A grey-layered ANP based decision support model for analyzing strategies of resilience in electronic supply chains

Engineering Applications of Artificial Intelligence 87 (2020) 103338

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Engineering Applications of Artificial Intelligence journal homepage: www.elsevier.com/locate/engappai

A grey-layered ANP based decision support model for analyzing strategies of resilience in electronic supply chains✩ R. Rajesh Management Division, ABV-Indian Institute of Information Technology & Management, Gwalior 474015, India

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Keywords: Supply chain resilience Supply chain risk Resilient strategies Grey theory Analytic network process

ABSTRACT Augmented globalization and vertical integration have made contemporary supply chains an intricate network subject to a number of vulnerabilities. Preemptive measures are needed for dealing with mutable risks and vulnerabilities to safeguard robust supply chain systems. Supply chain risk management (SCRM) connotes a set of risk management responses essentially instigated to confront supply chain risks. As supply chain risks are intertwined, one resilient strategy for risk mitigation can moderate several supply chain risks. A complex decision making problem involving twelve major supply chain risks and twenty one resilient strategies for risk mitigation have been acknowledged in this research with archetypal focus on electronics manufacturing supply chains. A combination of Multi criteria decision aid (MCDA) and artificial intelligence (AI) is increasingly used in decision making of complex real world problems. A decision support model incorporating an amalgamation of grey theory and layered analytic network process (ANP) has been employed for quantifying various resilient strategies for risk mitigation. The proposed model was also applied in a practical setting taking a case study of an electronics manufacturing company. Sensitivity analysis was also conducted to ensure the robustness of obtained results. The combined methodology proposed in this research could be effectively used by top management, to pigeonhole the resilient supply chain strategies for better managing their supply chains.

1. Introduction Supply chain systems are becoming more lengthy and complex, as a result of increased globalization and vertical integrations. In this scenario, proactive practices need to be adopted for securing supply chain systems by tackling core vulnerabilities. Vulnerabilities along with associated supply chain risks increases with increasing interactive complexities of the supply networks (Christopher and Peck, 2004). Hence, complex decision making situations involving evaluation of multiple criteria using several decision aids is becoming typical in modern supply chains. Multi criteria decision aid (MCDA) and artificial intelligence (AI) have been applied for increasing the success of decision making considering complex real world problems. Supply chain risk management (SCRM) characterizes tools and practices of complex real world problems to better manage various supply chain risks (Christopher and Lee, 2004). SCRM extends its principles and practices over three major areas namely, supply chain management, enterprise risk management and crises management. As most of the companies develop plans to protect their supply chains against recurrent low impact risks and usually avoid high impact low likelihood risks, it is essential that SCRM should be integrated with supply chain planning (Chopra and Sodhi, 2012).

The resilience capabilities and the sustainable competitive advantage of the firm increases when the level of risk sharing and the top management support increases (Ponomarov and Holcomb, 2009). The level of risk sharing is dependent on continual risk assessment, risk analysis and risk mitigation practices. Natural disasters, war, terrorism or any other external factors could lead to sudden unexpected break downs of supply chain, known as disruptions. Apart from that, security related issues are more frequently atop the minds of end consumers making it necessary for the members in the supply chain to take a new look at vulnerability measures. As observed from a complexity perspective of SCRM, managers are advised to reduce the interactive complexity of the supply chain by reducing the level of buffers at different stages. If the network is having a reasonably high interactive complexity, improved buffer levels are recommended to manage potential vulnerabilities (Yang and Yang, 2010). Managers will be able to tailor balanced and effective risk mitigation strategies for their firms, by properly understanding the supply chain risks, its variety and inter connectedness. Also, managers should take into consideration of the frequency of occurrence of similar risks, as a measure of supply chain vulnerability (Manuj and Mentzer, 2008). In general, in order to meet the challenges of a turbulent business environment and to tackle vulnerabilities, structural flexibility of the supply

✩ No author associated with this paper has disclosed any potential or pertinent conflicts which may be perceived to have impending conflict with this work. For full disclosure statements refer to https://doi.org/10.1016/j.engappai.2019.103338. E-mail addresses: [email protected], [email protected].

https://doi.org/10.1016/j.engappai.2019.103338 Received 30 June 2016; Received in revised form 12 March 2018; Accepted 28 October 2019 Available online xxxx 0952-1976/© 2019 Elsevier Ltd. All rights reserved.

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Engineering Applications of Artificial Intelligence 87 (2020) 103338

chains, as the lack of a single component can halt total production and deliverables. As supply chain risks are customarily intertwined, one resilient strategy for risk mitigation can curb the effects of many related risks. This leads to a number of research questions as; which resilient strategy for risk mitigation has an overall high effectiveness on curbing various supply chain risks? How to prioritize the implementation of resilient strategies in consideration of its risk reduction capabilities? Can the effects of resilient strategies over diverse supply chain risks be quantified? A grey-based decision support model has been proposed in this research to rationally quantify the strategies of supply chain resilience. Since the number of strategies considered in this research is relatively large, conventional decision making models needs very large amount of data to be processed making it practically infeasible or results less reliable. Concept of layered ANP model has been proposed in this research to quantify attributes when the number of attributes to be considered is quite large. Incorporation of grey theory aids in transforming human/judgmental information into grey values that could be further analyzed using computations involving grey theory. A combination of grey theory and layered ANP could effectively find answer to the above posed research questions. This paper is further organized as follows. Section 2 discusses on the motivation and the background of the study. This is followed by constructing the framework of supply chain risks and resilient strategies for risk mitigation in the context of electronic supply chains and the relevant literature, as indicated in Section 3. The procedure of proposed methodology using a combination of grey theory and layered ANP to identify and quantify resilient strategies for supply chain risk mitigation is described in Section 4. Section 5 details the evaluation of the proposed framework for selection of resilient strategies in a real case electronics manufacturing company. Section 6 talks over the results and Section 7 discusses on the implications of this research from the case company perspective, which is followed by the conclusions and scope of future work as discussed in Section 8.

Nomenclature SCR RSCS PNEV ⊗ 𝑎𝑘𝑖𝑗 ⊗ 𝑏𝑘𝑔𝑖𝑗 𝑘 ⊗ 𝑐ℎ𝑖𝑗

⊗ 𝑎̃𝑖𝑗 ⊗ 𝑐̃ℎ𝑖𝑗 ⊗ 𝑏̃ 1 𝑔𝑖𝑗 ⊗ 𝑏̃ 2 𝑔𝑖𝑗 ⊗ 𝑏̃ 3 𝑔𝑖𝑗 ⊗ 𝑏̃ 𝑓 𝑔𝑖𝑗 ⊗ 𝑎̇ 𝑖𝑗 ⊗ 𝑏̇ 𝑓 𝑔𝑖𝑗 ⊗ 𝑐̇ ℎ𝑖𝑗 𝑎𝑖𝑗 𝑏𝑓 𝑔𝑖𝑗 𝑐ℎ𝑖𝑗 𝑎∗ 𝑖𝑗 𝑏𝑓 ∗ 𝑔𝑖𝑗 𝑐 ∗ ℎ𝑖𝑗 𝐴∗

Supply Chain Risk Resilient Supply Chain Strategy Principal Normalized Eigen Vector Grey matrices representing importance of SCR ‘𝑖’ over ‘𝑗’ rated by the 𝑘𝑡ℎ analyst Grey matrices representing the importance of RSCS ‘𝑖’ over ‘𝑗’ for each SCR, ‘𝑔’, rated by the 𝑘𝑡ℎ analyst Grey matrices representing the importance of SCR ‘𝑖’ over ‘𝑗’ for each RSCS, ‘ℎ’, rated by the 𝑘𝑡ℎ analyst Average grey matrix of importance relations among SCR Average grey matrices of importance relations of SCR for each RSCS Grey matrices of importance relations among RSCS ∀ 1 ≤ 𝑖 ≤ 𝑛∕3; 1 ≤ 𝑗 ≤ 𝑛∕3; 1 ≤ 𝑔 ≤ 𝑚 Grey matrices of importance relations among RSCS ∀ 𝑛∕3 < 𝑖 ≤ 2𝑛∕3; 𝑛∕3 < 𝑗 ≤ 2𝑛∕3; 1 ≤ 𝑔 ≤ 𝑚 Grey matrices of importance relations among RSCS ∀ 2𝑛∕3 < 𝑖 ≤ 𝑛; 2𝑛∕3 < 𝑗 ≤ 𝑛; 1 ≤ 𝑔 ≤ 𝑚 Grey matrices of importance relations among RSCS; 1≤𝑓 ≤3 Normalized grey matrix of ⊗ 𝑎̃𝑖𝑗 Normalized grey matrices of ⊗ 𝑏̃ 𝑓 𝑔𝑖𝑗 Normalized grey matrices of ⊗ 𝑐̃ℎ𝑖𝑗 Total normalized crisp matrix of ⊗ 𝑎̃𝑖𝑗 Total normalized crisp matrices of ⊗ 𝑏̃ 𝑓 𝑔𝑖𝑗 Total normalized crisp matrices of ⊗ 𝑐̃ℎ𝑖𝑗 Final crisp matrix of ⊗ 𝑎̃𝑖𝑗 Final crisp matrices of ⊗ 𝑏̃ 𝑓 𝑔𝑖𝑗 Final crisp matrices of ⊗ 𝑐̃ℎ𝑖𝑗 Crisp relation matrix of 𝑎∗ 𝑖𝑗

𝐵𝑔𝑓 ∗ 𝐶ℎ∗ 𝐴̇ ∗

Crisp relation matrices of 𝑏𝑓 ∗ 𝑔𝑖𝑗 Crisp relation matrices of 𝑐 ∗ ℎ𝑖𝑗 AHP input matrix of 𝑎∗ 𝑖𝑗

𝐵̇ 𝑔𝑓 ∗ 𝐶̇ ∗ 𝑒1 𝑒𝑔2

AHP input matrices of 𝑏𝑓 ∗ 𝑔𝑖𝑗 AHP input matrices of 𝑐 ∗ ℎ𝑖𝑗 PNEV value corresponding to 𝐴̇ ∗ Set of PNEV values corresponding to 𝐵̇ 𝑔1∗

𝑒𝑔3

Set of PNEV values corresponding to 𝐵̇ 𝑔2∗

𝑒𝑔4

Set of PNEV values corresponding to 𝐵̇ 𝑔3∗

𝑒ℎ5

Set of PNEV values corresponding to 𝐶̇ ℎ∗ ANP super matrix of Layer 1 comprising of PNEV values, 𝑒1 , 𝑒𝑔2 and 𝑒ℎ5 ANP super matrix of Layer 1 comprising of PNEV values, 𝑒1 , 𝑒𝑔3 and 𝑒ℎ5 ANP super matrix of Layer 1 comprising of PNEV values, 𝑒1 , 𝑒𝑔4 and 𝑒ℎ5 AHP input matrix of Layer 2 of importance relations among RSCS for each SCR AHP input matrix of Layer 2 of importance relations among SCR for each RSCS ANP super matrix of Layer 2 comprising of PNEV values, 𝑒1 , 𝑒𝑔6 and 𝑒ℎ7

ℎ

𝐴̈ 𝐵̈ 𝐶̈

𝐵𝑔∗∗ 𝐶ℎ∗∗ 𝐷̈

2. Motivation and background of the study Supply chain systems are prone to ample internal and external turbulence that could often lead to mismatches in demand and supply, or even disruptions. Effective implementation of supply chain risk management practices is essential to build resilience in supply chain systems (Rajesh, 2019). These risk management practices can act as resilient strategies for risk mitigation in supply chains. The right combination of resilient strategies for risk mitigation should be carefully planned to effectively reduce their combined effect over the entire risk profile of the company (Rajesh and Ravi, 2017; Rajesh, 2017). As we see many of the disruptions in the past were supply related, upstream of supply chains need to adopt flexible supply strategies. This can include strategies for multiple suppliers or flexible supply contracts. The famous Nokia–Ericsson case, remind us the need of flexible strategies for sourcing. Relying on single sourcing was the prime reason for the huge loss incurred by Ericsson, while Nokia managed to continue their production due to flexible strategies. Reading from the literature on artificial intelligence and risk analytics, we could perceive that the topic has gained considerable attention during the past decade. This is due to the increasing need of specialized tools for handling the amplified uncertainties of today’s supply chains in the era of globalization, vertical integration, and increased collaborations. Wu et al. (2015) have provided a comprehensive review on decision-making problems in Enterprise Risk Management (ERM). They analyzed the development of management science models in three of the major risk management application areas such as; financial risk analytics, supply chain risk analytics, and environmental and social risk analytics. They reviewed a broad spectrum of management science modeling ranging from analytic models for optimizing decisions, mathematical programming models for optimizing interacting systems, and simulation modeling for numerical analysis of uncertainties.

chain is looked-for, which can build flexible options into the design of supply chains (Christopher and Holweg, 2011). Electronic manufacturing supply chains are typical cases in point of vulnerable supply 2

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with real insights. Ponomarov and Holcomb (2009) recognized the scope of resilience from ecological, economic, social and psychological perspectives. Tang and Nurmaya Musa (2011) pointed out the need of multidisciplinary, integrated view of SCRM practices for proactive risk management. Chopra and Sodhi (2012) opined that by continually stress testing the supply chains and tailoring reserves, managers will be in a position to protect and improve the bottom line against different types of supply-chain risks. Monroe et al. (2014) conducted a vast literature study on supply chain vulnerabilities and disruptions to develop vivid understanding of the possible supply chain risks and the risk mitigation strategies. Hofmann et al. (2013) suggested practices for addressing sustainability issues in resilient supply chains. It is also seen that risk models and methodologies were also developed in literature (Fahimnia et al., 2015). Talluri et al. (2013) proposed a methodology incorporating data envelopment analysis and nonparametric statistical methods to analyze and rank alternative mitigation strategies. Marley et al. (2014) examined current supply chain processes and analyzed alternative strategies of risk mitigation using normal accident theory and its constructs. Vilko et al. (2014) examined supply chain risk management by considering the levels and nature of uncertainty in supply chains. Rajesh et al. (2015) conducted studies on the positive and negative influences of risk mitigation strategies on various risks and formulated an index for the selection of best risk mitigation strategy. However, the study needs to be extended to address the following questions; How effectively resilient strategies can mitigate overall risks by considering each and every individual importance relation? How to quantify resilient strategies for risk mitigation if the dependence relations among risks are prominent? How do the importance relations among various supply chain risks changes when a particular risk mitigation strategy alone is considered? Answering these questions, the actual problem of prioritization of risk mitigation strategies has to be modified to a complex problem involving several dependence relations and network connections. Also, supply chain risks are customarily interlinked and can be seen in association with certain characteristics in general, like lead time, flexibility, integration and sourcing. This reveals the existence of pronounced clusters of supply chain risks. In general, risk mitigation strategies may have effects over a cluster of supply chain risks due to their general traits. These clusters might have diverse priorities allowing for the nature of supply chains. ANP is found to be the best tool in cases of the aforesaid situations where there are dependences, network connections and an increased need for setting cluster priorities in the decision making model. Several classifications of supply chain risks and mitigation approaches can be seen in literature, of which the categories relevant to electronic manufacturing supply chains were identified and framed for this study. The risks identified were classified into four clusters for the ease of monitoring. Also, it facilitates the managers to set cluster priorities in the decision making models if desired. The clusters representing risk groups are as follows, (i) Lead-time related risks: Forecast risks, delay risks and receivable risks are classified under this cluster. Forecast errors and inadequate insights of quantitative forecasting can affect the responsiveness of the supply chain and could extend the lead times of delivery (Chopra and Sodhi, 2012). Delay risks are the resulting risks arising from longer lead times and that could culminate in lost sales or diminished reputations of the firms (Braunscheidel and Suresh, 2009). Receivable risks occur when the lead time of receivables extends often due to bad financial status of the customers (Tsai, 2008). (ii) Flexibility related risks: Supply chain design risks, capacity risks, inventory risks and risks of contingencies are categorized under this cluster. Design risks occur when the supply chain design become inflexible to accommodate market fluctuations or changing needs of customers (Klibi et al., 2010). Too high or too low capacity utilizations results in capacity inflexibility where the firm cannot meet fluctuations

Wu and Birge (2016) have given a brief review on the need of risk analytics in the era of big data. They have proposed a fieldbased and property-based classification of risks and commented on the importance of new risk management techniques, such as Enterprise Risk Management (ERM) in the era of big data. This is important as it seeks the most effective ways to manage and mitigate financial and operational risks through a systematic and integrated approach. Wu and Wu (2016) conducted studies on the pricing strategies of firms during uncertain demands. They have analyzed the deterministic and stochastic policies for monopoly and duopoly cases of markets, and commented that the price volatility and dynamic pricing allow firms to use information to have greater control over the uncertainties. The influence of demand uncertainties over the pricing strategies of the firm are determined by parameters such as discount rates and demand/ cost dynamics. Wu et al. (2017) analyzed the increasing practices of incorporating artificial intelligence into problems for decision-making in engineering risk analytics. According to them, today’s systems carry high levels of uncertainties and hence, intelligent systems such as expert systems, artificial neural networks, support vector machines, evolutionary computations, fuzzy systems, knowledge based systems, case based reasoning, agent based systems etc. are needed to tackle the increasing levels of uncertainties. They also commented that risk analytics and artificial intelligence are most important topics for today’s supply chain systems in the era of globalizations in consideration of the increasing threats from natural, engineering, economic, and technical sources. From the literature, it is evident that the needs for artificial intelligence and related tools have proliferated for risk analytics in modern supply chain systems. This is the primary motivation for this research. For the effective implementation of resilient strategies for risk mitigation, it is desirable to quantify them for their effectiveness over individual risks. The problem become complex as the resilient strategies for risk mitigation can have influence over themselves. The same stands true for the case of risks as; one risk may have a direct or indirect effect over the other. We have used a combination of grey theory and a proposed a layed analytic process approach to deal with this complex problem. Since there are judgmental decisions in the inputs, grey theory finds suitable to deal with the related uncertainties. Uncertainties in decision making environments can be of different forms such as: (i) stochastic uncertainties, which deals with uncertainties associated with probabilistic outcomes; (ii) un-ascertainties, dealing with events or data where only some of the possible outcomes of an event are known; (iii) rough uncertainties, dealing with uncertainties in approximating data or values; (iv) fuzzy uncertainties, which are related to cognitive uncertainties in decision-making; and (v) grey uncertainties that occur due to uncertainties in informational data (Deng, 1989). We have taken judgmental data for inputs and the related uncertainties are informational uncertainties. Hence, the use of grey systems and related theories are recommended to handle the associated uncertainties. Grey theory is every so often used when there are clear extensions and unclear intentions, while fuzzy theory is used when there are clear intentions and unclear extensions (Wang et al., 2014). The initial uncertainties in decision-making are managed through grey numbers and a sequence of operators for whitenization of the grey data. 3. Framework of risks and resilient strategies for risk mitigation Jüttner et al. (2003) demarcated the concepts of supply chain vulnerability and its managerial counterpart, supply chain risk management. Christopher and Peck (2004) in their research defined supply chain resilience and elaborated strategies for constructing a resilient supply chain. They concluded that resilience can chiefly be achieved through flexibility and agility. Sheffi (2005) explained supply chain resilience with practical acumens and pointed measures for building resilient supply chains through redundancy and flexibility. Tang and Tomlin (2008) identified six of commonly occurring risks in supply chains and proposed flexible strategies for mitigating those risks 3

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theory and layered ANP methodologies has been used in this research to quantify the resilient strategies for risk mitigation. In general, risk mitigation strategies may have effects over a cluster of supply chain risks due to their general traits. These clusters might have diverse priorities allowing for the nature of supply chains. ANP is found to be the best tool in cases of the aforesaid situations where there are dependences, network connections and an increased need for setting cluster priorities in the decision making model. Still, there are quite a few issues to be addressed, when ANP is used as a tool for solving the present problem. Exact information for inputs to ANP is not available as most of the supply chain risks and mitigation strategies are seldom quantifiable; hence a combination of grey theory to deal with judgmental information becomes essential. Apart from that, the problem needs comparison of importance among each risk with respect to every mitigation strategy and the importance of each mitigation strategy for every individual risks for making the selection problem more transparent and effective. But it will ultimately result in an outsized number of comparison matrices resulting in a massive super matrix, as detailed under step (6), Section 4. The concept of layered ANP has been proposed in the research where the specific outputs obtained from the ANP selection model of one layer is used as inputs to the next layer, thereby reducing the number of comparison matrices as well as the size of the super matrix, which is elaborated for the case under step (6) of Section 5. By employing the proposed methodology, the most effective strategies for supply chain resilience can be identified, analyzed and implemented for effectively reducing the overall vulnerability of the supply chain. This is possible as it considers the entire importance relations among individual risk for every mitigation strategy and vice versa, through a meticulous analysis. A flow chart representation of the proposed methodology using a combination of these methodologies is shown in Fig. 1. The notations used for representing various ratings, values, and matrices are shown in the nomenclature part of this paper. I will make a brief of the notations used for demonstrating the methodology, which is a combination of grey theory and layered ANP approaches. ⊗ 𝑎𝑘𝑖𝑗 can

in demand (Cucchiella and Gastaldi, 2006). Similarly, too high or too low levels of inventory results in inventory inflexibilities, which would be reflected in product strategies (Cachon, 2004). Risks of contingencies are usually high impact risks which can be managed through a mix of several flexibilities. (iii) Integration related risks: Technology risks and system risks are classified under this cluster, where technology risks occur when the manufacturer uses obsolete technologies or fails to integrate recent technologies into their supply chains (Tomlin, 2006). System risks occur due to the integration of inappropriate system applications into the core supply chain operations (Tang and Nurmaya Musa, 2011). (iv) Sourcing related risks: Procurement risks, continuity risks and IPR risks are categorized under this cluster. Sourcing risks of procurement can occur in three forms; related with supply cost, supply commitment and supply stability (Xu, 2010). Continuity of supply chains is one of the greatest challenges associated with logistics outsourcing. Operators form partnerships that increase the likelihood of diminishing transparency of the supply chain (Craighead et al., 2007). Outsourcing or offshoring also results in severe IPR risks, as it is difficult to protect the IPR when the firm is a global manufacturer or supplier (Holweg et al., 2011). A framework showing the risk categories considered for the present study and their relevant literatures are shown in Table 1. A number of risk mitigation approaches are generally practiced by leading manufacturing firms for building resilience in their supply chains. The detailed framework of resilient strategies for risk mitigation considered, their contribution in resilience of the supply chain and the related literatures are presented in Table 2. For the ease of reference and convenience of understanding the supply chain risks (SCRs) and the resilient supply chain strategies (RSCSs) were given four letter reference codes, as shown in Tables 1 and 2, respectively. 4. Methodology Integration of MCDA and AI provides new capabilities for structuring complex decision making situations in static and distributed environments. Some of them are to handle massive information or to model ill-structured information, while some others aid in the construction of advanced decision models or to develop efficient computational optimization algorithms. Grey theory can be effectively used for the construction and evaluation of advanced decision models. It was formulated from grey sets by combining principles of system theory, space theory and control theory (Deng, 1982). It can be meritoriously used to generate satisfactory outcomes using a relatively small amount of data or data with great variability among factors and to solve uncertainty problems in cases of discrete data and incomplete information (Deng, 1989; Rajesh and Rajendran, 2019). Grey theory has been widely used by researchers to handle ambiguity generated from human judgments through regularizing the data with proper treatment (Wang et al., 2014; Zeng et al., 2016). Also, it is easy to convert grey numbers into crisp numbers by applying modified-CFCS (Converting the Fuzzy data into Crisp Scores) method, employing a three step procedure (Fu et al., 2012). Analytic Network Process (ANP) is a powerful tool for analyzing cases of various decision making problems with dependence and feedback connections (Saaty, 1996). In ANP, criteria, sub-criteria and alternatives considered for selection are treated equally as nodes in a network. Each of these nodes might be compared to any other nodes as long as there exists a relation between them. Nodes are grouped in clusters so as to introduce cluster priorities, if desired. A matrix is constructed by representing all nodes of the network horizontally and vertically named as unweighted super matrix in the network model, where each element represents a relation between the nodes. This matrix is then normalized to weighted super matrix of the network model. The alternatives are ranked by calculating the limit matrix through synthesizing the weighted super matrix. A combination of grey

represent the importance relations among risks, as rated by analysts, 𝑘 can represent the importance relations for whereas; ⊗ 𝑏𝑘𝑔𝑖𝑗 , and ⊗ 𝑐ℎ𝑖𝑗 each risk in consideration of the resilient strategies, and the importance relations for each resilient strategy in consideration of risks. ⊗ 𝑎̃𝑖𝑗 , and ⊗ 𝑐̃ℎ𝑖𝑗 can represent the average grey matrices for calculation, and the matrices ⊗ 𝑏𝑘𝑔𝑖𝑗 are separated into three set of sub-matrices, represented by ⊗ 𝑏̃ 1 𝑔𝑖𝑗 , ⊗ 𝑏̃ 2 𝑔𝑖𝑗 , and ⊗ 𝑏̃ 3 𝑔𝑖𝑗 . The above matrices are normalized and the normalized matrices are represented as; ⊗ 𝑎̇ 𝑖𝑗 , ⊗ 𝑏̇ 𝑓 𝑔𝑖𝑗 , and ⊗ 𝑐̇ ℎ𝑖𝑗 . The total normalized matrices are calculated for grey value whitenization and are represented as; 𝑎𝑖𝑗 , 𝑏𝑓 𝑔𝑖𝑗 , and 𝑐ℎ𝑖𝑗 , respectively. The values of crisp relation matrices obtained in succession are respectively represented as; 𝑎∗ 𝑖𝑗 , 𝑏𝑓 ∗ 𝑔𝑖𝑗 , and 𝑐 ∗ ℎ𝑖𝑗 . Also, these matrices are named as; 𝐴∗ , 𝐵𝑔𝑓 ∗ , and 𝐶ℎ∗ , respectively. The above matrices are to be converted into input matrices for AHP to obtaining their respective weightings, which are represented as; 𝐴̇ ∗ , 𝐵̇ 𝑔𝑓 ∗ , and 𝐶̇ ℎ∗ , respectively. The principal normalized Eigen values (PNEV) calculated for the above matrices are indicated as; 𝑒1 , 𝑒𝑔2 , 𝑒𝑔3 , 𝑒𝑔4 , 𝑒𝑔5 . From the above values, I have constructed the ANP decision matrices, called as the super matrices that are indï 𝐵, ̈ and 𝐶, ̈ respectively. These matrices constitute the Layer1 cated by 𝐴, of the model and the ANP super matrices of the Layer 2 are indicated ̈ And, 𝐵 ∗∗ , and 𝐶 ∗∗ can represent the AHP input matrices of Layer by 𝐷. 𝑔 ℎ 2. The detailed step by step procedure of the proposed methodology is elaborated as follows: Step 1: Identify the supply chain risks and the resilient strategies Identify the possible supply chain risks and resilient strategies for supply chain risk mitigation with specific focus on electronic manufacturing industry. Let the number of supply chain risks identified be ‘m’ and the resilient strategies for risk mitigation to be ‘n’. There are ‘l’ 4

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Engineering Applications of Artificial Intelligence 87 (2020) 103338 Table 1 Categories of supply chain risks and their relevant literature. Risk clusters

Sl No.

Supply chain risk categories

Ref. codes

Cluster relation

Relevant literature Remarks

SCR 1

Forecast

FRCT

Forecast errors could extend the lead times of delivery

Gaudenzi and Risks arising from inaccurate Borghesi (2006) and forecasts due to seasonality, Chopra and Sodhi product variety and shorter (2012) lifecycles come under forecast risks.

SCR 2

Delays

DLYS

Lead time of deliverables Braunscheidel and High capacity utilizations or poor extends beyond Suresh (2009) and yield of the firm results in expectations Chopra and Sodhi deferred deliverables, constituting (2012) delay risks.

SCR 3

Receivables

RCBS

Lead time of receivables extends beyond expectations

Tsai (2008) and Chopra and Sodhi (2012)

SCR 4

Supply chain design

SCDN

Design inflexibilities of products

Christopher and Lee Design risks arise when (2004) and Klibi manufacturer fails to incorporate et al. (2010) design changes or to adapt with the current market situations.

SCR 5

Capacity

CPTY

Capacity inflexibilities or misutilisations

Underutilization or overutilization Cucchiella and Gastaldi (2006) and of capacities results in poor responses towards changing Tang and demands that are classified under Nurmaya Musa capacity risks. (2011)

SCR 6

Inventory

INRY

Inflexibilities in product strategies

Cachon (2004) and Risks arising from too high or too low inventories can be Tang and Zimmerman (2013) categorized under inventory risks. Excess inventory for products with high value or short lifecycle increases the inventory holding costs.

SCR 7

Contingencies

CNGS

Managed through flexible - Fischhoff et al. supply strategies, process (2006) and Tomlin (2006) strategies, product strategies and pricing strategies

SCR 8

Technology

TCGY

Inappropriate technology integrations

Tomlin (2006) and When the manufacturer uses Chopra and Sodhi obsolete technologies or fails to integrate technological changes, (2012) customer dissatisfaction or unfulfilled demand occurs, constituting technology risks.

SCR 9

System

SYTM

Incongruous system integrations

Harland et al. (2003) and Tang and Nurmaya Musa (2011)

SCR 10

Procurement

PRCT

Sourcing risks related with Giunipero and supply cost, supply Eltantawy (2004) commitment and supply and Xu (2010) continuity

SCR 11

Continuity

CNTY

Transparency of supply Craighead et al. Continuity in supply chains is one chain is waning as logistic (2007) and Tuncel of the greatest challenges processes are outsourced and Alpan (2010) associated with logistics outsourcing, as there are chances of forming partnership relations among operators.

SCR 12

IPR

IPRR

Outsourcing or off-shoring Finch (2004) and makes it difficult to Holweg et al. protect IP (2011)

Lead time related risks

Flexibility related risks

Integration related risks

Sourcing related risks

supply chain analysts chosen for rating the importance relations among supply chain risks and resilient strategies.

Risks arising from delayed receivables from the customers due poor financial strength of the customers or having too fewer number of customer accounts contribute to receivable risks.

Occurrence of events like natural disaster, labor disputes, war and terrorism are rare and unpredictable but has a very high impact over the supply chain. These risks of disruptions can be categorized under contingencies.

Risks arising from system integrations, excessive networking, e- commerce applications or even information breakdowns can be included under system risks. Unanticipated increase in acquisition costs due to exchange rate fluctuations, single sourcing and changing term contracts are categorized as procurement risks.

Increased vertical integration, amplified global outsourcing and having world-wide markets results in severe risks related to intellectual property rights.

Identified risks need to be classified into clusters in order to bring together analogous supply chain risks in consideration of its occurrence

Step 2: Classify the supply chain risks in clusters 5

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Engineering Applications of Artificial Intelligence 87 (2020) 103338 Table 2 Resilient supply chain strategies for risk mitigation. Sl No.

Resilient supply chain strategies

RSCS 1

Ref. codes

Relevant literature

Remarks

Fine-tune supply chain FTSD design

Chiang et al. (2003) and Qi et al. (2010)

Supply chain design can be modified or adjusted in such a way keeping redundant paths throughout and by reducing the length of supply chains.

RSCS 2

Supply base strengthening

SBSG

Sturgeon and Lester (2004) and Nam et al. (2011)

Increasing the number of alternative suppliers make the supply chains resilient and thus enable the companies to shift order quantities across suppliers.

RSCS 3

Supply contract flexibility

SCFB

Milner and Rosenblatt (2002) Introducing flexibility in binding contracts enable the and Cheng et al. (2011) supply chain to shift order quantities across time.

RSCS 4

Dynamic assortment planning

DAPL

Kök et al. (2009) and Sauré and Zeevi (2013)

RSCS 5

Centralize demand

CTDM

Blanchard and Kiyotaki (1987) The unpredictability in demand under high forecast and Hein and Tarassow (2010) risks can be better managed by aggregating the demand.

RSCS 6

Capacity flexibility

CPFB

Hall and Liu (2010) and Chen Resilience can be enhanced by using decentralized et al. (2013) capacity for products with predictable demands and centralized capacity for products with unpredictable demands.

RSCS 7

Process standardization PSDN

Ma et al. (2011) and Costinot Process as well as products can be standardized to et al. (2013) reduce the inventory levels and to facilitate interchangeable assemblies.

RSCS 8

Agile operations

AOPN

Charles et al. (2010) and Gligor et al. (2013)

Agility can be imparted through flexible operations, which aids in delivering new products with shorter lead times.

RSCS 9

Manufacturing flexibility

MFFB

Stavrulaki and Davis (2010) and Liao et al. (2010)

Flexible manufacturing systems can be adopted to facilitate parallel operations to reduce process time and to increase resilience.

RSCS 10

Risk hedging

RHDG

Scholes (1996) and Corbett et al. (2012)

Collaborative working with partners increases trust among them and upsurges opportunities for risk hedging.

RSCS 11

Enhance visibility

EVBY

Barlas and Gunduz (2011) and Information distortions can be reduced by increasing Yao and Zhu (2012) the visibility of capacity and inventory so that the demand exaggerations during product shortages can be eluded.

RSCS 12

Cross-training of employees

CTEM

Aksin et al. (2010) and Closs et al. (2011)

Employees for one job are trained to perform other jobs too. This increases flexibility of labor to evade bottlenecks.

RSCS 13

Product flexibility via postponement

PFPT

Yang and Yang (2010) and Choi et al. (2012)

Product flexibility can be achieved through delayed differentiation. This helps in rapid incorporation of configuration changes for products

RSCS 14

Vindicate product ranges

VPRG

Christopher and Holweg (2011) and de Leeuw et al. (2013)

By rationalizing, products with higher risks can be avoided. The exposure to risk can be reduced by the proper choice of products

RSCS 15

Inventory flexibility

INFB

Kemahlıoğlu-Ziya and Bartholdi (2011) and Sarker (2014)

Decentralize inventory for products with stable demand and centralize inventory for products with uncertain demands.

RSCS 16

Logistics flexibility

LGFB

Atasoy (2013) and Bruzzone and Longo (2014)

Implementing flexibility in routing increases responsiveness of products by changing the modes of transportation quickly

RSCS 17

Dynamic revenue management

DRMT

Jerath et al. (2010) and Meissner and Strauss (2012)

Demand and revenue management capability of the company can be enhanced by dynamically influencing the customer product selection

RSCS 18

Silent product rollover

SPRL

Barroso et al. (2011) and Billington et al. (2012)

Product rollover increases the control over product exposure to enhance the capability of managing supply and demand

RSCS 19

Proliferate customer accounts

PCAC

Pine et al. (2010) and Hofmann and Kotzab (2010)

Financial risks arising of delayed receivables from customers can be reduced via increasing the number of customer accounts

RSCS 20

Responsive pricing strategies

RPST

Chiu et al. (2011) and Ha et al. (2011)

Adopting responsive pricing strategies enable the company to swing production quantities across different products to reduce risks related with unstable pricing

RSCS 21

Using insurance

UINS

Lin et al. (2010) and Schmitt Insurance is the way of dealing with risks, by trying and Singh (2012) to avoid its effects. This helps to cover the direct loss or damages to any tangible items

6

Assortment planning allows the firm to make faster responses over the demands of different products through increased control.

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Engineering Applications of Artificial Intelligence 87 (2020) 103338

Fig. 1. Flow chart of the proposed methodology for selection of resilient sc strategies.

(i) Rating the level of importance of supply chain risk (SCR) ‘i’ over ‘j’ on linguistic scales varying from ELI to EHI. (ii) Rating the level of importance of resilient supply chain strategy (RSCS) ‘i’ over ‘j’ for each SCR, ‘g’ (iii) Rating the level of importance of SCR ‘i’ over ‘j’ considering each RSCS, ‘h’.

and mitigating mechanisms. This also helps in ascertaining the effects of resilient strategy over a typical cluster of risks. Step 3: Compute the initial relation matrices Each supply chain analyst ‘k’ is assigned the task of evaluating the importance relation among supply chain risk, as well as the importance relations among resilient strategies for risk mitigation on a linguistic scale varying from ELI, VLI, LI, MLI, MI, MHI, HI, VHI, EHI representing ‘‘extremely low importance’’, ‘‘very low importance’’, ‘‘low importance’’, ‘‘moderately low importance’’, ‘‘medium importance’’, ‘‘moderately high importance’’, ‘‘high importance’’, ‘‘very high importance’’, and ‘‘extremely high importance’’ respectively. Three sets of relationship matrices were developed as,

Step 4: Compute the grey relation matrices The initial set of relationship matrices were converted to the corresponding grey relation matrices using the conversion scales shown in Table 3. Three sets of grey relation matrices were developed correspondingly as, 7

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Engineering Applications of Artificial Intelligence 87 (2020) 103338

the grey number ⊗ 𝑏̃ 𝑓 𝑔𝑖𝑗 ; 1≤ f ≤ 3, and ⊗ 𝑐̇ ℎ𝑖𝑗 represents the normalized lower limit value of the grey number ⊗ 𝑐̃ℎ𝑖𝑗 ( ) ⊗ 𝑎̇ 𝑖𝑗 = ⊗ 𝑎̃𝑖𝑗 −𝑚𝑖𝑛 ⊗ 𝑎̃𝑖𝑗 ∕𝛥𝑚𝑎𝑥 (10) 𝑗 𝑚𝑖𝑛 ( ) 𝑚𝑎𝑥 𝑓 ̃𝑓 ⊗ 𝑏̇ 𝑓 𝑔𝑖𝑗 = ⊗ 𝑏̃ 𝑓 𝑔𝑖𝑗 −𝑚𝑖𝑛 (11) 𝑞 ⊗ 𝑏 𝑔𝑖𝑗 ∕𝜃 𝑚𝑖𝑛 ( ) 𝑚𝑖𝑛 𝑚𝑎𝑥 ⊗ 𝑐̇ ℎ𝑖𝑗 = ⊗ 𝑐̃ℎ𝑖𝑗 −𝑞 ⊗ 𝑐̃ℎ𝑖𝑗 ∕𝛽𝑚𝑖𝑛 (12)

(i) [⊗ 𝑎𝑘𝑖𝑗 ]: represents the importance of SCR ‘i’ over ‘j’ rated by the 𝑘𝑡ℎ analyst; where 1 ≤ i ≤ m; 1 ≤ j ≤ m; 1 ≤ k ≤ l. (ii) [⊗ 𝑏𝑘𝑔𝑖𝑗 ]: represents the importance of RSCS ‘i’ over ‘j’ for each SCR, ‘g’, rated by the 𝑘𝑡ℎ analyst; where 1 ≤ i ≤ n; 1 ≤ j ≤ n; 1 ≤ k ≤ l; 1 ≤ g ≤ m. 𝑘 ]: represents the importance of SCR ‘i’ over ‘j’ for each RSCS, (iii) [⊗ 𝑐ℎ𝑖𝑗 ‘h’, rated by the 𝑘𝑡ℎ analyst; where 1 ≤ i ≤ m; 1 ≤ j ≤ m; 1 ≤ k ≤ l; 1 ≤ h ≤ n. The grey numbers specify an upper and a lower range of values respectively, i.e. ) ( (1) ⊗ 𝑎𝑘𝑖𝑗 = ⊗ 𝑎𝑘𝑖𝑗 , ⊗ 𝑎𝑘𝑖𝑗 ( ) ⊗ 𝑏𝑘𝑔𝑖𝑗 = ⊗ 𝑏𝑘𝑔𝑖𝑗 , ⊗ 𝑏𝑘𝑔𝑖𝑗 (2) ) ( 𝑘 𝑘 𝑘 (3) ⊗ 𝑐ℎ𝑖𝑗 = ⊗ 𝑐ℎ𝑖𝑗 , ⊗ 𝑐ℎ𝑖𝑗

where ⊗ 𝑎̇ 𝑖𝑗 represents the normalized upper limit value of the grey number ⊗ 𝑎̃𝑖𝑗 , ⊗ 𝑏̇ 𝑓 𝑔𝑖𝑗 represents the normalized upper limit value of the grey number ⊗ 𝑏̃ 𝑓 𝑔𝑖𝑗 ; 1≤ f ≤ 3, and ⊗ 𝑐̇ ℎ𝑖𝑗 represents the normalized upper limit value of the grey number ⊗ 𝑐̃ℎ𝑖𝑗

⊗ 𝑎̃𝑖𝑗 =

𝑙 (∑

⊗ 𝑏̃ 𝑔𝑖𝑗 =

⊗ 𝑐̃ℎ𝑖𝑗

𝑘 𝑘 ⊗ 𝑎𝑖𝑗

⎛ =⎜ ⎜ ⎝

𝑘

∑ ,

⊗ 𝑏𝑘𝑔𝑖𝑗 𝑙

∑

𝑘

𝑘 ⊗ 𝑐ℎ𝑖𝑗

𝑙

𝑘 𝑘 ⊗ 𝑎𝑖𝑗

∑ , ∑

,

𝑘

⊗ 𝑏𝑘𝑔𝑖𝑗

𝑘

(5)

(6)

(16)

(17)

(18)

(iii) Computing the final crisp values ( ( )) 𝑎∗ 𝑖𝑗 = 𝑚𝑖𝑛 ⊗ 𝑎̃𝑖𝑗 + 𝑎𝑖𝑗 × 𝛥𝑚𝑎𝑥 𝑚𝑖𝑛 ( ( )) 𝑚𝑎𝑥 𝑏𝑓 ∗ 𝑔𝑖𝑗 = 𝑚𝑖𝑛 ⊗ 𝑏̃ 𝑓 𝑔𝑖𝑗 + 𝑏𝑓𝑔𝑖𝑗 × 𝜃 𝑓 𝑚𝑖𝑛 ( ( )) 𝑚𝑎𝑥 𝑐 ∗ ℎ𝑖𝑗 = 𝑚𝑖𝑛 ⊗ 𝑐̃ℎ𝑖𝑗 + 𝑐ℎ𝑖𝑗 × 𝛽𝑚𝑖𝑛 [ ] 𝐴∗ = 𝑎∗ 𝑖𝑗 [ ] 𝐵𝑔𝑓 ∗ = 𝑏𝑓 ∗ 𝑔𝑖𝑗 [ ] and 𝐶ℎ∗ = 𝑐 ∗ ℎ𝑖𝑗

Step 6: Dividing the average grey relation matrices for resilient strategies When the number of alternatives (RSCS) become large, the size of matrix, [⊗ 𝑏̃ 𝑔𝑖𝑗 ] become computationally intensive. In order to limit unnecessary computations and also, the aim of the model being the selection of best RSCS, the model has been simplified into a two layer model. The first layer considers the construction and evaluation of three super matrices for obtaining best two alternatives in each case, say RSCS-A&B, RSCS-C&D and RSCS-E&F, followed by the construction and evaluation of super matrix using selected alternatives in the second layer. The matrices, [⊗ 𝑏̃ 𝑔𝑖𝑗 ] were divided into three sets of matri[ ] ces, [⊗ 𝑏̃ 1 𝑔𝑖𝑗 ], [⊗ 𝑏̃ 2 𝑔𝑖𝑗 ], ⊗ 𝑏̃ 3 𝑔𝑖𝑗 each consisting of ( 𝑛 × 𝑛 ) dimension 3

(15)

(ii) Computing total normalized crisp values ( )) ( ) ( ⎛ ⎞ + ⊗ 𝑎̇ 𝑖𝑗 × ⊗ 𝑎̇ 𝑖𝑗 ⎟ ⎜ ⊗ 𝑎̇ 𝑖𝑗 1 − ⊗ 𝑎̇ 𝑖𝑗 𝑎𝑖𝑗 = ⎜ ( ) ⎟ ⎜ ⎟ 1 − ⊗ 𝑎̇ 𝑖𝑗 + ⊗ 𝑎̇ 𝑖𝑗 ⎝ ⎠ ( )) ( ) ( ⎞ ⎛ + ⊗ 𝑏̇ 𝑓 𝑔𝑖𝑗 × ⊗ 𝑏̇ 𝑓 𝑔𝑖𝑗 ⎟ ⊗ 𝑏̇ 𝑓 𝑔𝑖𝑗 1 − ⊗ 𝑏̇ 𝑓 𝑔𝑖𝑗 ⎜ 𝑓 𝑏𝑔𝑖𝑗 = ⎜ ( ) ⎟ ⎟ ⎜ 1 − ⊗ 𝑏̇ 𝑓 𝑔𝑖𝑗 + ⊗ 𝑏̇ 𝑓 𝑔𝑖𝑗 ⎠ ⎝ ( ( )) ( ) ⎛ ⎞ + ⊗ 𝑐̇ ℎ𝑖𝑗 × ⊗ 𝑐̇ ℎ𝑖𝑗 ⎟ ⎜ ⊗ 𝑐̇ ℎ𝑖𝑗 1 − ⊗ 𝑐̇ ℎ𝑖𝑗 𝑐ℎ𝑖𝑗 = ⎜ ( ) ⎟ ⎜ ⎟ 1 − ⊗ 𝑐̇ ℎ𝑖𝑗 + ⊗ 𝑐̇ ℎ𝑖𝑗 ⎝ ⎠

)

𝑘 ⎞ ⊗ 𝑐ℎ𝑖𝑗 ⎟ ⎟ 𝑙 ⎠

𝑗

𝑗

(4)

𝑙

(14)

⊗ 𝑏̃ 𝑓 𝑔𝑖𝑗 = 𝑚𝑎𝑥 ⊗ 𝑏̃ 𝑓 𝑔𝑖𝑗 −𝑚𝑖𝑛 𝑗

𝑚𝑎𝑥 𝛽𝑚𝑖𝑛 = 𝑚𝑎𝑥 𝑐̃ℎ𝑖𝑗 −𝑚𝑖𝑛 ⊗ 𝑐̃ℎ𝑖𝑗 𝑗

)

𝑙

(13)

𝑚𝑎𝑥 𝜃 𝑓 𝑚𝑖𝑛

𝑗

Step 5: Compute the average grey relation matrices ] ] [ ] [ [ The average grey relation matrices ⊗ 𝑎̃𝑖𝑗 , ⊗ 𝑏̃ 𝑔𝑖𝑗 𝑎𝑛𝑑 ⊗ 𝑐̃ℎ𝑖𝑗 can [ ] be constructed from 3 × l grey relation matrices respectively, ⊗ 𝑎𝑘𝑖𝑗 , [ ] [ ] 𝑘 ; k = 1 – l as, ⊗ 𝑏𝑘𝑔𝑖𝑗 and ⊗ 𝑐ℎ𝑖𝑗 (∑

𝑚𝑖𝑛 ⊗ 𝑎̃𝑖𝑗 𝛥𝑚𝑎𝑥 𝑚𝑖𝑛 = 𝑚𝑎𝑥 ⊗ 𝑎̃𝑖𝑗 −𝑗

(19) (20) (21) (22) (23) (24)

Step 8: Convert into Analytic Hierarchy Process (AHP) input matrices The crisp relation matrices were converted into AHP input matrices by using a conversion scale for the values as shown in Table 4. The values were so ranged to make the pair-wise comparisons in the same way as in AHP, where the relative importance of one factor over the other varies within a range of ‘9’ to ‘1/9’ (Sabu et al., 2018). The matrices were converted into AHP input matrices shown correspondingly as,

3

matrices as, (i) [⊗ 𝑏̃ 1 𝑔𝑖𝑗 ] consists of importance relations among RSCS ∀ 1 ≤ i ≤ n/3; 1 ≤ j ≤ n/3; 1 ≤ g ≤ m, (ii) [⊗ 𝑏̃ 2 𝑔𝑖𝑗 ] consists of importance relations among RSCS ∀ n/3 < i ≤ 2n/3; n/3 < j ≤ 2n/3; 1 ≤ g ≤ m, (iii) [⊗ 𝑏̃ 3 𝑔𝑖𝑗 ] consists of importance relations among RSCS ∀ 2n/3 < i ≤ n; 2n/3 < j ≤ n; 1 ≤ g ≤ m. These matrices, [⊗ 𝑏̃ 1 𝑔𝑖𝑗 ], [⊗ 𝑏̃ 2 𝑔𝑖𝑗 ] and [⊗ 𝑏̃ 3 𝑔𝑖𝑗 ] in general can be represented for convenience as, [⊗ 𝑏̃ 𝑓 𝑔𝑖𝑗 ]; where 1 ≤ f ≤ 3. Step 7: Compute the crisp relation matrices from the average grey relation matrices The grey values can be converted into crisp values by modifiedCFCS method by a three step procedure elaborated as follows; (i) Normalization of the grey values ( ) ⊗ 𝑎̇ 𝑖𝑗 = ⊗ 𝑎̃𝑖𝑗 −𝑚𝑖𝑛 ⊗ 𝑎̃𝑖𝑗 ∕𝛥𝑚𝑎𝑥 (7) 𝑗 𝑚𝑖𝑛 ( ) 𝑚𝑎𝑥 ⊗ 𝑏̃ 𝑓 𝑔𝑖𝑗 ∕𝜃 𝑓 𝑚𝑖𝑛 (8) ⊗ 𝑏̇ 𝑓 𝑔𝑖𝑗 = ⊗ 𝑏̃ 𝑓 𝑔𝑖𝑗 −𝑚𝑖𝑛 𝑗 ( ) 𝑚𝑖𝑛 𝑚𝑎𝑥 ⊗ 𝑐̇ ℎ𝑖𝑗 = ⊗ 𝑐̃ℎ𝑖𝑗 −𝑗 ⊗ 𝑐̃ℎ𝑖𝑗 ∕𝛽𝑚𝑖𝑛 (9)

𝐴∗ → 𝐴̇ ∗

(25)

𝐵𝑔𝑓 ∗ → 𝐵̇ 𝑔𝑓 ∗ ; 1 ≤ 𝑓 ≤ 3; 1 ≤ 𝑔 ≤ 𝑚

(26)

𝐶ℎ∗

(27)

→

𝐶̇ ℎ∗ ; 1 ≤ ℎ ≤ 𝑛

Step 9: Calculating the Principal Normalized Eigen Vectors (PNEVs) Set of principal normalized Eigen vectors for the matrices (𝐴∗ , 𝐵𝑔𝑓 ∗ , ∗ 𝐶ℎ ) are to be calculated, where 1 ≤ h ≤ n; 1 ≤ g ≤ m; 1 ≤ f ≤ 3. The corresponding PNEVs for matrices are shown as, (i) 𝑒1 represents the PNEV value corresponding to 𝐴̇ ∗ (ii) 𝑒𝑔 represents the set of PNEV values corresponding to 𝐵̇ 1∗ ∀ 1 ≤ g 2

𝑔

≤m (iii) 𝑒𝑔3 represents the set of PNEV values corresponding to 𝐵̇ 𝑔2∗ ∀ 1 ≤ g ≤m

where, ⊗ 𝑎̇ 𝑖𝑗 represents the normalized lower limit value of the grey number ⊗ 𝑎̃𝑖𝑗 , ⊗ 𝑏̇ 𝑓 𝑔𝑖𝑗 represents the normalized lower limit value of 8

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Engineering Applications of Artificial Intelligence 87 (2020) 103338

(iv) 𝑒𝑔4 represents the set of PNEV values corresponding to 𝐵̇ 𝑔3∗ ∀ 1 ≤ g ≤m (v) 𝑒ℎ5 represents the set of PNEV values corresponding to 𝐶̇ ℎ∗ ∀ 1 ≤ h ≤n The values are obtained by keeping a Consistency Ratio (CR) less than 0.1 (Saaty, 1996).

Table 3 Linguistic assessment and the associated grey values.

Step 10: Construct the ANP super matrix The ANP super matrix, 𝐴̈ is constructed based on the values obtained from step 9, is as shown in Table 5. 𝐴̈ comprises of PNEV values, 𝑒1 , 𝑒𝑔2 and 𝑒ℎ5 . Similarly ANP super matrices, 𝐵̈ and 𝐶̈ are to be constructed based on PNEV values, (𝑒1 , 𝑒𝑔3 and 𝑒ℎ5 ); (𝑒1 , 𝑒𝑔4 and 𝑒ℎ5 ), respectively.

Linguistic assessment

Associated grey values

Extremely low importance (ELI) Very low importance (VLI) Low importance (LI) Moderately low importance (MLI) Medium importance (MI) Moderately high importance (MHI) High importance (HI) Very high importance (VHI) Extremely high importance (EHI)

[0.0, [0.1, [0.2, [0.3, [0.4, [0.5, [0.6, [0.7, [0.8,

0.2] 0.3] 0.4] 0.5] 0.6] 0.7] 0.8] 0.9] 1.0]

Table 4 Conversion scales from final crisp to AHP input values.

Step 11: Solve and rank resilient strategies The resilient strategies were ranked by raising the super matrix to the power of (𝑘 − 1), where 𝑘 is an arbitrary number and the best of the ̈ 𝐵̈ and 𝐶. ̈ Let the two resilient strategies were taken from each sets of 𝐴, resilient strategies, say RSCS (A) and RSCS (B) be selected by solving ̈ the resilient strategies, say RSCS (C) and RSCS (D) be selected by 𝐴, solving 𝐵̈ and the resilient strategies, say RSCS (E) and RSCS (F) be ̈ respectively. selected by solving 𝐶, Step 12: Compute AHP input matrices for Layer 2 The AHP input matrices for Layer 2 were constructed by considering the selected resilient strategies from Layer 1; RSCS (A), RSCS (B), RSCS (C), RSCS (D), RSCS (E) and RSCS (F) and the considered ‘m’ supply chain risks. 𝐵𝑔∗∗ and 𝐶ℎ∗∗ were constructed by pair-wise comparisons over a range of 9 to 1/9, same as in Layer 1, where 1 ≤ g ≤ m; 1 ≤ h ≤ 6. Step 13: Compute the ANP super matrix of Layer 2 Matrix, 𝐴̇ ∗ obtained in step 8 along with 𝐵𝑔∗∗ and 𝐶ℎ∗∗ are used as inputs for the ANP super matrix of Layer 2 by calculating their PNEV values, 𝑒1 , 𝑒𝑔6 and 𝑒ℎ7 ; respectively, where 1 ≤ g ≤ m; 1 ≤ h ≤ 6. The ANP super matrix of Layer 2, 𝐷̈ is constructed alike as the construction of the super matrix, 𝐴̈ as in Layer 1.

Crisp value ranges

AHP input conversion

(0.00, (0.10, (0.15, (0.20, (0.25, (0.30, (0.35, (0.40, (0.45, (0.55, (0.60, (0.65, (0.70, (0.75, (0.80, (0.85, (0.90,

1/9 1/8 1/7 1/6 1/5 1/4 1/3 1/2 1 2 3 4 5 6 7 8 9

0.10] 0.15] 0.20] 0.25] 0.30] 0.35] 0.40] 0.45] 0.55] 0.60] 0.65] 0.70] 0.75] 0.80] 0.85] 0.90] 1.00)

international offices at Hong Kong, Dubai, USA and Nepal. The global supply chain network of PQR is shown in Fig. 2. The following methodology was incorporated to have a qualitative and quantitative analysis of various strategies for building resilience in their supply chains. A selection model was considered with resilient strategies as the alternatives and supply chain risks as the factors, by checking how effective those strategies are in mitigating the individual risks? From the constructed ANP selection model as shown in Fig. 3, it can be seen that the selection problem is practically a tedious task requiring large number of pairwise comparisons among variables. The methodology described in Section 4 is practically implemented for the case supply chain of PQR in a step by step procedure as follows:

Step 14: Selection of the best resilient strategy By raising the super matrix to the power of (𝑘 − 1), where 𝑘 is ̈ the six selected strategies are ranked on its an arbitrary number, 𝐷, importance based on their supply chain risk reduction capabilities. This helps managers in identifying the best RSCS. The strategies having more influence on risk mitigation environment should be implemented and practiced to improve supply chain resilience.

Step 1: Twelve supply chain risks and twenty one resilient strategies for risk mitigation were identified with specific focus to electronic manufacturing industry. Three supply chain analysts, experts in the field of supply chain resilience were chosen for rating the importance relations among supply chain risks and risk mitigation strategies. The identified supply chain risks and the resilient strategies for risk mitigation are shown in Tables 1 and 2 respectively.

5. Illustration of proposed model in a case company A representative case study of the proposed methodology for testing the impact of mitigation strategies was carried out in an electronic manufacturing company ‘PQR’ in India, who are global manufacturers of electronic gadgets, particularly mobiles and tablets. The supply chain of PQR is primarily targeted in improving the satisfaction levels of customers with assured services available. Having a global manufacturing network intensifies the risks and chances of vulnerability of its supply chain. All products in range are manufactured by ensuring strict social and environmental standards. Also, all of the suppliers are expected to keep the same standards in manufacturing their supply components. The products of PQR are having wide global markets across Bangladesh, Nepal, Srilanka, Maldives, UAE, KSA, Kuwait, Qatar, Oman, Afghanistan, Brazil and South Africa. Their supply chain presently occupies the global market with nearly two percent of the total mobile sales in the world. After gaining a significant market in urban areas, in recent years PQR is aiming to hit the rural markets with efficient and responsive products. Their market strategy of ‘‘sell deep and sell more’’ has been widely popularized and they have always concentrated on the development of their core productive capabilities. They have a major manufacturing and assembly plant in India with

Step 2: The risks identified and classified into four clusters are shown in Table 6. Step 3: Three sets of initial relationship matrices were constructed as per the particulars shown under step 3 in Section 4; on linguistic scales varying from ELI to EHI. Step 4: Three sets of grey relation matrices were developed as per conversions shown in Table 3. The matrix, [⊗ 𝑎𝑘𝑖𝑗 ] (upper triangular); k = 1, obtained is presented in Table 7. Similarly 12 × 3 (36) grey relationship 𝑘 ] matrices, [⊗ 𝑏𝑘𝑔𝑖𝑗 ] and 21 × 3 (63) grey relationship matrices, [⊗ 𝑐ℎ𝑖𝑗 were constructed based on Eqs. (1)–(3).

[ ] [ ] [ ] Step 5: The average grey relation matrices, ⊗ 𝑎̃𝑖𝑗 , ⊗ 𝑏̃ 𝑔𝑖𝑗 𝑎𝑛𝑑 ⊗ 𝑐̃ℎ𝑖𝑗 were constructed [ ] [from 3] × 3 [(9) sets] of grey relation matrices respec𝑘 . The average grey relation matrix tively as, ⊗ 𝑎𝑘𝑖𝑗 , ⊗ 𝑏𝑘𝑔𝑖𝑗 and ⊗ 𝑐ℎ𝑖𝑗 9

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Engineering Applications of Artificial Intelligence 87 (2020) 103338

Fig. 2. Global supply chain network of illustrative case company PQR.

Fig. 3. ANP selection model considering various supply chain risks and resilient strategies for risk mitigation.

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Engineering Applications of Artificial Intelligence 87 (2020) 103338 Table 5 ̈ Construction of ANP super matrix of Layer 1, 𝐴.

] [ is]shown in Table 8. Similarly 12 constructed, ⊗ 𝑎̃𝑖𝑗 (upper triangular) [

Table 6 Supply chain risks categorized in clusters.

⊗ 𝑏𝑘𝑔𝑖𝑗

and 21 average grey relation average grey relation matrices, [ ] matrices, ⊗ 𝑐̃ℎ𝑖𝑗 were developed by following Eqs. (4)–(6).

Step 6: As there are twenty one alternatives to be evaluated, the dimension of matrix, [⊗ 𝑏̃ 𝑔𝑖𝑗 ] becomes 21 × 21 and( there are) 12 such matrices to be computed with each matrix needing

(21×21)−21 2

i.e., 210

paired comparisons. Hence, the total number of paired comparisons will be 210 × 12 (2520) for the set of matrices [⊗ 𝑏̃ 𝑔𝑖𝑗 ] alone, which is practically a tedious job. In order to simplify the problem, the average grey relation matrices, [⊗ 𝑏̃ 𝑔𝑖𝑗 ] were divided into three sets of matrices, [⊗ 𝑏̃ 𝑓 𝑔𝑖𝑗 ]; 1 ≤ f ≤ 3, each consisting of 7 × 7 dimension so the (( matrices, ) ) number of paired comparisons can be reduced to

(7×7)−7 2

Sl. No. Lead time related risks

Flexibility related risks

Integration related risks

Sourcing related risks

1 2 3 4

SCDN CPTY INRY CNGS

TCGY SYTM

PRCT CNTY IPRR

FRCT DLYS RCBS

the six strategies to be selected for the case study in their order of preference by implementing the methodology were ‘‘risk hedging’’ >‘‘using insurance’’ > ‘‘fine-tune supply chain design’’ >‘‘supply contract flexibility’’ > ‘‘enhance visibility’’ > ‘‘inventory flexibility’’.

× 12 × 3

i.e., 756. The selection model was also modified accordingly in order to practically simplify the model into a two layer network, which is shown in Fig. 4.

6. Results and discussion This research has attempted to quantify the effectiveness of various mitigation approaches on a practical perspective. Mitigation approaches, based on its effectiveness were sorted using combined methodologies of grey theory and layered ANP approaches. This model was also applied in the context of an Indian electronic manufacturing industry. Twelve major SCRs and twenty one RSCSs were considered for the case study. A two layer evaluation model was constructed and solved for the case problem and the most effective resilient strategies were found to be RSCS 10 (risk hedging ) and RSCS 21 (using insurance). For clarity, a digraph showing the overall importance of RSCS in mitigating various SCR, as seen from the importance ratings is plotted in Fig. 5. From the digraph, it is easy for the managers to understand the effects of RSCS over various SCR and it is also possible to easily identify those RSCS having broader effects. The results obtained for the problem strictly depend on the importance ratings given by supply chain analysts. How far the ranking priorities vary with the variations in weightings assigned for supply chain analysts? Do there exist any personal bias in the ranking priorities of resilient strategies for the case supply chain? Sensitivity analysis was conducted to answer these questions. We have assigned highest weightings to the importance ratings given by each of the supply chain analysts (analyst 1, analyst 2 and analyst 3) separately, keeping equal weightings for others. The weightings assigned for supply chain analysts during sensitivity analysis are shown in Table 14. Results of sensitivity analysis reveal that there are no serious changes in the final results obtained earlier. The selection indices obtained for various RSCS in Layer 1, during sensitivity analysis have been compared with that of the actual model to check the possibilities of any biases and is found to be unbiased. This can be perceived from

Step 7: The grey relation matrices were converted into crisp relation matrices using Eqs. (7) to (24). The crisp relation matrix obtained, 𝐴∗ (upper triangular) is shown in Table 9. Similarly the set of crisp relation matrices, 𝐵𝑔𝑓 ∗ and 𝐶ℎ∗ , were constructed. Step 8: The corresponding AHP input matrices were obtained from the crisp relation matrices using Table 4 and Eqs. (25)–(27). The AHP input matrix, 𝐴̇ ∗ (upper triangular) developed is shown in Table 10. Similarly the set of matrices, 𝐵̇ 𝑔𝑓 ∗ and 𝐶̇ ℎ∗ were formulated. Step 9: Set of principal normalized Eigen vectors, 𝑒1 , 𝑒𝑔2 , 𝑒𝑔3 , 𝑒𝑔4 , 𝑒ℎ5 for the matrices (𝐴∗ , 𝐵𝑔𝑓 ∗ , 𝐶ℎ∗ ) were calculated by keeping a Consistency Ratio (CR) less than 0.1; where 1 ≤ h ≤ 21; 1 ≤ g ≤ 12; 1 ≤ f ≤ 3. Step 10: The ANP super matrix of Layer 1, 𝐴̈ is constructed according to Table 5, which is displayed in Table 11. Also, ANP super matrices of Layer 1, 𝐵̈ and 𝐶̈ were constructed. Step 11: The best of the two resilient strategies each, obtained by ̈ 𝐵̈ and 𝐶̈ are shown in Table 12. solving the super matrices, 𝐴, Step 12: The AHP input matrices for Layer 2 were constructed using the selected resilient strategies in Table 12 and the considered twelve supply chain risks. The set of matrices, 𝐵𝑔∗∗ and 𝐶ℎ∗∗ were formulated, same as in Layer 1, where 1 ≤ g ≤ 12; 1 ≤ h ≤ 6. Step 13: The ANP super matrix of Layer 2,𝐷̈ was framed according to step 13 in Section 4; which is detailed in Table 13. Step 14: The six selected strategies were ranked on its importance based on their supply chain risk reduction capabilities and the best RSCS is identified to be as RSCS 10, ‘‘risk hedging’’. The best of 11

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Fig. 4. Revised model representing the selection problem considering various supply chain risks and resilient strategies for risk mitigation.

Table 7 Grey relation matrix showing relative importance of supply chain risks by sc analyst 1.

FRCT indicates the associated supply chain risk, ‘Forecast risk’, as in Table 1. Similarly, the other elements of table can be read. The level of importance of SCR, i over SCR, j; rated by analyst 1, is represented as grey value, ⊗ 𝑎1𝑖𝑗 .

the sensitivity analysis plot as shown in Fig. 6. Hence, it is evident

In order to validate the model for the case company we have

that there exists no serious bias in the ratings given by supply chain

critically analyzed the following best six RSCS obtained on basis of the

analysts. The best two of the strategies selected in Layer 1; by solving

proposed model and its practical implications of the case.

̈ 𝐵̈ and 𝐶̈ remains the same in all scenario, each super matrices, 𝐴, despite the weightings assigned for analysts. Hence, the Layer 2 remains

Risk hedging: The case company now directly works in collaboration

identical in all cases resulting in the same ranking order for strategies.

with its suppliers and partners, which opens opportunities for risk hedg-

The results of sensitivity analysis and the order of ranking for most

ing in their supply chains. Vendor Managed Inventory (VMI) practiced

effective resilient strategies are shown in Table 14.

with typical component suppliers increases the opportunities of risk 12

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Engineering Applications of Artificial Intelligence 87 (2020) 103338 Table 8 Average grey relation matrix showing relative importance of supply chain risks.

FRCT indicates the associated supply chain risk, ‘Forecast risk’, as in Table 1. Similarly, the other elements of table can be read. The average level of importance of SCR, i over SCR, j is represented as grey value, ⊗ 𝑎̃𝑖𝑗 .

Table 9 Crisp relation matrix showing relative importance of supply chain risks.

FRCT indicates the associated supply chain risk, ‘Forecast risk’, as in Table 1. Similarly, the other elements of table can be read. The crisp value showing level of importance of SCR, i over SCR, j is represented as, 𝑎∗ 𝑖𝑗 .

Table 10 AHP input matrix showing relative importance of supply chain risks.

FRCT indicates the associated supply chain risk, ‘Forecast risk’, as in Table 1. Similarly, the other elements of table can be read. The AHP input value showing level of importance of SCR, i over SCR, j is represented as, 𝑎̇ ∗ 𝑖𝑗 .

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Table 11 ̈ ANP super matrix of Layer 1, 𝐴.

FRCT indicates the associated supply chain risk, ‘Forecast risk’, as in Table 1 and FTSD indicates the resilient supply chain strategy, ‘Fine-tune supply chain design’ as in Table 2, respectively. Similarly, the other elements of the table can be read.

Table 12 Selected resilient strategies from Layer 1.

FTSD indicates the risk mitigation strategy, ‘Fine-tune supply chain design’ as in Table 2. Similarly, the other elements of the table can be read. Selection index represents the values obtained by synthesizing the super matrices of Layer 1.

Table 13 ̈ ANP super matrix of Layer 2, 𝐷.

hedging and reduced vulnerability of their supply chain to a large extend.

and enables them to extend beyond boundaries and practice new product innovations.

Using insurance: All major plants and warehouses of the case company are protected under the cover of multiple insurances, which makes the company feels secure against major disruptive events such as natural disasters. Insurance improves the confidence level of their supply chain

Fine-tune supply chain design: Supply chain designs are frequently tuned by ‘PQR’ to adopt as per new market situations. Increased trends of online marketing and internet sales are utilized by them without 14

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Engineering Applications of Artificial Intelligence 87 (2020) 103338 Table 14 Sensitivity analysis for selection of resilient strategies (Layer 1). Sl No. Scenario 1

1 2 3 4 5 6

Scenario 2

Scenario 3

Selection index Selected strategies (Layer 1)

Selection index Selected strategies (Layer 1)

Selection index Selected strategies (Layer 1)

0.2776 0.1722 0.2153 0.1851 0.2953 0.2723

0.2714 0.1759 0.2754 0.2561 0.2983 0.2677

0.2732 0.17 0.2766 0.2557 0.2866 0.27

FTSD SCFB EVBY RHDG UINS INFB

RSCS RSCS RSCS RSCS RSCS RSCS

A B C D E F

FTSD SCFB EVBY RHDG UINS INFB

RSCS RSCS RSCS RSCS RSCS RSCS

A B C D E F

FTSD SCFB EVBY RHDG UINS INFB

RSCS RSCS RSCS RSCS RSCS RSCS

A B C D E F

For different scenario; 1, 2 and 3, the respective weightings for supply chain analysts are assigned as (0.6, 0.2, 0.2), (0.2, 0.6, 0.2), and (0.2, 0.2, 0.6).

Fig. 5. Digraph representing the importance of RSCS in mitigating various SCR (Relations superior to moderately high importance are plotted).

Fig. 6. Sensitivity analysis plots for RSCS compared with that of the actual obtained model.

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Engineering Applications of Artificial Intelligence 87 (2020) 103338

compromising on the speeds of delivery. Customer delights are ensured with ample services available and even by providing replacement warrantee for high end products.

electronic manufacturing supply chains can be considered as a scope of future research. Risk hedging emerges as the best strategy for resilience for the case supply chain and top level managers are recommended to increase their collaborative working capabilities to improving the level of risk hedging opportunities. Although not being a risk mitigation strategy, using insurance can alleviate the effects of certain risks, when processes go wrong. This is how it deals with the risks by avoiding its’ effects. Thus, insuring risky assets and operations can increase the resilience capabilities of supply chains. (iii) Implications to policy The study also offers several implications to policy makers. Establishing one or several policies to upsurge the effects of the aforementioned resilient strategies for risk mitigation can improve the overall resilience capabilities of supply chains. Some of the policy implications are; (a) Improving the level of collaborative working with partners: improving the information sharing and visibilities in supply chains can improve the collaborative capabilities in supply chains. (b) Improving the level of flexibilities in supply chains: Supply flexibilities can be increased by implementing strategies such as multiple supplier strategies or flexible supply contracts. Process flexibilities can be improved by implementing flexible manufacturing systems, and demand flexibilities can be improved by responsive pricing strategies and postponements. And, (c) Prioritize the resilient strategies for risk mitigation before implementing them.

Supply contract flexibility: Flexibility is imparted to the supply contracts by having collaborative partner relations. The company has built trust among their suppliers in having a collaborative relation through regular orders, on time payments and reduced bank debts during the recent years. Supply contracts were made to use this flexibility for utilizing the inventory and capacity by adopting enhanced information sharing practices. Enhance visibility: Virtuous information sharing practices and collaborative partner relations enhances the visibility of available resources and unforeseen risks associated with their supply chains. The pipeline inventories are more visible, which enable their supply chains to adopt postponement strategies which in turn reduce the cost of capacity and holding costs of inventory. Inventory flexibility: Inventory flexibility is achieved by the use of standard/ modular designs for their products, which are having same part family combinations. For most of the instances, they implement innovations of similar existing products, to catch immediate market attentions and this helps in utilizing the existing work-in-process (WIP) inventories. 7. Research implications

8. Conclusions and scope of future works The present study has several research implications. These implications can be extended towards the theoretical knowledge, practice, and policy of the supply chain decision-making environments. The implications are elaborated as follows; (i) Implications to theoretical knowledge This study proposes many implications to theoretic knowledge of organizations and their supply chains. The main contributions are in consideration of the resource based view of organizations and the complexity theory view of the supply chain. According to resource based view, organizations are sustained by harnessing those resources that are valuable, erratic, inimitable, and non-substitutable. The resources can be in several forms as; assets, capabilities, organizational processes, firm attributes, information and knowledge that enable firm to improving effectiveness, efficiency, and responsiveness. From the study, resilient strategies for risk mitigation can improve the overall flexibility of the system and can properly utilize the organizational resources to achieving effectiveness, efficiency, and responsiveness. Considering a complexity theory perspective into supply chains, any additional element (buffer) added to a supply chain will increase its interactive complexities. Organizations or their supply chains will become less predictable, when the level of interactive complexities increases. In this case, adding capacities and inventories are considered among the resilient strategies for risk mitigation. This is possible by building redundancies to improve flexibilities associated with capacities and inventories. But, these strategies have not emerged amongst the best five resilient strategies to deal with concomitant risks for the case supply chain. This is owing to the fact that improving capacities and inventories will eventually increase the interactive complexities of the system. Hence, some of the associated risks such as capacity risks and inventory risks gradually increase for the system. So considering the complexity theory perspective in supply chains, capacity and inventory flexibilities must be carefully observed according to the needs of the supply chain. (ii) Implications to practice Supply chain managers are recommended to implement the resilient strategies for risk mitigation in accordance with the strategic focus of supply chains. The results obtained are for a case electronic supply chain, and it can be assumed that similar pattern of prominence among resilient strategies for risk mitigation may exist for other electronic supply chains. An empirical study to evidence this, among several

Implementing proactive approaches to identify, measure, mitigate and monitor supply chain risks demands increasing attention in modern supply chains. Expertise practices are needed for dealing with vulnerability and improving the resilience of supply chains. Both qualitative and quantitative analyses of the strategies for risk mitigation are essential for reducing, monitoring and mitigating various supply chain risks. By quantifying the resilient strategies for risk mitigation, supply chain managers could assign weightings for its implementation and practice. The proposed methodology for decision support uses the benefits of grey theory and layered ANP approaches to deal with human judgments to be converted into a set of numerical values which could be used by managers for better decision making processes. This research has a lot of managerial implications as it is possible for the logistics management team to ascertain effective strategies for risk mitigation by simply concentrating on those with higher selection indices. Also, it is possible to take proactive initiatives to tackle supply chain risks by implementing suitable risk mitigation strategies and to impart supply chain resilience. The case study points into many insights of the model. It is seen that, building flexibility in different forms builds resilience in supply chain as a whole. Collaborative working with partners enabled them to utilize their capacity and inventory to a great extent. Insurance also improves confidence level among supply chain partners and enable them to open new product innovations by keeping buoyancy in the market. This has led to the improvement of their visibility, trust and even opens opportunities for risk hedging. There are some limitations of this research as well. The model only considers twelve major risks and twenty one strategies for supply chain resilience typically seen in an electronics manufacturing industry. Supply chain analysts need to have an exhaustive knowledge about the firm, its practices and products for critical evaluation of resilient attributes and strategies. Also, the ratings given by supply chain analysts might have reflected their personal biases towards certain variables. Cluster priorities were not introduced in the model for decision making; which might have further added complexity to the model. Future research could focus on extended applications of the model considering more supply chain risks and resilient strategies for risk mitigation. Also, the study can be extended by considering many of the human/ behavioral attributes contributing to the overall resilience of the supply chain. The model evaluates the mitigation approaches 16

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at a strategic level. The same can be extended to check the resilience practices of the firm at tactical and operational levels by considering major risks and mitigation approaches at that levels. The representative case study has been done in case of an electronic manufacturing industry. The same model can be considered for application in different industries as well with some marginal changes.

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Rajesh is presently working as Assistant Professor (Operations) in ABV-Indian Institute of Information Technology, Gwalior, India. He has completed Ph.D. from the Indian Institute of Space Science and Technology (IIST), Trivandrum, India and Post-doc from the Indian Institute of Technology Madras (IIT-M), India. He is working the areas of decision-making, supply chain risk management, resilience, sustainability, and operations management.

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