Network characteristics and supply chain resilience under conditions of risk propagation
Introduction
Modern supply chains are complex networks that are exposed to various risks (Bode and Wagner, 2015; Ponomarov and Holcomb, 2009). For example, Ford Motor Company has up to 10 tiers of suppliers and its Tier 1 suppliers alone number 1400 companies across 4400 manufacturing sites (Simchi-Levi et al., 2015). Such globally distributed business entities are exposed to risks, including, but not limited to, natural disasters, political uncertainties, labor risks, and financial disruptions. With the driving forces of profitability and competitive advantage, each business entity inside a supply chain network is tightly interconnected with the others. For such complex and tightly coupled systems, a small local disruption that occurs anywhere within the network may potentially extend to other business entities, causing the disruption to be substantial and long-lasting. This risk propagation process, otherwise known as the ripple effect (Ivanov et al., 2014, 2019; Dolgui et al., 2018), and the major disruptions that it can lead to, are unavoidable, unpredictable, and hard to manage. Thus, it is particularly important for such supply chains to be resilient (Ambulkar et al., 2015; Jüttner and Maklan, 2011; Pettit et al., 2010), i.e., to have the ability to better prepare for, respond to, and recover from these inevitable disruptions (Ponomarov and Holcomb, 2009).
Supply chain resilience (SCR) can be influenced by many factors, including supply chain visibility, the availability of inventory, the adoption of proper disruption mitigation plans, and the overall structure of the network (Chowdhury and Quaddus, 2017; Macdonald et al., 2018; Pettit et al., 2013). Among these factors, network structure plays a particularly important role in determining SCR (Basole and Bellamy, 2014; Mari et al., 2015; Zhao et al., 2011). For example, scale-free networks tend to perform better immediately following a disruption because they are more robust to random disruptions (Kim et al., 2011; Thadakamalla et al., 2004; Zhao et al., 2011). Also, as risks can propagate through the interaction links, the connection pattern of a network can influence the risk propagation process and, hence, the recoverability of the whole supply chain.
Studies that focus on supply chain network structure and SCR generally adopt one of two approaches. The first is the traditional operations research approach of optimizing a supply chain distribution network by selecting the specific nodes and links that should be included (Snyder et al., 2012). This approach is effective for static and simple supply chain networks in which the nodes and possible links can be enumerated and are controllable. The second approach is the network science approach, which studies complex networks and focuses on the distinct elements or actors represented by nodes and their links (Basole and Bellamy, 2014; Kim et al., 2011; Zhao et al., 2011). Studies that adopt the network science approach have primarily investigated the impact of different network types on resilient behavior, and the most frequently studied network types are the scale-free network, the small-world network, and the random network (Basole and Bellamy, 2014; Kim et al., 2015; Nair and Vidal, 2011; Zhao et al., 2011). Although such efforts provide valuable insights into how network structure influences SCR, focusing strictly on the network type has limitations. Because many real supply chain networks do not belong to one specific type of network, research results, which depend on such a focus, may not be generalizable to many real-world situations.
Focusing on network characteristics has the potential to address the above limitation. A network characteristic describes one facet of the network structure. For example, the clustering coefficient measures the degree to which nodes in a network tend to cluster together, and the average path length gives the average length of the shortest path between any pair of nodes. Since any type of network can always be described by a group of network characteristics, we believe it is reasonable to presume that a set of network characteristics could more effectively represent a “real” supply chain network than would be possible by using the network type alone. More importantly, it is these individual network characteristics, rather than the network type, that a supply chain manager can adjust to improve the supply chain structure. Activities such as forward and backward integration, major process simplification, configuration of factories, warehouses, or retail locations, and major product design (Schroeder and Goldstein, 2016) may not directly affect the type of network, but they can have a significant impact on network characteristics. Looking at network characteristics, therefore, allows practitioners to compare different operational strategies with respect to their ability against disruptions.
We wish to test the presumption that network characteristics are more effective than the network type at explaining the resilience of a supply chain network. To perform this investigation, we must address several questions. First, since SCR deals both with preparing for and recovering from a disruption, can focusing on network characteristics allow us to capture this complexity? Second, of the many network characteristics that exist, which characteristics are most appropriate for investigating SCR? Finally, in what ways can the expanded knowledge provided by the use of network characteristics be applied to real supply chain networks to support effective decision-making? Our study aims to address these questions by investigating the relationship between network characteristics and SCR.
We therefore seek to contribute to the SCR and supply chain network literature in the following ways: 1) by showing that using network characteristics can provide better insights into the complexity of SCR than is possible by simply using the network type; 2) by studying a much wider range of network characteristics than those considered in the existing literature, and using them to analyze several different types of resilience measures; 3) by adopting a complex network perspective for quantitatively characterizing the impacts of supply chain disruptions in the presence of risk propagation; and 4) by showing that a reduced set of key network characteristics can be used to estimate the resilience measures for real supply chain networks.
The remainder of the paper is organized as follows: Section 2 provides a review of the literature. Section 3 describes the SCR indicators and the simulation model employed to calculate them. Section 4 presents the experimental design for our investigation and Section 5 provides the analysis of the results. To illustrate how our study can be applied to a real supply chain network, we present a case study in Section 6. Finally, we conclude and discuss contributions and future research directions in Section 7.
Section snippets
Supply chain resilience
Supply chain resilience has gained increasing attention in recent years, both from practitioners and from the research community (Hosseini et al., 2019a; Pettit et al., 2013; Ponomarov and Holcomb, 2009; Vugrin et al., 2011). Driven by globalization and the adoption of lean operations, supply chains are becoming increasingly complex and the business entities that constitute them are increasingly dependent on one another. Such complex and tightly coupled supply chain networks are very vulnerable
Measuring supply chain network resilience
The resilience indicators that we use in this study are derived from the supply chain network disruption literature (Pettit et al., 2013; Sheffi and Rice, 2005; Zhao et al., 2011), and they capture both the “robustness” and the “recoverability” aspects. These indicators are tied to characteristics of a disruption response by equating them to specific aspects of the disruption profile (Sheffi and Rice, 2005), similar to the one illustrated in Fig. 2. Such a disruption profile can be used to
Design and implementation of investigation
As described in Section 3, supply chain network resilience can be characterized by both the severity of the disruption and the subsequent risk propagation behavior. Risk propagation behavior is shaped by both network structure and node level risk capacities – how easily each node can be influenced by its disrupted neighbors (risk diffusion rate) and how quickly it can recover from a disruption (risk recovery rate). Given a supply chain network and a disruption of a given severity, together with
Descriptive analysis
To begin our analysis, we present the network resilience indicator results for the different network types that were generated, for both the robustness measures and the recoverability measure. To ensure that the corresponding networks are comparable we only present network instances with an average degree of four at the quick risk diffusion process. The results, presented in Fig. 4, indicate that specific patterns of resilience behavior are not obvious across different network types. This
Case study
We now show that the proposed key network characteristics can be used to evaluate SCR in general supply chain networks by applying the approach to two different real-world supply networks: the Japanese automotive supply chain network and the supply chain network of Apple Inc. The literature indicates that these networks have different structures and that the automotive supply chain network is more characteristic of a scale-free type whereas the electronic supply chain network is more of a
Main findings
In this paper, we investigate the relationships between network characteristics and SCR. There are four main findings. First, we prove that network characteristics generally explain supply chain network resilience better than network type and average degree. Thus, to understand how network structure influences SCR, looking at network characteristics is a better approach than focusing on network type. Second, we select the key influential network characteristics that work as effectively as the
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