Network characteristics and supply chain resilience under conditions of risk propagation

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Abstract

Inspired by the fact that a combination of network characteristics can describe a network more fully than the network type is able to, especially since some networks do not belong to any one specific type, this research effort investigates the relationship between network characteristics and supply chain resilience. We begin by demonstrating that the investigation of network characteristics can lead to a better understanding of supply chain resilience. We then identify the key network characteristics that best represent network structure in determining resilience. We show that utilizing a reduced list of characteristics yields performance equal to that when using a complete set of characteristics. Based on the results, we summarize key points that can support interpretation of the impact of network characteristics on resilience. We also conduct a case study to illustrate how our approach can be employed to understand resilience.

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

References (75)

  • S. Hosseini et al.

    Resilient supplier selection and optimal order allocation under disruption risks

    Int. J. Prod. Econ.

    (2019)
  • D. Ivanov et al.

    Disruption-driven supply chain (re)-planning and performance impact assessment with consideration of pro-active and recovery policies

    Transp. Res. E Logist. Transp. Rev.

    (2016)
  • Y. Kim et al.

    Supply network disruption and resilience: a network structural perspective

    J. Oper. Manag.

    (2015)
  • Y. Kim et al.

    Structural investigation of supply networks: a social network analysis approach

    J. Oper. Manag.

    (2011)
  • Y. Li et al.

    Value of supply disruption information and information accuracy

    J. Purch. Supply Manag.

    (2017)
  • P. Nuss et al.

    Mapping supply chain risk by network analysis of product platforms

    Sustain. Mater. Technol.

    (2016)
  • B. Ruhnau

    “Eigenvector-centrality — a node-centrality?”

    Soc. Netw.

    (2000)
  • K. Stephenson et al.

    Rethinking centrality: methods and examples

    Soc. Netw.

    (1989)
  • A. Świerczek

    “The impact of supply chain integration on the ‘snowball effect’ in the transmission of disruptions: an empirical evaluation of the model”

    Int. J. Prod. Econ.

    (2014)
  • C.W. Zobel

    Representing perceived tradeoffs in defining disaster resilience

    Decis. Support Syst.

    (2011)
  • C.W. Zobel et al.

    Characterizing multi-event disaster resilience

    Comput. Oper. Res.

    (2014)
  • R. Albet et al.

    Error and attack tolerance of complex networks (vol 406, pg 378, 2000)

    Nature

    (2000)
  • H. Baroud et al.

    Inherent costs and interdependent impacts of infrastructure network resilience

    Risk Anal.

    (2015)
  • R.C. Basole et al.

    Supply network structure, visibility, and risk diffusion: a computational approach

    Decis. Sci. J.

    (2014)
  • R. Bhamra et al.

    Resilience: the concept, a literature review and future directions

    Int. J. Prod. Res.

    (2011)
  • M. Bruneau et al.

    A framework to quantitatively assess and enhance the seismic resilience of communities

    Earthq. Spectra

    (2003)
  • C.R. Carter et al.

    Toward the theory of the supply chain

    J. Supply Chain Manag.

    (2015)
  • D.C. Chatfield et al.

    Stockout propagation and amplification in supply chain inventory systems

    Int. J. Prod. Res.

    (2013)
  • M. Christopher et al.

    Building the resilient supply chain

    Int. J. Logist. Manag.

    (2004)
  • C.W. Craighead et al.

    The severity of supply chain disruptions: design characteristics and mitigation capabilities

    Decis. Sci. J.

    (2007)
  • P. Dankelmann et al.

    The average eccentricity of a graph and its subgraphs

    Util. Math.

    (2004)
  • P.P. Datta et al.

    Information sharing and coordination mechanisms for managing uncertainty in supply chains: a simulation study

    Int. J. Prod. Res.

    (2011)
  • A. Dolgui et al.

    Ripple effect in the supply chain: an analysis and recent literature

    Int. J. Prod. Res.

    (2018)
  • C.F. Durach et al.

    Antecedents and dimensions of supply chain robustness: a systematic literature review

    Int. J. Phys. Distrib. Logist. Manag.

    (2015)
  • E. Estrada et al.

    Communicability in complex networks

    Phys. Rev. E - Stat. Nonlinear Soft Matter Phys.

    (2008)
  • E. Estrada et al.

    Resistance Distance, Information Centrality, Node Vulnerability and Vibrations in Complex Networks

    Netw. Sci.: Compl. Nat. Technol.

    (2010)
  • M. Falasca et al.

    A decision support framework to assess supply chain resilience

  • Cited by (121)

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