficult to quantify the exact probability of occurrence. So,the factor “driving power” divided by “dependence power” is pro-posed to be used as a measure of the occurrence of the uncertaintyor risk. Higher the factor, chance of the occurrence of the event ismore. Thus, it helps us to quantify the strength of occurrence in thesupply chain system.

So, Risk Prioritization Number can be found out by using thenew formula based on ISM – MICMAC analysis:

Risk Prioritization Number

severity detectiondriving power

dependance power= × ×

The higher the cost or severity associated with a risk higher will bethe criticality of the risk. More the driving power of the risk, its abilityto initiate other problems in the supply chain is high. So, risks withhigher driving power must be given higher priority. Similarly, thehigher the dependence power of a risk, more variables lead to thisparticular risk. The main focus must be shifted to it root causes i.e. itsdrivers and a lower priority will be assigned to risk with higher de-pendence power. Risk with highest dependence power is more of aneffect than a cause to any other event. Thus, the above mathematical


formula can be used to prioritize risk taking into account the severity,detection and mutual dependence of the variables or risk in thisparticular case. This can be applied in any industry which is havingfuzzy ISM (MICMAC) analysis. While using the risk prioritizationproposed formula above, the factors having zeroed driving or depen-dence power should not be ranked using the method for the particulardomain. The variables can be directly assigned priority by qualitativeanalysis of efforts to mitigate the particular cause or using the costassociated with it. However, it needs to be validated through thevarious empirical as well as the quantitative frameworks.

6. Managerial implications of the study

With the supply chains are operating under uncertainties,studies pertaining to supply chain risk management are becomingvery practical and relevant according to the chosen business do-main. Risk calculation methodology also should be a practicebased one and it eventually supports strategic and decision mak-ing process in a supply chain (Tummala and Schoenherr, 2011).This paper endorses the practice based research from Indiancontext. The present study also proposes a new model for RPNcalculations, which is going to be an imperative and basic tool formajor supply chain practitioners and organizations in the riskanalysis. The retail strategists/managers can use ISM frameworkwithin their service environment to classify risk factors dependingon their impact on the supply chain system using the structuredapproach. Further, these elements can also be useful for derivingRPN values to rank based on the driving and dependence power.Followed by that, a comprehensive plan for the supply chain riskmitigation plan can also be designed. The study gives directions onrisk evaluations with a discrete approach. Along with the analysisof current situation inside the company with respect to risks, it isequally important to anticipate the future. One of the methodscould be pure play analysis of companies from the same domain.Thus, by giving importance to supply chain risk management, acompany can reduce extra costs and improve their bottom line.Also, retail chains trying to enter India or currently operating canalso apply these model building and the findings of this paperwould help them to design the strategies for mitigating those riskfactors.

7. Limitations and scope for further research

The ISM model developed here is based on the expert opinion andit may be biased and limited to a particular industry that they belongto. The links established using ISM may be tested for validity using

Table A1Risk assumptions.

Risk no. Risk Background of risks

R1 Globalization Currency fluctuations; design transferR2 Raw material and product quality Retailers do not have the complete SOR3 Scarcity of resources Scarcity of raw material; power shortR4 Supplier uncertainty Failure to deliver on time; supplier baR5 Lack of co-ordination/alignment Lack of communication; no cross funcR6 Behavioral aspect of employees Employee disputes; inefficient/unskilleR7 Infrastructure risks Transport breakdown; inadequate meaR8 Delay in schedule/lead time Order fulfillment error; change in prodR9 Demand uncertainty Error in demand forecast (short termR10 Customer dissatisfaction Product returns; customers complaintR11 financial risk High cash conversion cycle; low markR12 Security and safety Pilferages and shrinkage of the materi

Structural Equation Modeling (SEM). The variables under considera-tion are very limited and generic as this is the initial phase of thestudy. These variables can have the multitude effect having a differentdegree of inter-relationships. This model and methodology will befoolproof when applied to a single company environment, where thecosts and frequency of occurrences of disruptions are recorded in thehistory to prioritize risk. Depending on the situation, mitigation stra-tegies can be formulated taking into consideration the budget andefforts required. The risk prioritization model can also be validatedthrough various sub-domains/industry stakeholders across the supplychains. Case studies engaging different dynamics and business en-vironments can also be developed to ascertain the newly proposedRisk Priority Model.

8. Conclusion

It is very important for the managers to know and understandthe risks involved in apparel retail supply chains. And, the inter-dependence of the risks may also result into chain of risks thereincreasing the costs of mitigations. One risk may lead to variousother disruptions also causing domino effect. It is therefore es-sential to take preventive action after thorough analysis of eachrisk and prioritizing them using the suggested method. Theprobabilities of known risks must be carefully assigned by lookinginto past records. The cost of preventive action along with the costof corrective action can also be considered (cost benefit analysis).Results, in case the probabilities of future events, are not welldefined. Structural Equation Modeling (SEM) approach can besuggested in the future scope that could help manager to under-stand whether the retail professionals are on the same line withrespect to the interdependencies of risks or causes of a particularproblem. It would then help to take corrective action with totalemployee involvement and through other modes as well.

Acknowledgment

We would like to thank Editor-in-Chief and anonymous reviewersfor their feedback and inputs to enhance the quality of the paper. Also,we place our sincere thanks to Dr. Rameshwar Dubey for his con-tinued motivation and guidance to shape the article in the presentform.

Appendix A

See Tables A1–A3.

s, competition; legal and political risk; policy changes; etc.P of the product quality and it varies from season to season/and product to productage; labor shortage; resource cost; cost of technology etc.nkruptcy; unreliable supplier; Cost and quality not reliable/consistent; etc.tional teams; no transparency between partners/departments; etc.d employee; resistance to change; unavailability of labor due to absence; etc.ns of transport; inconsistent warehouse facility; IT failure; etc.uction schedules; machine breakdown; delay in delivery; change in design; etc.or long term); bullwhip effect; short product life cycle; risk from new entrants; etc.s; reduced demand; stock out; poor quality; wrong product delivery; etc.et share; low profit margins; decreasing revenues; etc.als in the warehouse/losses in transit, performance of the product, cyber attack etc.

Table A2Summary of ISM research works from 2000 to 2012.

Authors(s) Research objective Research findings

Gorane and Kant(2013)

To identify the supply chain management enablers (SCMEs) and es-tablish relationships among them using interpretive structuralmodeling (ISM) and find out driving and dependence power of en-ablers, using fuzzy MICMAC analysis.

This paper has identified 24 key SCMEs and developed an integrated modelusing ISM and the fuzzy MICMAC approach, which is helpful to identify andclassify the important SCMEs and reveal the direct and indirect effects ofeach SCME on the SCM implementation.

Pfohl et al. (2011) Structural analysis of potential supply chain risks using InterpretiveStructural Modeling (ISM) and MICMAC analysis methodology.

ISM was proved to be a effective methodology to establish inter-relation-ship among supply chain risks.

Tummala & Schoenherr(2011).

The purpose of this paper is to purpose a comprehensive and co-herent approach for managing risks in supply chains.

The Supply Chain risk Management Process (SCRMP) framework proposedhere is a coherent and comprehensive approach for managing risks anduncertainties associated with a given problem. Risk identification, mea-surement, assessment, evaluation, mitigation and control strategies havebeen discussed in detail. The SCRMP can be used as an aid in makingdecisions.

Olson and Wu (2011) To compare tools to aid supply chain organizations in measuring,evaluating and assessing various risks.

The work considers the strategies of outsourcing to China and other na-tions. It offers many cost advantages as low cost producers anywhere cancompete. There are greater risks with outsourcing but these can be handlesusing the ability to communicate in real time (via Internet). The use of DataEnvelopment Analysis and Monte Carlo Simulation for evaluation of risk onhypothetical data shows that vendors from the Great China are preferred tothose from western nations due to low risk-adjusted cost and higherefficiencies.

Jharkharia (2011) To understand mutual influences of the factors those adversely im-pact the process and results of ERP.

The Interpretative Structural Modeling (ISM) has been used to establish therelationship between the critical factors. Three factors namely, poor un-derstanding of business implications and requirements, poor data qualityand lack of top management support, have been identified as drivers forERP implementation and hence need serious attention.

Farooq and O’Brien(2010)

To present results of a developed technology selection frameworkand provide insights into the risk calculation and their implication inmanufacturing technology selection process.

The paper explains the role of risk and an approach to calculate risk in themanufacturing technology selection process. The research quantifies therisk involving different manufacturing technology selection alternatives.

Laeequddin et al.(2009)

To address the issue whether supply chain members should strive tobuild the trust or strive to reduce the risk with its members and fromwhich perspective?

A conceptual framework was developed considering five key perspectives:characteristics, economics, dynamic capabilities, technology, and institu-tions to evaluate the risk in a relationship. These perspective of risk caninitiate and build trust between supply chain members in the globalbusiness environment.

Pujawan and Geraldin(2009)

To provide a framework to proactively manage supply chain risks. The two house of Risk (HOQ) models have been adopted to supply chainrisk management. HOQ1 determines which risk agents in the five supplychain processes (SCOR) are to be given priority for prevention. HOQ2 givespriority to those actions considered effective but with reasonable moneyand resource commitments.

Manuj and Mentzer(2008)

To explore the phenomenon of risk and risk management strategiesin global supply chains.

Six risk management strategies have been suggested depending upon thenature of demand and supply uncertainty.(1) Postponement(2) Speculation(3) Hedging(4) Control/Share/Transfer(5) Security(6) Avoidance

The paper also provides insights into the role of three moderators in theprocess of supply chain risks namely(1) Team composition(2) Supply chain complexity(3) Inter-organizational learning

Khan and Burnes(2007)

To develop a research agenda for risk and supply chain management. The paper discusses the fact that the application of risk theory in supplychain management is still in its nascent stages and all the models for riskmeasurement need to be empirically tested.

Faisal et al. (2006) To present an approach to effective supply chain risk mitigation usingISM. To understand the dynamics between various enablers that helpto mitigate risk in the supply chain.

The model shows that there exists a group of enablers having a high drivingpower and low dependence power which requires maximum attention andis of strategic importance. Another group of variables consists of thosevariables which have high dependence and are the resultant actions.

Gaudenzi and Borghesi(2006)

To evaluate supply chain risks that stand in the way of supply chainobjective using Analytical Hierarchy Process (AHP).

The application of AHP is helpful particularly to support the prioritizationof objectives and the analysis of overall impact. The establishment ofthrough consideration of critical issues requires the involvement of man-agers from different areas.

Cucchiella and Gastaldi(2006)

To individualize a framework to manage uncertainty in the supplychain finalized to reduce the firm risk.

The risk management framework has been designed using the real optiontheory. After analyzing the risk characteristics it is possible to individualizethe real options that better suit the risk under consideration. The outsourceoption has been tested using Mat Lab to cover two risks related to pro-duction capacity and price fluctuation.

Kleindorfer and Saad(2005)

To develop a conceptual framework that reflects the joint activities ofrisk assessment and mitigation that are fundamental to disruptionrisk (natural disaster, strikes, economic disruptions, etc.) in supplychain.

The framework “SAM-SAC” consists of six activities(1) Specification of sources and vulnerabilities(2) Assessment(3) Mitigation(4) Strategies with dual dimension


Table A3 Questions for Delphi rounds

No. Questions Key words/issues

1 How do you rate the globalization is having perceived risk for Indian retail companies? Globalization2 How do you perceive the impact of product quality and standards affect the apparel retail business? Product standards3 Are you seeing the availability of raw material and their quality standards in India is a risk to the retail business? Raw material4 Absence of standard systems/operating procedures in the retail environment is a risk to the business – How do you

see?SOP systems

5 Do you include the non-availability of the skilled resources in the retail domain as a major risk in the Indianenvironment?

Resources availability

6 Availability of less merchandise options in different forms of retail gives some amount of risk to the business, in termsof making merchandise available to the people. How do you see as a risk to the business?

Less options in merchandise

7 Perception of small players that retail business is a domain to be handled only by the big corporate houses with hugeinvestments – Is that a threat to the development?

Big players threat

8 How do you rate the impact of reliability of the suppliers in the retail business? Reliability of suppliers8 Do you see the lack of alignment of the stakeholders with the retail firm is high? What kind of threats posed by? Misalignment in the business9 How do you see the behavior practices of the employees risk the retail business as they are directly in touch with the

customers?Employee behavior

10 Financial risk is always there with the retail business? From the Buying as well as the supplier's perspective? Financial Risk11 Retail business is impacted by a delay in the lead-time of the product? How does it impact the business Lead time handling12 Customer dissatisfaction will lead to a huge impact on the Indian retail business? Customer satisfaction13 Retail companies are not having the big support in terms of infrastructure. Is that a risk posed to the Industry, when

compared to the industry standards in abroad.Infrastructure risk

14 How do you rate the pilferages and shrinkages control system, is that a risk to the retail business? Retail systems15 Employee orientation methods are not being given on the retail business. Is it perceived to be a risk? Employee orientation16 Customization trend in the business is at the very low level at the Indian retail. Is that posing a threat to the business

growth?Customization

17 India does not have the logistics support to the retail industry in terms of warehousing and advanced systems such asVMI and all, Is that a threat to the business performance?

Logistics support

18 Does Indian retail follow the global trend and do not have the sense of developing the products for the domesticcustomers? Is that a risk to the business?

Alignment with international clothingtrends

19 Existing HR policies in the retail domain is a big threat? HR policies20 Fear of dominance of Foreign brands is also posing a risk to the retail business? Proposed FDI

Table A2 (continued )

Authors(s) Research objective Research findings

(5) Actions and(6) Necessary conditions

Sinha et al. (2004) To propose a methodology to mitigate supply chain risks. The model involves the process of identifying, assessing, planning andimplementing solution, conducting FMEA analysis, and doing continuousimprovement.

Chopra and Sodhi(2004)

To categorize various supply chain risks and suggest risk mitigationstrategies for these categories.

Nine categories of risks have been identified and the impact of eight mi-tigation strategies on these risks has been assessed. The following twomanagerial implications follows:(1) Stress testing your supply chain can create a shared organization wide

understanding of the supply chain risks.(2) Adapt risk mitigation approaches to the circumstances of a particular

company.

Zsidisin et al. (2004) To explore, analyze, and derive common themes on supply risk as-sessment techniques.

The paper provides the supply managers insights into the techniques theirfirms can adapt to assess supplier risks. the cases studied would helppurchasing organizations to assess supply risk with techniques such as(1) Addressing supplier quality issues(2) Improving supplier processes(3) Reducing the likelihood of supply disruptions(4) Promoting goal congruence between buying and selling firms and(5) Reducing outcome uncertainty associated with inbound supply

Harland et al. (2003) The purpose of the paper is to provide a review of definition andclassification of risks. It also provides a holistic view of risk assess-ment and measurement.

The research describes the development of a risk tool to increase visibilityof risk in the supply chain network. The tool was tested on four case studiesin the electronics sector. The case studies were conducted to design a fra-mework for the tool.

Zsidisin and Ellram(2003)

The main objective of this paper is to provide a grounded definitionof supply risk. Case studies from various purchasing organizationshave been considered for this purpose.

The paper provides academicians and practitioners a starting point tounderstand the supply risks and to provide insights to how these risks cannegatively impact the business environment.


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