Analysis on supply chain risks in Indian apparel retail chains and proposal of risk prioritization model using Interpretive structural modeling

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

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Analysis on supply chain risks in Indian apparel retail chains and proposal of risk prioritization model using Interpretive structural modeling 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, India b Symbiosis Institute of Operations Management, Symbiosis International University, Nashik Campus, Plot No. A-23, Shravan Sector, New Cidco, Nasik 422008, Maharashtra, India

art ic l e i nf o

a b s t r a c t

Article history: Received 8 December 2014 Received in revised form 6 June 2015 Accepted 7 June 2015

Indian apparel retail industry is on a complete transformation journey and trying to evolve as an organized industry. It is very common to find the disruption factors in every business and the ways to mitigate and manage them is of current research interest. The paper discusses the selective risks associated with the apparel retail supply chains in India by structural analysis of the controllable risks that are identified. The work also reveals the use of Interpretative Structural Modeling (ISM) to establish the interdependencies 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 ISM modeling technique and Fuzzy MICMAC analysis. It also classifies the risk factors based on their driving and dependence power. ISM is proved to be a useful tool to help understand the impact of risks at stages of retail supply chain. Globalization, labor issues and security and safety of resources turns out to be the strong drivers of other supply chain uncertainties. The domino effect of these risks leads to financial crises for the organization. The paper also proposes a new model for the Risk Priority Number (RPN) calculation using ISM and Fuzzy MICMAC methodology for the applications in retail and various other domain risk studies. The sample size of experts is small and to remove the biasness of opinion, the model can be further validated using Structural Equation Modeling (SEM) in the future. The outcome would help practicing managers to analyze and to take actions for managing the factors by improving the bottom line of the organization by proper utilization of resources. & 2015 Elsevier Ltd. All rights reserved.

Keywords: ISM Fuzzy MICMAC Retail risks Supply chain risk Prioritization Risk assessment Risk Priority Number

1. Introduction In the last two decades, supply chains of businesses have been experiencing rapid globalization and emerging technological changes especially in the manufacturing and retail business. Today, supply chains across industries are being stretched the way it was never done before. The most trusted brands do only the assembling of components which are outsourced for manufacturing. Similarly, major apparel retailers do their business as well. They do product development and outsource rest of their operations. This has made supply chains more complex, fragile and prone to many disruptions. It is an established fact that recent commercial n

Corresponding author. E-mail addresses: [email protected] (V.G. Venkatesh), [email protected] (S. Rathi), [email protected] (S. Patwa). http://dx.doi.org/10.1016/j.jretconser.2015.06.001 0969-6989/& 2015 Elsevier Ltd. All rights reserved.

chains are dynamic networks of interconnected firms and industries (Hakansson and Snehorta, 2006). And, the search for better markets and cheaper sources of raw materials have made the supply chains more and more complex and retailers need to sustain their business (Sahin and Robinson, 2002; Wu and Olson, 2008; Ganesan et al., 2009). Many disruptions and risk factors have threatened production and retail distribution systems. They directed a decline in the market share, cost escalation and dissatisfaction amongst customers. In the last decade, supply chain risks are studied diligently and are categorized into inherent or high frequent risks and disruption or infrequent risks (Kleindorfer and Saad, 2005; Oke and Gopalakrishnan, 2009). These disruptions could also be due to political, labor, market uncertainty, material, financial and information risk impacting supply chain performance (Shapira, 1995; Prater et al., 2001; Christopher and Lee, 2004; Quinn, 2006; Tang, 2006a, 2006b; Poirier et al., 2007; Tang and Nurmaya Musa, 2011). How does one protect the business

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from disruption? The answer lies in the integration of supply chain risk management as a core component in the operations of the business. This intuited the studies on supply chain risks and mitigating strategies, which are increasingly becoming popular (Wei and Choi, 2010), eventually lead to the studies from the domain specific risk mitigating strategies as well. India, being a growing destination for the retail business, the risk is to be analyzed from supply chain perspective, though the sector is highly fragmented. Boston Consulting Group reports that the organized retail industry will achieve $260 billion business by 2020 (BCG Report, 2011). In the last decade, Indian retail market has shown the considerable growth in the Apparel business and so as food business. With many foreign apparel players eyeing to enter India through FDI, it has become a research destination. To support that, though there are reports existing in this domain, the focus on retail supply chain still attracts several problems to be explored in the supply chain and its risk domain. This paper will explore and analyze selective disruption factors in the domain of study. The study also proposes a methodology to prioritize risks by analyzing the interdependencies between them. This contextual relationship is established through a technique called Interpretive Structural Modeling (ISM) and followed by a Matriced’ Impacts Cruoses Multiplication Applique a un Classement (MICMAC) analysis for segregation of study variables. Thus, our proposed model is based on a notion that each risk is associated with multiple ones in a way that either it drives them or is dependent on them. To design the mitigation strategies, the first step is to identify and analyze the risk in terms of its frequency of occurrence, severity in terms of cost and what other disruptions it could lead to. The focus is to propose a methodology based on MICMAC analysis to analyze and prioritize the supply chain risks so that appropriate strategies can be designed to improve the business efficiency. For prioritizing the risks, there is a new formula proposed based on the structural model, which is the unique contribution of the model. The paper has been structured as follows: It starts with the introduction about the supply chain risk management, followed by the literature review on supply chain risk and Indian retail industry. Then, the discussions on establishing the variables, ISM model formulation and MICMAC analysis. It ends with the discussions on the new risk assessment framework, managerial implications and future scope.

2. Literature review The literature review has been done through systematic literature 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 protocol in a systematic way. The review includes papers from various journals like Business Process Management, International Journal of Physical Distribution and Logistics Management, Journal of Operations Management, Supply Chain Management: An International Journal, The International Journal of Logistics Management, Journal of Manufacturing Technology Management, International Journal of Operations and Production Management and etc. It also includes articles and Reports from Harvard Business Review and reports on Supply chain risk management published by various prominent consulting companies like Deloitte, PwC, Accenture, Technopak, etc. The second part covers the review on identifying the risks variables and understanding the risk mitigating strategies from 2000 to 2014. Supply chain risk is defined as “any risk to material, product and information flow from original supplier to the delivery of the final 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 and Bode, 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 system. Further, it is being identified as a function of uncertainty level and the impact of an event (Sinha et al., 2004). However, it is the common belief that management within SC gathered more focus and momentum only after the 9/11 attacks in USA (Ghadge et al., 2012). This risk can be an internal element to the supply chain or due to external factors (Goh et al., 2007). They can also be classified as operations and disruptions risk (Tang, 2006a). The former are associated with uncertainties inherent in a SC which include demand, supply, and cost uncertainties. Disruption risks, on the other hand, are those caused by major natural and man-made disasters such as flood, earthquake, tsunami, and major economic crisis. Supply chains are vulnerable to disruptions due to a number of variables. These disruptions or risks can also have significant impact on profit margins of the businesses and such failure occurs due to one element which has an impact on both upstream and downstream operations (Chopra and Meindl, 2001). It is not only the profitability, but also the reputation of firm at stake. With the customers' expectations becoming more, and managing lead times of products is becoming very challenging, unless due attention is given to risk assessment exercise, the probability of supply chain failure is high (Khan et al., 2008, 2012). Not only has this, but the cost of break cascaded across the businesses also and it may impact 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 effectively. Several papers discuss on risk identification and assessment methodologies (Chopra and Sodhi, 2004; Lavastre et al., 2012). Various models for supply chain risk management have been 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 and Martel, 2012; Chiu and Choi, 2013; Li and Womer, 2012). Table A2 gives summary of various modeling studies conducted in supply chain management field so far. Some of these models help businesses to identify their risks and give a direction for continuity plan by evolving the mitigating strategies as well (Juttner, 2005). The means of managing the risks is very unique to individual business. To support that, Juttner et al. (2002, 2003) suggest investigating risk management in different supply chains and developing strategies based on their environments. Risk assessment

Table 1 Variables (risks) for ISM. Risk no.

Risk

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

Globalization Raw material and product quality standards Scarcity of resources Supplier uncertainty Lack of co-ordination/alignment Behavioral aspect of employees Infrastructure risks Delay in schedule/lead time Demand uncertainty Customer dissatisfaction Financial risk Security and safety

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process is the most imperative step in risk management domain and it starts from product development stage (Ghadge et al., 2013). Further, phases of managing these risks can vary from identification/analysis or estimation through risk assessment to various ways of managing risks (Norrman and Jansson, 2004). Nevertheless, very few papers show orientation on the specific domain concentration such as food business (Vorst et al., 1998; Diabat et al., 2012; Wang et al., 2012), manufacturing (Farooq and O’Brien 2010), electronics industry (Sodhi and Lee, 2007), toy industry (Johnson, 2001), automotive and electronics domain (Craighead et al., 2007; Blos et al., 2009; Wagner and Bode, 2009), aerospace industry (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, decision makers, risk management responses with performance outcomes and influencers for risk management decisions. It is established by Hallikas et al. (2002, 2004) and Kern et al. (2012) that structured research on risk management domain is to be established with possibilities of identifying, controlling and monitoring the risk factors. Moreover, the organizations are not aware about the vulnerability of their supply chain threats, irrespective of their domain of operations and though there are more variations exists in the form of wrong usages and misconceptions, it is due to other factors such as the absence of common specification, perception and difference in needs (Mullai et al., 2008; Wieland and Wallenburg, 2012). Christopher et al. (2011) argue for a good scope in studying from sourcing and design point of view. There are some research reports from clothing industry evaluate risk from the sourcing perspective (Masson et al., 2007; Kam et al., 2011; Vedel and Ellegaard, 2013). Lendaris (1980) has defined a way to model the risks and an integrated structural model has been proposed by Hachicha and Elmsalmi (2014) using ISM approach. Our paper has endorsed that methodology and tried to give a discrete approach towards the risk factor modeling and calculations for the Indian apparel industry, which is one of the complex networks. From the above review, it can be established that analysis of the domain specific 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 followed by apparel domain in India. 2.1. Indian apparel retail industry Indian retail industry is the second largest employer after agriculture (around 8 percent of the population) and it has the highest number of outlets in the world. Despite that advantage, the industry is at the nascent stage (Garg, 2010). According to the market research report study (2013), the retail market in India grew at a CAGR of 12.47 percent during the period 2007–2012 and will grow at a rate of 13.23 percent from 2012 to 2017. Increasing urban demographics, rapid development of shopping malls, raising brand-conscious customers, and strong influence from the Western world are changing catalysts of Indian retail industry (Halepete and Iyer, 2008). The paper also argues that low level of organized retail penetration, coupled with an ineffective supply chain, characterizes the infrastructure of the Indian retail industry. Moreover, retail industry in India is becoming so adaptive and anticipative, as they flexible enough to meet the demand of changing customer markets in (Ramesh et al., 2008). Further, Dabas et al. (2012), elucidate that Indian retail industry is dominated by multi faceted tax systems and very poor infrastructure. In the current scenario, studies on the current state of Indian retail along with strategies for growth would have immense significance for

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international retailers vying to enter the Indian market (Batra and Niehm, 2009). Also, Indian retail environment is dominated by the apparel retailing through organized and unorganized formats of retailing. Though the records for later does not exist, there are few reports support the data for organized one (Technopak Report, 2014). Early studies in Indian retail industry such as Sahay and Mohan (2003) confirm that almost one-third of the Indian companies had no supply chain strategy. But the influence the western firms on the supply chains, supply chain is clearly a visible element across the business (Anbanandam et al., 2011). It varies from product to product. Apparel business in retail has a share of US$41 billion, which is poised to grow around US$64 billion by 2018 (Technopak Report, 2014). Currently, online retail is also booming up in India. It constitutes merely 5–7 percent of the apparel market in India, but it is expected to grow at a CAGR of more than 35 percent in the next 10 years (Market Research Report, 2012). Further, with FDI decision pending in policy environment, no doubt that retail supply chain gets focus in the research, as many players are very keen entering India 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 to the individualistic in nature, many of the strategies to be adopted in Indian market should be niche and unique to manage regional situations. Unless the companies assess the risks to be managed, more damages can be predicted in the supply chains, and it is becoming challenging in the clothing trade as product life cycles are getting shortened (Khan et al., 2008). There are positive signs that Indian apparel retailers are considering their supply chain management operations as a strategic tool in their overall business strategy like their western counterparts, instead of viewing it as an operational one. The studies on risk management in clothing supply chain are also at the beginning stage. Moreover, organized retail business in India is dominated by the apparel domain (Technopak Report, 2014). Further, Indian apparel retail chains are moving through the professional approach in their operations and because of that uncertainties are implicitly built in. It is very much helpful to study these risks in detail pertaining to the specific domain, as some of the supply chain risk factors are highly influential in the Indian scenario. Thus, the literature review also gives the scope for new direction for research on risk management focus in the Indian apparel retail segment. It has been clearly established in the review that risk studies exist in the other domains and current one in this paper is pioneer for the Indian retail segment and possible extension of the established framework can also be proposed in addition to that. The next phase of the article gives overall research methodology and ISM model building with the proposed risk prioritization framework followed by the managerial explanations.

3. Research methodology The main purpose of this paper is to develop contextual relationships to analyze the costs associated with risks and prioritize them. The occurrence of one risk gives rise to multiple risks resulting into a domino effect which makes it very important for the managers to control these risks before they occur. A group of practitioners have been identified to develop the ISM model to show the relationships between various risks involved in the supply chain. The results of ISM are further extended using the fuzzy MICMAC analysis to identify the driving and dependence power of each of these variables. A risk calculation method has been proposed further using the driving and dependence power to priorities these risk to help managers decide on the most critical risk to mitigate.

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3.1. ISM – why and how? Interpretive structural modeling (ISM) is a process that transforms unclear and poorly articulated mental models of systems into visible, well-defined models useful for many purposes (Sushil, 2012). It is a structural relationship diagram which makes it easy to visualize the inter relationship between various elements. In other words, it helps in presenting a complex system in a simplified format. It enables to make a mind map of elements which depend on one another to form a complex relationship. The method has some limitations and it has been subsequently discussed. The model development is described in step by step approach in the next section. ISM facilitates the identification of the structure within a system. Following are the steps involved in the ISM methodology (Sage, 1977; Jharkharia and Shankar, 2004; Faisal et al., 2006; Sushil, 2012): (1) Identification of variables: The key variables of the system are identified using literature study and brain storming sessions with the industry experts and academicians. (2) Contextual relationship: A contextual relationship is identified among each variable (identified in step 1) with respect to which the pairs of variables would be examined. The contextual relationship is in the form of a matrix called the structural self-interaction matrix (SSIM). Notations used to develop the SSIM: V: Risk variable i leads to variable j A: Risk variable j leads to variable i X: Risk variable i leads to variable j and vice versa O: No relationship between the variables (3) Initial Reachability Matrix: The SSIM is then converted into a binary matrix, called initial reachability matrix by substituting V, 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 in the reachability matrix becomes 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 in the reachability matrix is 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 in the reachability matrix becomes 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 in the reachability matrix becomes 0 and the (j, i) entry also becomes 0. (4) Transitivity check: The reachability matrix is developed from the SSIM and the matrix is checked for transitivity. The transitivity of the contextual relation is a basic assumption made in ISM. It states that if variable A is related to B and B is related to C, then A is necessarily related to C. (5) Levels: The transitivity matrix obtained in step (4) is converted into the canonical matrix format by arranging the elements according to their levels. (6) Building the ISM model: Variables in each level are then connected based on their relationships as defined in the structural self-interaction matrix.

3.2. Identification of the variables 3.2.1. Delphi methodology Delphi 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 by involving practicing professionals to conclude opinion on the subject to be researched. It has been used in the different areas

such as for strategic decision making, policy formulation and to draw the conclusions based on convergence in the multifaceted complex ideas (Czinkota and Ronkainen, 2005; Grisham, 2009). Although the survey methodology could be performed in the current study, Delphi technique has been used as a tool to initiate research direction in the selected domain. It is because, the structured study on Indian apparel retail supply chain is currently at a nascent stage. The technique aims to gather study and finalize the facts on various barriers from in-depth query of experts and stakeholders with the practical context. The method has been executed at steps; (1) Constitution of members for the expert panel; (2) identification of the barriers and formulation of the feedback system; (3) execution in the two rounds. In the first step, 20 supply chain executives/managers having diversified backgrounds participating in the business decisions at the senior level are chosen. This selection process was done through a structured approach proposed by Okoli and Pawlowski (2004) and also local popularity in Indian apparel business environment and their willingness to participate in this research study. Out of 20 participants, 14 people have participated in the two round Delphi process. In the second step, the possible list of factors of risks was prepared. We have finalized 12 factors based on the ranking as well as the concurrence from the participants (from the practice point of view) and well supported by literature as well. The next section describes the factors for the present study and their literature support. A supply chain is susceptible to many types of risk. We identified and categorized risks into 12 distinct types that can be controlled and mitigated if proper steps are taken. With increase in globalization, complexity and dynamism of supply chains are leading to greater exposure to risk from political and economic events (Ghoshal, 1987; Harland et al., 2003; Manuj and Mentzer, 2008; Holweg et al., 2011). Globalization increases the number of cross country transaction in shipping goods from one place to other with Customs and regulation risk (Manuj and Mentzer, 2008), Security and International Terrorism (Sheffi, 2001; Williams et al., 2008) change in cost of resource acquisition due to frequent fluctuation in currency rates. Next category of risk to an organization is the awareness of Raw material and Product quality requirements of the products. Supply chain can be at risk due to regulatory compliance, quality requirements and product environments (Cucchiella and Gastaldi, 2006; Tse et al., 2011; Li and Womer, 2012). At present, section of customers are becoming aware about the product quality and its raw material contents such as residuals of hazardous chemicals in the fabric processing especially (such as dyeing and printing) and other unfriendly contents in the various accessories (such as lead content in the shank buttons). For example, Increase in carbon foot print and pollution by the organization impacts society and this creates negative perception among the common masses about the company's image (Bickerstaff, 2004). Risk of scarcity of resources is a major concern for an organization. A supply chain is dependent on the resource for its functioning (Newman et al., 1993; Jones et al., 2000; Carter and Rogers, 2008). Unavailability of skilled man power and the right technology to perform the task can prevent an organization from functioning better. Information scarcity is a key facet of uncertainty in terms of the existence (Baird and Thomas, 1990). Scarcity in availability of cotton can put the apparel industry at risk by loss of customer due to increase the price of the finish goods. Also, it will force the organization to search alternate resource or supplier. Supplier uncertainty to deliver goods at the right time is the next category of risk. Suppliers' uncertainty to respond to changes in demand leads to the decline in the market share. Stakeholders' bankruptcy or mishap increases the uncertainty of supplier in fulfilling the demand (Krause and Handfield, 1999; Chopra and Sodhi, 2004; Cousins et al., 2004;

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Nembhard et al., 2005; Simangunsong et al., 2012; Vedel and Ellegaard, 2013). Lack of Alignment/co-ordination among the players of supply chain is categorized as a risk in the supply chain. Misalignment, resulting from lack of transparency among the players or lack of communication co-ordination or interaction leads to supply chain breakdown (Cucchiella and Gastaldi, 2006; Shen et al., 2013). Information sharing among the members of the supply 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. The occurrence of frequent labor turnover affect both financial and reputation of the organization. Resistance to change and misuse of organization's assets and employee disputes are common behaviors observed in most industries and these behaviors can act as a bottleneck to an organization. Next category of risk is due to improper support services and can be categorized as Infrastructure risk. It affects the operational activity and can cause the supply chain to standstill. Lack of sufficient equipment, transportation breakdown, warehouse or IT Breakdown (Pfohl et al., 2011) can prevent the supply chain from function smoothly. Also, Delay in schedule/lead time, next factor of risk, can prevent a supply chain in putting the product into the market at the right time. The risk of delay in production (Pfohl et al., 2011) or information or execution can affect the supply chain severely. Lead time in case of innovative products like fashion apparel should be as low as possible, slight delay can increase the risk of failure for an organization (Cucchiella and Gastaldi, 2006; Pujawan and Geraldin, 2009) Delay in return process impacts the reverse supply chain of an organization. Demand uncertainty due to frequent fluctuation in consumer demand or inaccurate forecasting can be a cause of bullwhip effect in the Supply chain. Risk due to demand uncertainty can impact its reputation and even take it out of business (Christopher and Lee, 2004; Cucchiella and Gastaldi, 2006; Manuj and Mentzer, 2008; Simangunsong et al., 2012). Satisfying customer's need is one of the goals of a supply chain and customer dissatisfaction can be a major risk to an organization. Frequent stockout, poor quality of product causes customer dissatisfaction leading to customer complaints and product return. Delay in return process (Pujawan and Geraldin, 2009), non-availability of product (Meulbrook, 2000) or less degree of customer interaction (Mitchell, 1995) increases the risk due to customer dissatisfaction. In order to keep customer happy, the assets and resources of the organization must be protected from the misuse, mishaps and theft (Pfohl et al., 2011). The risk due to security and safety can be fatal to an organization. Security risk relates to adverse events that threaten human resources, operations integrity, and information systems; and lead to outcomes such as freight breaches, data stealing, vandalism, crime, and sabotage (Manuj and Mentzer, 2008). Not only asset protection, keeping the employee safe and secure should be a concern of the organization. Security needs of both core supply chain entity and outsourcing organizations are creators of risk (Guinipero and Eltantawy, 2004). Further, Khan and Creazza (2009) advocate the new dimension of the risks with the product design and supply chain interface which is controlled by prominently by the irregularities in product quality supply as well. The last category of risk is the financial risk. One of the objectives of the organization is to make profit. To stay profitable, the cash flow should be managed properly. Managing financials for an organization is the biggest challenge and risk of financial mismanagement can lead to downfall of the organization. In many business sectors, an industry or an organization delivers the goods or service to its customer on credit. Debtors default (Meulbrook, 2000) affects the cash flow severely thereby increasing the financial risk of the organization. Mitigating financial risk leads to smooth flow of cash and keep organizations profitable (Kleindorfer and Saad, 2001; Hendricks and Singhal, 2005; Arcelus et al., 2012).

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3.3. Interpretive structural modeling (ISM) The technique, ISM, proposed by Warfield (1974) is qualitative in its approach. Many researchers used this methodology to direct order and decompose the complexity of relationships among elements (Sage, 1977; Mandal and Deshmukh, 1994; Ravi and Shankar, 2005; Sahney et al., 2006; Faisal et al., 2006; Faisal et al., 2007). The model uses judgment from group members and establishes the connection amongst the elements (Mandal and Deshmukh, 1994; Gorane and Kant, 2013). There are other applications of ISM in other areas as well. Some representative applications are: world-class manufacturing (Haleem et al., 2012), decision making (Lee and Rhee, 2011), Value chain management (Mohammed et al., 2008), Product design (Lin et al., 2006), Waste management (Sharma and Sushil, 1995), Vendor selection (Mandal and Deshmukh, 1994), Supply chain management (Agarwal et al., 2007), and so on. It is used to explore contextual relationship where they are mutually related. Following are the representative works of ISM model including Analysis on agile factors for the product launch (Chang et al., 2013), Barriers of Eco-friendly manufacturing adoption (Mittal and Sangwan, 2011, 2013), Analyzing the barriers for Six Sigma program implementation (Soti et al., 2011), Analyzing the barriers for energy saving in China (Wang et al., 2008), Critical factors of ERP implementation (Jharkharia and Shankar, 2004). The model is built using the industry experts' practical experience and the knowledge base of academicians to decompose a complicated system into several sub-systems and construct a multilevel structural model. The variables of the structural selfinteraction matrix in this paper are the types of risks involved in the supply chain of an organization. The risks are identified from literature review and expert interactions. More than 100 different types of risks were identified which can have an impact on the business. We include only the most common and general type of risks which generally occur in almost every industry. The developed model would be a generic one and can be modified as per the specific objectives of the company. Two academicians and six industry experts were consulted to develop the SSIM. Table 1 shows the various risks considered to develop the ISM. The structural self-relationship matrix and initial reachability matrix are developed as per the steps and rules discussed in ISM methodology section. The SSIM, initial reachability matrix and transitivity matrix for our model are as shown in Tables 2, 3, and 4 respectively. Through the Structural Self Relationship Matrix, we can have following relationships established. The risk factor globalization leads to various other risks such as supplier uncertainty, increase in the product quality standards (R2), and infrastructure risks (R7) such as the organized retailing including the technology up

Table 2 Structural self relationship matrix.

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

R12

R11

R10

R9

R8

R7

R6

R5

R4

R3

R2

V O O O X V A A O A A –

V O V V V V V V V V – –

O V O O O O V V O – – –

O O O O O O O A – – – –

V O V V V V X – – – – –

V V O O V V – – – – – –

O O O O A – – – – – – –

O O V A – – – – – – – –

V O V – – – – – – – – –

A A – – – – – – – – – –

V – – – – – – – – – – –

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Table 3 Initial reachability matrix.

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

Table 5 Level 1 of risk variables.

R1

R2

R3

R4

R5

R6

R7

R8

R9

R10

R11

R12

Variables

Reachability set

Antecedent set

Intersection

1 0 1 0 0 0 0 0 0 0 0 0

1 1 1 0 0 0 0 0 0 0 0 0

0 0 1 0 0 0 0 0 0 0 0 0

1 0 1 1 1 0 0 0 0 0 0 0

0 0 1 0 1 1 0 0 0 0 0 1

0 0 0 0 0 1 0 0 0 0 0 0

1 1 0 0 1 1 1 0 0 0 0 1

1 0 1 1 1 1 1 1 1 0 0 1

0 0 0 0 0 0 0 0 1 0 0 0

0 1 0 0 0 0 1 1 0 1 0 0

1 0 1 1 1 1 1 1 1 1 1 1

1 0 0 0 1 1 0 0 0 0 0 1

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

1,2,4,5,7,8,10,11,12 2,7,8,10,11 1,2,3,4,5,7,8,10,11,12 4,7,8,10,11 4,5,7,8,10,11,12 5,6,7,8,10,11,12 7,8,10,11 7,8,10,11 7,8,9,10,11 10,11 11, 4,5,7,8,10,11,12

1,3 1,2,3 3, 1,3,4,5,12 1,3,5,6,12 6 1,2,3,4,5,6,7,8,9,12 1,2,3,4,5,6,7,8,9,12 9 1,2,3,4,5,6,7,8,9,10,12 1,2,3,4,5,6,7,8,9,10,11,12 1,3,5,6,12

1 2 3 4 5,12 6 7,8 7,8 9 10 11 5,12

Level

I

Table 6 Level 2 of risk variables. Table 4 Transitivity matrix.

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

R1

R2

R3

R4

R5

R6

R7

R8

R9

R10

R11

R12

1 0 1 0 0 0 0 0 0 0 0 0

1 1 1 0 0 0 0 0 0 0 0 0

0 0 1 0 0 0 0 0 0 0 0 0

1 0 1 1 1 0 0 0 0 0 0 1n

1n 0 1 0 1 1 0 0 0 0 0 1

0 0 0 0 0 1 0 0 0 0 0 0

1 1 1n 1n 1 1 1 1 1n 0 0 1

1n 1n 1 1 1 1 1 1 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

1n 1 1n 1n 1n 1n 1 1 1n 1 0 1

1 1n 1 1 1 1 1 1 1 1 1 1

1 0 1n 0 1 1 0 0 0 0 0 1

gradation in the supply chain system. Again, this must lead to the financial risks in the form finding the new investments to cope up the pressure. Further, globalization (R1) leads to the increase in the risks of security and safety of the cargo (R12), as the material pass through various layers in the supply chains. But the scarcity of resources (R3) triggered the material to be sourced through the globalized partner and increased the standard of the material, which is also a potential risk for the supply chains which work on the strict lead time basis. Further, uncertainty in suppliers and lack of alignment amongst are the potential risks which lead to scarcity of resources as well (R3). An organization facing the supply disturbance risk due to non alignment may delay the shipments and gives financial risk as well. Lack of coordination (R5) may lead to the organization to look for additional technology (like tracking), space requirement (warehousing) which are potential infrastructure risks in the apparel supply chains. Behavior aspects (R6) such as unexplained absence in the retail selling floor and distribution environments may lead to develop new systems in the infrastructure such as monitoring the movements of staff and products within supply chain environments and they also delay the delivery of the order which is very prominent in the Indian retail business. People do see no prominent relationship between the infrastructure and delay in the shipments (R8) as it would lead to the customer dissatisfaction indirectly and eventually lead to the financial risks by locking the investments in the retail chain. It has been observed that layers of security and safety (R12) to the products in the supply chains impact the delay of the delivery in the system (R8). However, there is no clear linkage established between demand uncertainty (R9) and security and safety of the material (R12) and also customer dissatisfaction as it has been prominently indicating the fulfillment of the orders and experiencing the products (R10). Customer dissatisfaction risk and

Variables

Reachability set

Antecedent set

Intersection

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R12

1,2,4,5,7,8,10,12 2,7,8,10 1,2,3,4,5,7,8,10,12 4,7,8,10 4,5,7,8,10,12 5,6,7,8,10,12 7,8,10 7,8,10 7,8,9,10 10, 4,5,7,8,10,12

1,3 1,2,3 3 1,3,4,5,12 1,3,5,6,12 6 1,2,3,4,5,6,7,8.9.12 1,2,3,4,5,6,7,8,9,12 9 1,2,3,4,5,6,7,8,9,10,12 1,3,5,6,12

1 2 3 4 5,12 6 7,8 7,8 9 10 5,12

Level

II

Table 7 Level 3 of risk variables. Variables

Reachability set

Antecedent set

Intersection

R1 R2 R3 R4 R5 R6 R7 R8 R9 R12

1,2,4,5,7,8,12 2,7,8 1,2,3,4,5,7,8,12 4,8,7 4,5,7,8,12 5,6,7,8,12 7,8 7,8 8,9,7 4,5,7,8,12

1,3 1,2,3 3 1,3,4,5,12 1,3,5,6,12 6 1,2,3,4,5,6,7,8,9,12 1,2,3,4,5,6,7,8,9,12 9 1,3,5,6,12

1 2 3 4 5,12 6 7,8 7,8 9 5,12

Level

III III

Table 8 Level 4 of risk variables. Variables

Reachability set

Antecedent set

Intersection

R1 R2 R3 R4 R5 R6 R9 R12

1,2,4,5,12 2 1,2,3,4,5,12 4 4,5,12 5,6,12 9 4,5,12

1,3 1,2,3 3 1,3,4,5,12 1,3,5,6,12 6 9 1,3,5,6,12

1 2 3 4, 5,12 6 9 5,12

Level

IV IV

IV

Table 9 Level 5 of risk variables. Variables

Reachability set

Antecedent set

Intersection

Level

R1 R3 R5 R6 R12

1,5,12 1,3,5,12 5,12 5,6,12 5,12

1,3 3 1,3,5,6,12 6 1,3,5,6,12

1 3 5,12 6 5,12

V V

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Table 10 Level 6 of risk variables.

159

Table 12 Fuzzy direct relationship matrix.

Variables

Reachability set

Antecedent set

Intersection

Level

R1 R3 R6

1 3 6

1,3 3 6

1 3 6

VI VI VI

Financial Risk (R11)

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

R1

R2

R3

R4

R5

R6

R7

R8

R9

R10

R11

R12

0 0 0.5 0 0 0 0 0 0 0 0 0

0.7 0 0.1 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0

0.5 0 0.7 0 0.5 0 0 0 0 0 0 0

0 0 0.3 0 0 0.3 0 0 0 0 0 0.3

0 0 0 0 0 0 0 0 0 0 0 0

0.3 0.5 0 0 0.3 0.7 0 0.3 0 0 0 0.5

0.5 0 0.7 0.7 0.5 0.7 0.7 0 0.5 0 0 0.3

0 0 0 0 0 0 0 0 0 0 0 0

0 0.5 0 0 0 0 0.5 0.7 0 0 0 0.3

0.5 0 0.7 0.7 0.3 0.5 0.5 0.5 0.5 0.7 0 0.7

0.3 0 0 0 0.3 0.7 0 0 0 0 0 0

Customer Dissatisfaction (R10)

Delay in schedule / lead time (R8)

Demand Uncertainty (R9)

Infrastructure Risk (R7)

Supplier Uncertainty (R4)

Scarcity of Resources (R3)

Globalization (R1)

Product quality and raw materials standards (R2)

Lack of coordination/Alignment(R5)

Behavioral aspect of Employees (R6)

Security and Safety (R12)

Fig. 1. Interpretive Structural Model for risk relationship in apparel retail companies.

financial risks (the fear of losing the amount in the supply chains due to theft) together may lead to the safety and security of the goods in the supply chains. The next step is to create the reachability matrix as per the procedure. The next step is to convert the transitivity matrix to the canonical matrix format by arranging the elements according to their 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 an arrow which points from a higher level variable to a lower level variable. It implies that the higher level variable leads to the lower level variables. Lower level variables are at a higher level in the ISM hierarchy and are driven by the higher level variables. The ISM model for the interrelationships between the risks is shown in Fig. 1. The ISM uses SSIM to define the relationship among risks. The Initial Reachability Matrix is a binary matrix with 0 and 1. A ‘1’ denotes a relationship between the two risks and a ‘0’ denotes no relationship. It implies that we have considered only extreme

Table 11 Fuzzy 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 with the strength of relation between the any two variables, we need to consider the gray area between 0 and 1. We therefore analyze the risk for their driving and dependence power using fuzzy MICMAC analysis. The fuzzy direct relationship matrix is formed using expert 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 relationship. Other times the variables may or may not be strongly related. Sometimes a risk may lead to the other and sometimes it may 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 corresponding risk variables and sum of all the column elements gives the dependence power of corresponding risk variables. The fuzzy direct relationship matrix is recursively multiplied by the binary direct reachability matrix until a Fuzzy MICMAC stabilized matrix is obtained. A stabilized matrix is one for which the driving and dependence power is constant for at least last two iterations. The binary direct reachability matrix is obtained by replacing right diagonal elements in the initial reachability matrix by 0. In this particular model, the two matrices are given in Tables 13 and 14 along with the driving and dependence powers of risk variables in stabilized form. The variables are further divided into autonomous and 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. The first quarter consists of 5 (R3, R6, R1, R12 and R5) risks as Independent variables or driving ones. The ISM endorses the increase in globalization (R1) the complexities of the apparel supply chain would lead to various other risks in retail business. It is expected to play a key role as Indian customers used to see the only two seasons normally in the fashion life cycle such as either Spring–Summer (SS) or Autumn–Winter (AW). Due to the awareness of customers, retail companies are in turnaround phase to introduce more number of seasons to reduce the lifecycle of their merchandise. Moreover, the transfer of global designs to Indian retail environments is also a perceived risk as Indian customers are having varied demands in the market. Safety and security (R12) in the supply chains from Indian context is always a concern for supply chain managers as Indian retail chains are prone to pilferages and shrinkages across various steps in the order execution. It is appropriate by having it as a high driving power along with behavioral risk from the labor, as both of them will form the

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Table 13 Binary reachability matrix.

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

R1

R2

R3

R4

R5

R6

R7

R8

R9

R10

R11

R12

0 1 0 0 1 0 1 0 1 0 1 1

1 0 0 0 0 0 0 0 0 1 0 0

1 1 0 0 1 0 0 1 0 0 1 0

1 0 1 0 0 0 0 1 0 0 1 0

0 0 0 1 0 0 1 1 0 0 1 1

0 0 0 0 1 0 1 1 0 0 1 1

0 0 0 0 0 0 0 1 0 1 1 1

0 0 0 0 0 0 0 0 0 1 1 0

0 0 0 0 0 0 0 1 0 0 1 0

0 0 0 0 0 0 0 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 1 0

Table 14 Fuzzy MICMAC stabilized matrix. Variable

R1

R2

R3

R4

R5

R6

R7

R8

R9

R10

R11

R12

Driving power

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

0 0 0.5 0 0 0 0 0 0 0 0 0 0.5

0.7 0 0.5 0 0 0 0 0 0 0 0 0 1.2

0 0 0 0 0 0 0 0 0 0 0 0 0

0.5 0 0.7 0 0.5 0.7 0 0 0 0 0 0.3 2.7

0.3 0 0.5 0 0.3 0.7 0 0 0 0 0 0.3 2.1

0 0 0 0 0 0 0 0 0 0 0 0 0

0.7 0.5 0.7 0.7 0.5 0.7 0.7 0.3 0.5 0 0 0.5 5.8

0.7 0.5 0.7 0.7 0.5 0.7 0.7 0.3 0.5 0 0 0.5 5.8

0 0 0 0 0 0 0 0 0 0 0 0 0

0.7 0.5 0.7 0.7 0.5 0.7 0.7 0.7 0.5 0 0 0.5 6.2

0.7 0.5 0.7 0.7 0.5 0.7 0.7 0.7 0.5 0.7 0 0.7 7.1

0.3 0 0.5 0 0.3 0.7 0 0 0 0 0 0.3 2.1

4.6 2 5.5 2.8 3.1 4.9 2.8 2 2 0.7 0 3.1

risks at the bottom level. The model also positions the scarcity of resources in the driving quadrant as Indian retail industry still suffers with trained manpower and technology adoption due to the unavailability. It may be due to untrained manpower to understand the complexity of the business. The study also shows that there is a lack of coordination and alignment problems within apparel industry itself. One of the participants in the study who heads the Product Development function in the leading retail chain supports this with her quote “This problem is a perennial one for the Indian industry and it needs some maturity to adapt and to compete with the Global retail chain. Further, alignment problem starts at product development stage, where we need to

6 R3 5

R6

Linkage Variable

Independent Variable

R1 Driving Power

4 R12, R5 R4

3

R7

R2

R9

R8

2

Dependent Variable

Autonomous Variable

1

R10 R11

0 0

1

2

3

4

Dependence Power

Fig. 2. Cluster of risks.

5

6

7

convert the consumer taste and participate in designing the supply chains”. This is really impacting the business performance of Indian retail chains. Supplier uncertainty (R4) remains as the transient variable between autonomous and driving quadrant. The risk involved here is that suppliers for apparel retailers are not consistent in handling low volumes and also not responding quickly to the customers due to their innate operating conditions. Their performance also can also be driven by various other factors. The second cluster maps “autonomous variables” that have weak dependence and driving powers. These barriers are highly disconnected from the system and can lead individual effects. Raw material/product quality standards (R2) and uncertainty in demand management (R9) are the members of this cluster. It really endorses the statement from the Delphi participants that Indian retail industry is still at the nascent stage to implement scientifically designed demand management program. “Moreover, this poor demand management could be due to many reasons such as: No evidence of early supplier involvement (ESI) and vendor managed inventory (VMI) in the retail operations citing them as the sophisticated techniques”. (Quote from One of the Delphi participants, working with Leading Retailer in India). Indian apparel retail professionals show little reluctance in adopting those strategies due to their conservative outlook. Further, they are not clear with the specification requirement and it is subjected to change in terms of quality. The poor adoption of accepted standards like American Society of Testing Materials (ASTM) and American Association of Textile Chemists and Colorists (AATCC) in the materials and delivery standards also established as a risk in the supply chain system. Both the above factors act as autonomous effect on the business. The impact could obstruct the growth of Indian retail in the International arena also. Moreover, Indian retailers get confused with the standards to be followed. These risks are major from the customer

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point of view, and new to them, as till now, they act as a manufacturing hub. The next quadrant, dependent variables, has 3 variables as full members (R8, R10 and R11) and R7 acts as the transient variable between dependent and linkage quadrant. Infrastructure Risks (R7) exhibits strong dependence and driving power and should be given more focus than the others. Infrastructure implies warehouse management, logistics network and basic transport needs, regulatory compliance with respect to transport and etc. India does not have the well established infrastructure and regulatory framework in terms of apparel retail industry with the absence of detailed standard operating procedures from the controlling authorities. Though this risk is dependent on many factors, it may cause the delay in the establishment process. Financial risk (R11), which has high risk in cash conversion cycle; low market share; low profit margins; decreasing revenues and etc., is the result or effect of the risks all below in the hierarchy. This is the continuing problem in Indian retail environment, as fellow industry respondents also agreed on clutching the price at the supplier side, without knowing that slowly their efficiency of deriving the product at the low price to the customers is also declining. It will result in many suppliers to go bankrupt and disinterest in sustaining the business. And, it has high dependence power along with the other risks like customer dissatisfaction (R10) and delay in schedule lead time (R8). Lead time management in apparel industry is always a challenging task as Indian design industry does not enjoy recognized and an independent status, still has huge influence on the western and eastern counterparts for following up trends and forecast. Further, similar to other industries, it has a close relationship with customer feedbacks. However, the impact could be, Indian retail industry could enjoy the benefits from established supply base. Retail managers participated in the discussions concurred with the fact that retail buyers are not taking the lead time analysis should be leveled as a value adding activity in the system. But it does not support that and it has the side way of analysis. This methodology will also give a true picture of the criticality of the risks when it is analyzed from the particular company point of view. The trend may not be always the same and it is one of the limitations of this analysis. This modeling exercise can also be applied to other business areas where the interdependence between variables needs to be identified or the root cause of some problem is analyzed. If the dependence power of the variable is zero, it would signify that the variable is one of the root causes of the problem. A higher dependence power may have to be explored further to analyze for the root cause. On the other hand, a variable with zero driving power is the effect of all other factors. The traditional cause effect or fishbone or Ishikawa diagram which is being used by companies to find the root cause of a failure/problem can be replaced by Interpretative Structural Modeling (ISM). Visually, ISM becomes easier to understand the relationship between various interrelated factors leading to a particular effect. Sometimes, those cause effect diagrams may become very complicated and does not give the interdependencies between the variables. Also, ISM model is analogous to one of the new quality control tool i.e. the relations diagram used to explore the cause and effect relationship where the causes are likely to be mutually related. Further, this ISM model of risks and MICMAC analysis are helping us to propose a new calculation method, leading to be the unique contribution for ISM and Risk literature from this research work.

5. Risk prioritization: proposed model The prioritization of risk is essential for the managers to focus on a few risks which act as drivers of other risks. Various models

161

like the Failure Mode Effect Analysis (FMEA), Risk Benefit Analysis (RBA), and Cost Benefit Analysis (CBA) have been developed to prioritize risk based on factors such as the probability of occurrence, severity, and the detection ease (Khan et al., 2008). These models have been accepted by many and criticized by others for removing the element of human judgment (White, 1995). Kraljic (1983) proposes portfolio matrix using risk model as a base factor and Caniels and Gelderman (2005) gives the dependence and driving factors perspective. Further, there has been a debate between those who see risk as objective and those who argue that risk is subjective (Yates and Stone, 1992; Bernstein, 1996; Moore, 1983; Frosdick, 1997; Spira and Page, 2003). Some of works on risk calculation methodologies have been extensively done by Ritchie and Brindley (2007a, 2007b) and Rao et al. (2011). Our paper endorses the view expressed by Lupton (2005) that risk ranges between the techno-scientific perspective, which sees risk as objective and measurable, to the social constructionist perspective, and also sees it as being determined by the social, political and historical viewpoints of those concerned. The models mentioned above do not take into account the interdependence between the various risks. Occurrence of any event is a chance that any event will occur. But the events are not independent. ISM has shown a particular risk is a driver of multiple other risks. It may also be driven by other variables i.e. it may be dependent on various other factors. This is one of the major advantages of ISM over Ishikawa diagrams. In this model, we consider inter relationship between the variables to prioritize the risks along with severity or cost impact and the ease of detection of the risk. As far as risks in supply chain it is very difficult to assign a probability of occurrence to any particular event because of the uncertainty associated with it. Risk is measurable and can be estimated from the probabilities of the outcome. But, uncertainty is not quantifiable and probabilities of the outcome are not known (Knight, 2012). Yates and Stone (1992) also argue that risk implies uncertainty about the prospective outcome and if the probability of the outcome is known then there is no risk. Slack and Lewis (2001) discuss both the points. With both these arguments in existence, here we consider occurrence of the risks to be uncertain and very difficult to measure in terms of probability. Thus, with the existing FMEA as a base framework, we propose a new model to prioritize risk using the results of Fuzzy ISM. The driving and the dependence power of each variable derived from the fuzzy MICMAC analysis replace the factor “occurrence” in the current risk prioritization number formula (RPN¼ Occurrence  Severity  Detection). With this existing one, it is very difficult to quantify the exact probability of occurrence. So, the factor “driving power” divided by “dependence power” is proposed to be used as a measure of the occurrence of the uncertainty or risk. Higher the factor, chance of the occurrence of the event is more. Thus, it helps us to quantify the strength of occurrence in the supply chain system. So, Risk Prioritization Number can be found out by using the new formula based on ISM – MICMAC analysis:

Risk Prioritization Number = severity × detection ×

driving power dependance power

The higher the cost or severity associated with a risk higher will be the criticality of the risk. More the driving power of the risk, its ability to initiate other problems in the supply chain is high. So, risks with higher driving power must be given higher priority. Similarly, the higher the dependence power of a risk, more variables lead to this particular risk. The main focus must be shifted to it root causes i.e. its drivers and a lower priority will be assigned to risk with higher dependence power. Risk with highest dependence power is more of an effect than a cause to any other event. Thus, the above mathematical

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formula can be used to prioritize risk taking into account the severity, detection and mutual dependence of the variables or risk in this particular case. This can be applied in any industry which is having fuzzy ISM (MICMAC) analysis. While using the risk prioritization proposed formula above, the factors having zeroed driving or dependence power should not be ranked using the method for the particular domain. The variables can be directly assigned priority by qualitative analysis of efforts to mitigate the particular cause or using the cost associated with it. However, it needs to be validated through the various 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 becoming very practical and relevant according to the chosen business domain. Risk calculation methodology also should be a practice based one and it eventually supports strategic and decision making process in a supply chain (Tummala and Schoenherr, 2011). This paper endorses the practice based research from Indian context. The present study also proposes a new model for RPN calculations, which is going to be an imperative and basic tool for major supply chain practitioners and organizations in the risk analysis. The retail strategists/managers can use ISM framework within their service environment to classify risk factors depending on their impact on the supply chain system using the structured approach. Further, these elements can also be useful for deriving RPN values to rank based on the driving and dependence power. Followed by that, a comprehensive plan for the supply chain risk mitigation plan can also be designed. The study gives directions on risk evaluations with a discrete approach. Along with the analysis of current situation inside the company with respect to risks, it is equally important to anticipate the future. One of the methods could be pure play analysis of companies from the same domain. Thus, by giving importance to supply chain risk management, a company can reduce extra costs and improve their bottom line. Also, retail chains trying to enter India or currently operating can also apply these model building and the findings of this paper would help them to design the strategies for mitigating those risk factors.

Structural Equation Modeling (SEM). The variables under consideration are very limited and generic as this is the initial phase of the study. These variables can have the multitude effect having a different degree of inter-relationships. This model and methodology will be foolproof when applied to a single company environment, where the costs and frequency of occurrences of disruptions are recorded in the history to prioritize risk. Depending on the situation, mitigation strategies can be formulated taking into consideration the budget and efforts required. The risk prioritization model can also be validated through various sub-domains/industry stakeholders across the supply chains. Case studies engaging different dynamics and business environments can also be developed to ascertain the newly proposed Risk Priority Model.

8. Conclusion It is very important for the managers to know and understand the risks involved in apparel retail supply chains. And, the interdependence of the risks may also result into chain of risks there increasing the costs of mitigations. One risk may lead to various other disruptions also causing domino effect. It is therefore essential to take preventive action after thorough analysis of each risk and prioritizing them using the suggested method. The probabilities of known risks must be carefully assigned by looking into past records. The cost of preventive action along with the cost of corrective action can also be considered (cost benefit analysis). Results, in case the probabilities of future events, are not well defined. Structural Equation Modeling (SEM) approach can be suggested in the future scope that could help manager to understand whether the retail professionals are on the same line with respect to the interdependencies of risks or causes of a particular problem. It would then help to take corrective action with total employee involvement and through other modes as well.

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

7. Limitations and scope for further research The ISM model developed here is based on the expert opinion and it may be biased and limited to a particular industry that they belong to. The links established using ISM may be tested for validity using

Appendix A See Tables A1–A3.

Table A1 Risk assumptions. Risk no.

Risk

Background of risks

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

Globalization Raw material and product quality Scarcity of resources Supplier uncertainty Lack of co-ordination/alignment Behavioral aspect of employees Infrastructure risks Delay in schedule/lead time Demand uncertainty Customer dissatisfaction financial risk Security and safety

Currency fluctuations; design transfers, competition; legal and political risk; policy changes; etc. Retailers do not have the complete SOP of the product quality and it varies from season to season/and product to product Scarcity of raw material; power shortage; labor shortage; resource cost; cost of technology etc. Failure to deliver on time; supplier bankruptcy; unreliable supplier; Cost and quality not reliable/consistent; etc. Lack of communication; no cross functional teams; no transparency between partners/departments; etc. Employee disputes; inefficient/unskilled employee; resistance to change; unavailability of labor due to absence; etc. Transport breakdown; inadequate means of transport; inconsistent warehouse facility; IT failure; etc. Order fulfillment error; change in production schedules; machine breakdown; delay in delivery; change in design; etc. Error in demand forecast (short term or long term); bullwhip effect; short product life cycle; risk from new entrants; etc. Product returns; customers complaints; reduced demand; stock out; poor quality; wrong product delivery; etc. High cash conversion cycle; low market share; low profit margins; decreasing revenues; etc. Pilferages and shrinkage of the materials in the warehouse/losses in transit, performance of the product, cyber attack etc.

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Table A2 Summary of ISM research works from 2000 to 2012. Authors(s)

Research objective

Gorane and Kant (2013)

To identify the supply chain management enablers (SCMEs) and establish relationships among them using interpretive structural modeling (ISM) and find out driving and dependence power of enablers, using fuzzy MICMAC analysis. Pfohl et al. (2011) Structural analysis of potential supply chain risks using Interpretive Structural Modeling (ISM) and MICMAC analysis methodology. Tummala & Schoenherr The purpose of this paper is to purpose a comprehensive and co(2011). herent approach for managing risks in supply chains.

Olson and Wu (2011)

To compare tools to aid supply chain organizations in measuring, evaluating and assessing various risks.

Jharkharia (2011)

To understand mutual influences of the factors those adversely impact the process and results of ERP.

Farooq and O’Brien (2010)

To present results of a developed technology selection framework and provide insights into the risk calculation and their implication in manufacturing technology selection process. To address the issue whether supply chain members should strive to build the trust or strive to reduce the risk with its members and from which perspective?

Laeequddin et al. (2009)

Pujawan and Geraldin (2009)

To provide a framework to proactively manage supply chain risks.

Manuj and Mentzer (2008)

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

Research findings This paper has identified 24 key SCMEs and developed an integrated model using ISM and the fuzzy MICMAC approach, which is helpful to identify and classify the important SCMEs and reveal the direct and indirect effects of each SCME on the SCM implementation. ISM was proved to be a effective methodology to establish inter-relationship among supply chain risks. The Supply Chain risk Management Process (SCRMP) framework proposed here is a coherent and comprehensive approach for managing risks and uncertainties associated with a given problem. Risk identification, measurement, assessment, evaluation, mitigation and control strategies have been discussed in detail. The SCRMP can be used as an aid in making decisions. The work considers the strategies of outsourcing to China and other nations. It offers many cost advantages as low cost producers anywhere can compete. There are greater risks with outsourcing but these can be handles using the ability to communicate in real time (via Internet). The use of Data Envelopment Analysis and Monte Carlo Simulation for evaluation of risk on hypothetical data shows that vendors from the Great China are preferred to those from western nations due to low risk-adjusted cost and higher efficiencies. The Interpretative Structural Modeling (ISM) has been used to establish the relationship between the critical factors. Three factors namely, poor understanding of business implications and requirements, poor data quality and lack of top management support, have been identified as drivers for ERP implementation and hence need serious attention. The paper explains the role of risk and an approach to calculate risk in the manufacturing technology selection process. The research quantifies the risk involving different manufacturing technology selection alternatives. A conceptual framework was developed considering five key perspectives: characteristics, economics, dynamic capabilities, technology, and institutions to evaluate the risk in a relationship. These perspective of risk can initiate and build trust between supply chain members in the global business environment. The two house of Risk (HOQ) models have been adopted to supply chain risk management. HOQ1 determines which risk agents in the five supply chain processes (SCOR) are to be given priority for prevention. HOQ2 gives priority to those actions considered effective but with reasonable money and resource commitments. Six risk management strategies have been suggested depending upon the nature 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 the process 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 supply chain management is still in its nascent stages and all the models for risk measurement need to be empirically tested. Faisal et al. (2006) To present an approach to effective supply chain risk mitigation using The model shows that there exists a group of enablers having a high driving ISM. To understand the dynamics between various enablers that help power and low dependence power which requires maximum attention and is of strategic importance. Another group of variables consists of those to mitigate risk in the supply chain. variables which have high dependence and are the resultant actions. Gaudenzi and Borghesi To evaluate supply chain risks that stand in the way of supply chain The application of AHP is helpful particularly to support the prioritization (2006) objective using Analytical Hierarchy Process (AHP). of objectives and the analysis of overall impact. The establishment of through consideration of critical issues requires the involvement of managers from different areas. Cucchiella and Gastaldi To individualize a framework to manage uncertainty in the supply The risk management framework has been designed using the real option (2006) chain finalized to reduce the firm risk. theory. After analyzing the risk characteristics it is possible to individualize the real options that better suit the risk under consideration. The outsource option has been tested using Mat Lab to cover two risks related to production capacity and price fluctuation. Kleindorfer and Saad To develop a conceptual framework that reflects the joint activities of The framework “SAM-SAC” consists of six activities (1) Specification of sources and vulnerabilities (2005) risk assessment and mitigation that are fundamental to disruption (2) Assessment risk (natural disaster, strikes, economic disruptions, etc.) in supply (3) Mitigation chain. (4) Strategies with dual dimension

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Table A2 (continued ) Authors(s)

Research findings

Research objective

(5) Actions and (6) Necessary conditions The model involves the process of identifying, assessing, planning and implementing solution, conducting FMEA analysis, and doing continuous improvement. Nine categories of risks have been identified and the impact of eight mitigation strategies on these risks has been assessed. The following two managerial 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.

Sinha et al. (2004)

To propose a methodology to mitigate supply chain risks.

Chopra and Sodhi (2004)

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

Zsidisin et al. (2004)

To explore, analyze, and derive common themes on supply risk assessment techniques.

Harland et al. (2003)

The research describes the development of a risk tool to increase visibility of risk in the supply chain network. The tool was tested on four case studies in the electronics sector. The case studies were conducted to design a framework for the tool. The main objective of this paper is to provide a grounded definition The paper provides academicians and practitioners a starting point to understand the supply risks and to provide insights to how these risks can of supply risk. Case studies from various purchasing organizations negatively impact the business environment. have been considered for this purpose.

Zsidisin and Ellram (2003)

The paper provides the supply managers insights into the techniques their firms can adapt to assess supplier risks. the cases studied would help purchasing 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

The purpose of the paper is to provide a review of definition and classification of risks. It also provides a holistic view of risk assessment and measurement.

Table A3 Questions for Delphi rounds No. Questions

Key words/issues

1 2 3 4

Globalization Product standards Raw material SOP systems

5 6 7 8 8 9 10 11 12 13 14 15 16 17 18 19 20

How do you rate the globalization is having perceived risk for Indian retail companies? How do you perceive the impact of product quality and standards affect the apparel retail business? Are you seeing the availability of raw material and their quality standards in India is a risk to the retail business? Absence of standard systems/operating procedures in the retail environment is a risk to the business – How do you see? Do you include the non-availability of the skilled resources in the retail domain as a major risk in the Indian environment? Availability of less merchandise options in different forms of retail gives some amount of risk to the business, in terms of making merchandise available to the people. How do you see as a risk to the business? Perception of small players that retail business is a domain to be handled only by the big corporate houses with huge investments – Is that a threat to the development? How do you rate the impact of reliability of the suppliers in the retail business? Do you see the lack of alignment of the stakeholders with the retail firm is high? What kind of threats posed by? How do you see the behavior practices of the employees risk the retail business as they are directly in touch with the customers? Financial risk is always there with the retail business? From the Buying as well as the supplier's perspective? Retail business is impacted by a delay in the lead-time of the product? How does it impact the business Customer dissatisfaction will lead to a huge impact on the Indian retail business? 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. How do you rate the pilferages and shrinkages control system, is that a risk to the retail business? Employee orientation methods are not being given on the retail business. Is it perceived to be a risk? Customization trend in the business is at the very low level at the Indian retail. Is that posing a threat to the business growth? India does not have the logistics support to the retail industry in terms of warehousing and advanced systems such as VMI and all, Is that a threat to the business performance? Does Indian retail follow the global trend and do not have the sense of developing the products for the domestic customers? Is that a risk to the business? Existing HR policies in the retail domain is a big threat? Fear of dominance of Foreign brands is also posing a risk to the retail business?

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Resources availability Less options in merchandise Big players threat Reliability of suppliers Misalignment in the business Employee behavior Financial Risk Lead time handling Customer satisfaction Infrastructure risk Retail systems Employee orientation Customization Logistics support Alignment with international clothing trends HR policies Proposed FDI

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