Mitigating supply chain disruptions through the assessment of trade-offs among risks, costs and investments in capabilities

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Abstract

One of the central questions in supply chain design is how to properly invest in supply chain capabilities in order to be more responsive to supply chain disruptions. This new perspective in supply chain design requires an understanding of the relationships among costs, supply chain risk drivers, and investments in supply chain capabilities. In this paper, we develop a multi-objective stochastic model for supply chain design under uncertainty and time-dependency. Sources of risk are modeled as a set of scenarios, and the risk of the system is determined. The objective is to examine the trade-offs among investments in improving supply chain capabilities and reducing supply chain risks, and to minimize cost of supply chain disruptions. Due to the NP-hard nature of the problem, a heuristic algorithm based on a relaxation method is designed to determine an optimal or near-optimal solution. To examine the efficiency of the heuristic algorithm, a numerical example is provided. Our findings suggest that increasing supply chain capabilities can be viewed as a mitigation strategy that enables a firm to reduce the total expected cost of a supply chain subject to disruptions.

Introduction

The design of a supply chain that can be efficient while responsive to disruptions is a significantly complex and challenging task (Christopher and Peck, 2004, Ponomarov and Holcomb, 2009, Pettit et al., 2010). Supply chain managers are striving to achieve the goal of fully integrated supply chains that are efficient and competitive, yet responsive to risks and disruptions. This is a daunting task due to the inherent risks in global supply chains, ranging from demand uncertainty to environmental turbulence (Chopra and Sodhi, 2004, Roh et al., 2014). While investment in supply chain capabilities increases the ability of the firm to be more resilient and responsive to supply chain disruptions, it has its own costs (Juttner, 2005, Chopra and Sodhi, 2014). Thus, organizations are faced with the evaluating the cost-benefit of investments in supply chain capabilities to address supply chain risks.

Although a focus on the design of efficient supply chains has helped organizations reduce their costs, it has increased their vulnerability to disruptions (Wright, 2013). Previous studies show that due to economies of scale, firms would be able to minimize their fixed cost through minimizing investment in the number of facilities (Goetschalckx et al., 2013, Huang and Goetschalckx, 2014). Thus, addressing the overall effectiveness of a supply chain requires examining the trade-off between investments in supply chain capabilities and the costs associated with disruptions. This requires a significantly different approach to supply chain design, using a perspective that incorporates the responsiveness and resiliency of a supply chain.

In recent years, academics and practitioners have focused on supply chain risks and the impact of such risks on supply chain design decisions (Blackhurst et al., 2005, Craighead et al., 2007, Elkins et al., 2005, Hendricks and Singhal, 2003, Hendricks and Singhal, 2005, Kleindorfer and Saad, 2005, Rice and Caniato, 2003, Tang, 2006). A great deal of work has focused on evaluating different sources of risk and disruption in supply chains, and how firms can develop mitigation strategies to respond to disruptions. Nevertheless, there is a gap in the literature on the trade-off between increased investment in supply chain capabilities and reduced supply chain risks. Chopra and Sodhi (2014) discuss the importance of development and implementation of risk management plans that reduce risks with limited impact on cost efficiency. While there is some anecdotal evidence on the benefits of implementing risk management plans, the cost-effectiveness of these programs has not been fully examined. To address this gap in the literature, we aim to provide a more holistic assessment of the trade-off between investment in supply chain capabilities and minimizing supply chain risk and cost.

The study makes two contributions to the literature in supply chain risk management. It develops a decision model for supply chain risk management with respect to the tradeoff between the cost associated with supply chain disruptions and the revenue generated as the result of investment in supply chain capability, where supply chain capability as investment in new facilities, products sites, and distribution channels, which are usually regarded as improving redundancy in the supply chain design. The existing studies in supply chain design do not examine the impact of supply chain capability on mitigating supply chain disruptions. Previous studies (e.g. Guille׳n et al. (2005)) provides a decision model for supply chain under uncertainty. However, whether firms would be able to mitigate supply chain disruptions through investment in supply chain capability remains unclear. Chopra and Sodhi (2014) argued that managers usually do not invest in supply chain capabilities because they view these investment as costs. We determine whether decisions to improve supply chain capability through investment in supply chain components such as facility, plant, and distribution channels has a positive impact on mitigating supply chain disruptions and minimizing supply chain cost. Such an approach to supply chain design has important managerial implications since manager would be able to incorporate supply chain risk decision into their supply chain design as part of their supply chain practices. Methodologically, we develop a heuristic algorithm to find the (near) optimal solution due to the NP-hard nature of the model. This algorithm is new and novel, which is used for problems that have binary variables and optimum solution is not always accessible for large scale problems, which is an extension of the method proposed by Narenji et al. (2011).

The remainder of this paper is organized as follows. In the next section, we discuss the importance of supply chain design as a risk mitigation strategy, and examine the scholarly work on supply chain risk management. Later, we introduce a multi-objective supply chain model that incorporates supply chain capability investment, supply chain risks and costs. Then we provide model interpretations and define our heuristic method based on a relaxation and decomposition method. Finally, we discuss the findings of the study, its contribution to the theory and practice of supply chain risk management and directions for future research.

Section snippets

Supply chain design as a risk mitigation strategy

While supply chain design may involve many strategic, tactical and operational decisions, most supply chain design decisions are concerned with location decisions, i.e., where to locate facilities such as plants, processing units, warehouses, and retail stores to minimize the total cost of transportation (Speier et al., 2011). With the emergence of integrated logistics, integrated manufacturing, and strategic procurement, supply chain design goals have expanded beyond their limited focus on

Literature review

Supply chain risk management (SCRM) is defined as the development and implementation of strategies to manage both day-to-day and exceptional risks along a supply chain, with the objective of reducing vulnerability and ensuring business continuity (Zsidisin et al., 2005, Wieland and Wallenburg, 2012). Sources of risk include (but are not limited to) supply disruptions, demand fluctuations, environmental uncertainty and turbulence, equipment breakdown, procurement failures, and forecast

Problem description

In the strategic design of supply chains, there are possibly several parameters whose measures cannot be determined accurately, and their values are considered to be stochastic. In our model, the probability of each scenario is determined as a discrete value and we use the expected value approach to evaluate it. Moreover, each scenario is time-dependent and dynamically affects all parameters. In other words, the probability of each scenario is not fixed, and it is changed during the time

The heuristic algorithm

We examined the number of constraints, and we learned that the redundancy of each parameter significantly affects total constraints. For instance, if the number of each parameter is equal to 2 (parameters include number of products, scenarios, periods, suppliers, manufacturers, warehouses, distribution centers and customers), we should define more than 500 constraints. Narenji et al. (2011) reported that when the number of parameters increases in MIP, a branch and bound algorithm is not capable

Discussion

In this study, to solve an NP-hard problem related to supply chain design under risks and disruptions, we designed a heuristic algorithm based on a relaxation method and the number of satisfied constraints. In the process of decision-making to choose the best solution, different strategies affect our alternative solutions (Fig. 9). The decision model can assist a manager to manage the trade-off among investment, risk, and costs within a supply chain. Among single Yi׳s (decision variables), the

Conclusion

The strategic design of a supply chain system is very important to the long-term profitability and survival of firms. One of the key questions in supply chain design is how to determine the trade-off between the capability of the supply chain (investment) and vulnerability to supply chain disruptions under a variety of uncertain future conditions (its risk). Firms would be able to decrease the negative impact of risks through investment in more capabilities; however these investments increase

Acknowledgment

This research is based upon work supported by the National Science Foundation (NSF) under Grant number 123887 (Research Initiation Award: Understanding Risks and Disruptions in Supply Chains and their Effect on Firm and Supply Chain Performance).

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