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Journal ArticleDOI

Supply network topology and robustness against disruptions – an investigation using multi-agent model

01 Mar 2011-International Journal of Production Research (Taylor & Francis Group)-Vol. 49, Iss: 5, pp 1391-1404
TL;DR: In this article, the authors examined the relationship between supply network's topology and its robustness in the presence of random failures and targeted attacks, using the basic framework and parameters in the experimental game presented in Sterman [1989, Modeling managerial behavior: Misperceptions of feedback in a dynamic decision making context.
Abstract: In this study we examine the relationship between supply network's topology and its robustness in the presence of random failures and targeted attacks. The agent-based model developed in this paper uses the basic framework and parameters in the experimental game presented in Sterman [1989, Modeling managerial behavior: Misperceptions of feedback in a dynamic decision making context. Management Science, 35 (3), 321–339] for modelling adaptive managerial decision making in an inventory management context. The study extends the linear supply chain context to a complex supply network and undertakes a rigorous examination of robustness of these supply networks that are characterised by distinct network characteristics. We theorise that network characteristics such as average path length, clustering coefficient, size of the largest connected component in the network and the maximum distance between nodes in the largest connected component are related to the robustness of supply networks, and test the research h...

Summary (3 min read)

Introduction

  • In recent times, supply disruptions are receiving considerable managerial attention due to their adverse impact on organizational performance.
  • The increased interest in supply chain disruptions is also evident in research studies.
  • The authors paper fits within this multiagent based approach.
  • It has been observed that several supply networks exhibit incredible robustness in the presence of disruptions while others fail to survive random failures or targeted attacks.
  • Further details on the analytical and empirical developments in the random graphs and scale-free network theory are presented in Albert and Barabasi (2000) and Dorogovtsev and Mendes (2002).

Average path length

  • The average path length presents an approach to characterize the spread of a network by calculating the average distance between any pair of nodes.
  • For a network with N nodes, it is likely that not all nodes will have the same number of edges (also referred as node degree).
  • The spread of the node degrees is characterized in terms of the distribution function P(k).
  • The degree distribution of most random networks can be approximated by binomial distribution (with Poisson distribution being a more appropriate approximation for very large number of nodes).

Clustering coefficient

  • Clustering coefficient capture the small-world nature inherent in several real-world networks.
  • In a random network the probability that nearest neighbors of a node are connected is equal to the probability that two nodes in the network are connected.
  • In the event of disruptions it could result in high level of vulnerability due to the high levels of dependency among the nodes.
  • Further, as the size of the largest connected component increase the maximum distance between any two nodes in the component increase.
  • Drawing on this reasoning the authors hypothesize: H3: In the presence of disruptions, the robustness of supply network is positively associated with the size of its largest connected component.

Research Design

  • The use of agent-based simulation model in supply chain context is gaining research interest (e.g. Moyaux, et al., 2007).
  • The approach enables us to capture the complexities and dynamics associated with network topologies and examine the evolutionary nature of choices made by firms within these supply networks.

Agent-Based Model

  • The authors model extends the experimental game presented in Sterman (1989) by allowing for more complex network topologies.
  • The results obtained from the agent-based model provide a satisfactory replication of the results in Sterman (1989).
  • Since in a network setup each supply chain entity (i.e. factory, distributors, warehouses, and retailers) can supply to more than one demand source, the authors had to add some extra rules that are not present in the basic experimental game setup presented in Sterman (1989).
  • Two supply chain entities that are directly connected to each other are at a distance of one.
  • In the random network topology each new node is connected to one randomly chosen existing node where all existing nodes have equal probability of being chosen.

Experimental Design

  • The development of the simulation model and the analysis of the data gathered from simulation runs follow the systematic approach suggested in literature (Kelton, 1997; Sargent, 1998; Nance and Sargent, 2002; Law, 2004).
  • The overall experimental design and parameters used for the study are reported in table 1.
  • The authors ran the agent based simulation model for 105 time ticks; each time tick corresponds to a week.
  • The authors collect data from twenty replications of each scenario of the simulation model, and use the average of the weekly data obtained from these 20 replications for analysis.

Results and Discussion

  • The authors examine the robustness of individual topologies by undertaking paired sample t-test for each network topology considered in the study.
  • In total 24 paired sample t-tests (for each disruption scenario explained in the experimental design) were conducted for each topology.
  • Robustness of a network topology against disruptions is gauged by a non significant difference in the mean for the performance measures as reported by the paired sample t-test (i.e. p-value > 0.05).
  • Initially, the authors undertake the binomial logistics regression analysis for the entire sample of network topologies considered in this study.
  • The authors use the topology type (categorical variable denoting scale-free and random network) as a control variable.

Overall Sample

  • The results of the binomial logistics regression analysis for the overall sample are presented in table 3. [Table 3 about here].
  • All other hypothesized relationships are strongly supported (p<0.05).
  • The results in table 3 also show that scale-free networks are relatively more robust from the inventory perspective, however, when viewed from the backorders and total cost perspectives, random networks are more robust.
  • The results present a compelling evidence of the association between network characteristics and robustness of supply networks.
  • While the maximum distance between nodes in the largest connected component is not significantly associated with inventory and total cost based robustness measures, a unit increase in this variable increases robustness from backorders perspective by almost 5 times.

Random Networks

  • The authors present the results of the binomial logistics regression analysis for the sub-sample comprising of random networks in table 5. [Table 5 about here].
  • A unit increase in average path length substantially increases the odds of vulnerability from backorders and total cost perspectives.
  • A unit increase in the maximum distance between nodes in the largest connected component was found to increase the odds of a robust supply network by 3.7 times, 14.1 times and 16.9 times when the robustness is evaluated from inventory, backorders and total cost perspectives respectively.
  • There are a few limitations of this study that provide directions for future research.

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1
SUPPLY NETWORK TOPOLOGY AND ROBUSTNESS AGAINST
DISRUPTIONS – AN INVESTIGATION USING MULTIAGENT MODEL
Anand Nair *
Department of Management Science
Moore School of Business
University of South Carolina
Columbia, SC - 29208, USA
Phone: (803) 777-2648
Fax: (803) 777-3064
E-mail: nair@moore.sc.edu
José M. Vidal
Department of Computer Science and Engineering
Swearingen Engineering Center
University of South Carolina
Columbia, SC - 29208, USA
Phone: (803) 777-0928
Fax: (803) 777-3767
E-mail: vidal@sc.edu
* Corresponding Author
(Forthcoming)
International Journal of Production Research

2
Supply Network Topology and Robustness against Disruptions – an
investigation using multiagent model
In this study we examine the relationship between supply network’s topology and its robustness
in the presence of random failures and targeted attacks. The agent based model developed in this
paper uses the basic framework and parameters in the experimental game presented in Sterman
(1989) for modeling adaptive managerial decision making in an inventory management context.
The study extends the linear supply chain context to a complex supply network and undertakes a
rigorous examination of robustness of these supply networks that are characterized by distinct
network characteristics. We theorize that network characteristics such as average path length,
clustering coefficient, size of the largest connected component in the network and the maximum
distance between nodes in the largest connected component are related to the robustness of
supply networks, and test the research hypotheses using data from several simulation runs.
Simulations were carried out using twenty distinct network topologies where ten of these
topologies were generated using preferential attachment approach (based on the theory of scale-
free networks) and the remaining ten topologies were generated using random attachment
approach (using random graph theory as a foundation). These twenty supply networks were
subjected to random demand and their performances were evaluated by considering varying
probabilities of random failures of nodes and targeted attacks on nodes. We also consider the
severity of these disruptions by considering the downtime of the affected nodes. Using the data
collected from a series of simulation experiments, we test the research hypotheses by means of
binomial logistic regression analysis. The results point towards a significant association between
network characteristics and supply network robustness assessed using multiple performance
measures. We discuss the implications of the study and present directions for future research.
Keywords: Supply networks, Topology, Disruptions, Robustness, Scale-free Networks, Random
Networks, Agent-based model, Binomial Logistics Regression
Introduction
In recent times, supply disruptions are receiving considerable managerial attention due to
their adverse impact on organizational performance. Sheffi and Rice (2005) highlight the
supply chain implication of the terrorist attack on September 11, 2001 by giving the
examples of adverse effect on Ford’s and Toyota’s operations. Chozick (2007) report that
70% of Japan's auto production was temporarily paralyzed for a week due to the
disruptions in the supply of piston ring caused by a 6.8-magnitude earthquake that hit
central Japan thereby damaging Riken Corp.’s production plant, the supplier that makes
custom piston rings for most of the car makers in Japan.

3
The increased interest in supply chain disruptions is also evident in research
studies. For instance, studies have examined the financial implications of supply chain
disruptions (e.g., Hendricks & Singhal, 2003; 2005) and investigated risk mitigation and
contingency planning strategies in the presence of supply chain disruptions (e.g. Sodhi,
2005; Tomlin, 2006). There is also a growing research stream that examines disruption
and related supply chain issues by using a multiagent-based simulation framework (e.g.
Thadakamalla et al., 2004).
Our paper fits within this multiagent based approach. In this study we examine
how supply network topology is associated with its robustness in the event of disruptions.
It has been observed that several supply networks exhibit incredible robustness in the
presence of disruptions while others fail to survive random failures or targeted attacks.
Sheffi and Rice (2005) provide examples of firms, whose supply networks are
characteristically distinct from each other, making their levels of resilience and
robustness to random failures and targeted attacks to be considerably different. This study
builds on the extant literature in statistical physics that examine the error and attack
tolerance of complex networks (Albert et al., 2000; Thadakamalla et al., 2004), and
consider the impact of supply network characteristics, such as average path length,
clustering coefficient, size of the largest connected component, and maximum distance
between two nodes in the largest connected component, on performance measured in
terms of inventory levels, backorders and total costs within a supply network.
Literature review and research hypotheses

4
Modeling of complex networks has focused on three main classes: (i) random graphs:
these variants of Erdős – Rényi model (Erdős and Rényi, 1959; Bollobás, 1985) are still
widely used in many fields and serve as a benchmark for many modeling and empirical
studies; (ii) small-world models: these models interpolate between the highly clustered
regular lattices and random graphs; and (iii) scale-free models (Barabási and Albert,
1999): these are motivated by the power-law degree distribution of the nodes in complex
networks as evident in several networks such as the World Wide Web (Albert et al.,
1999), the Internet (Faloutsos et al., 1999), or metabolic networks (Jeong et al., 2000).
When viewed from the perspective of robustness to failures, it is observed that random
networks and small-world networks have similar properties due to the similarity in their
degree distribution (Thadakamalla et al., 2004). Meanwhile, scale-free networks are
highly robust to random failures but are sensitive to targeted attacks. Thus, random
networks and scale-free networks present two characteristically distinct topologies, a
systematic examination of which can provide deeper insights regarding the association of
network characteristics with its robustness against disruptions.
Studies, such as Albert et al. (2000), have focused on random graphs and scale-
free network topologies to discern the error and attack tolerances of these networks.
Consistent with this stream of research and with literature emphasizing that supply
networks follow topologies commonly observed in complex adaptive systems (Surana et
al., 2005; Sun and Wu, 2005; Pathak et al., 2007; Wang et al., 2008; Bichou et al., 2007),
in this paper we consider random and scale-free network topologies for our research
investigation of robustness of supply networks.

5
The theory of random networks has its origin in the use of probability methods in
problems related to graph theory. Erdős and Rényi (1959) define a random graph to be
one in which N nodes are connected to n edges, chosen randomly from N(N-1)/2 possible
edges. There are
n
NN
C
]2/)1([
possible graphs that can be formed with all graphs having
equal probability of being realized in the probability space. The theory of random graphs
concerns with an examination of this probability space as
N .
The scale-free networks were motivated from a mismatch between the clustering
coefficients found in real-world network and those predicted by random graphs. Also, it
has been observed that even for those networks for which P(k) (a distribution function
representing the probability that a randomly selected node has exactly k edges) has an
exponential tail, the degree distribution do not follow Poisson distribution as suggested in
random graphs theory. Barabási and Albert (1999) present the idea of scale-free network
by considering the power-law degree distribution that is observed in several real world
networks. The networks grow by continuous addition of new nodes. Instead of following
a random-attachment of nodes, these networks follow a preferential attachment logic
whereby new nodes join a node that is already highly connected (i.e. exhibit high degree).
Formally, the probability Π that a new node n will connect to a node i in the network
depends on the degree k
i
of node i:
=Π
j
j
i
i
k
k
k )(.
Further details on the analytical and empirical developments in the random graphs
and scale-free network theory are presented in Albert and Barabasi (2000) and
Dorogovtsev and Mendes (2002). In the following subsections we present details
regarding network characteristics that are used for our research investigation.

Citations
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TL;DR: Some of the major results in random graphs and some of the more challenging open problems are reviewed, including those related to the WWW.
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Cites background from "Supply network topology and robustn..."

  • ...Another important observation in literature is a linkage of SC complexity and resilience (Blackhurst et al. 2005; Nair and Vidal 2011; Bode and Wagner 2015; Dubey et al. 2019a; Tan, Cai, and Zhang 2020)....

    [...]

  • ...…and Sokolov 2013; Demirel et al. 2019) • Robustness – ability to withstand a disruption (or a series of disruptions) to maintain the planned performance (Nair and Vidal 2011; Simchi-Levi, Wang, and Wei 2018) • Resilience – ability to withstand a disruption (or a series of disruptions) and…...

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TL;DR: The VSC model can help firms in guiding their decisions on recovery and re-building of their SCs after global, long-term crises such as the COVID-19 pandemic and can be of value for decision-makers to design SCs that can react adaptively to both positive changes and negative changes.
Abstract: Viability is the ability of a supply chain (SC) to maintain itself and survive in a changing environment through a redesign of structures and replanning of performance with long-term impacts. In this paper, we theorize a new notion-the viable supply chain (VSC). In our approach, viability is considered as an underlying SC property spanning three perspectives, i.e., agility, resilience, and sustainability. The principal ideas of the VSC model are adaptable structural SC designs for supply-demand allocations and, most importantly, establishment and control of adaptive mechanisms for transitions between the structural designs. Further, we demonstrate how the VSC components can be categorized across organizational, informational, process-functional, technological, and financial structures. Moreover, our study offers a VSC framework within an SC ecosystem. We discuss the relations between resilience and viability. Through the lens and guidance of dynamic systems theory, we illustrate the VSC model at the technical level. The VSC model can be of value for decision-makers to design SCs that can react adaptively to both positive changes (i.e., the agility angle) and be able to absorb negative disturbances, recover and survive during short-term disruptions and long-term, global shocks with societal and economical transformations (i.e., the resilience and sustainability angles). The VSC model can help firms in guiding their decisions on recovery and re-building of their SCs after global, long-term crises such as the COVID-19 pandemic. We emphasize that resilience is the central perspective in the VSC guaranteeing viability of the SCs of the future. Emerging directions in VSC research are discussed.

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Abstract: In this study, the ripple effect in the supply chain is analysed. Ripple effect describes the impact of a disruption propagation on supply chain performance and disruption-based scope of changes in...

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Cites background from "Supply network topology and robustn..."

  • ...…this included an increased understanding of how control parameters influence dynamic behaviours and how nonlinearities impact the performance of the SC. 2.4 Complexity and reliability theory Nair and Vidal (2011) study SC robustness against disruptions using graph-theoretical topology analysis....

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TL;DR: This study conceptualizes supply network disruption and resilience by examining the structural relationships among entities in the network by comparing four fundamental supply network structures, and shows that node/arc-level disruptions do not necessarily lead to network- level disruptions, and demonstrates the importance of differentiating a nodes/arc disruption vs. a network disruption.

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Additional excerpts

  • ...…capability, velocity capability, visibility capability, & collaboration capability • Network-level analysis with emphasis on a focal firm • Singe case study (a firm with its three supply chains) Nair and Vidal (2011) • Disruption not formally defined • Disruption as random failure and targeted…...

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  • ...Although some researchers have argued, for instance, that high-degree nodes (Craighead et al., 2007) and short average path length (Nair and Vidal, 2011) play critical roles in network disruption, our analysis shows that node failure and average walk length are not necessarily related with a…...

    [...]

  • ...• No operational measures for resilience • Unclear level of analysis • Survey Table 1 (Continued) Referencesa Definition Level of definition and analysis Methods/nature of study Main findings Conceptual definition Operational measuresb Wagner and Neshat (2010) • Disruption as “the trigger that leads to the occurrence of risk” (p. 122) • Not operational measures for disruption discussed; but instead, supply chain vulnerability drivers categorized into demand side, supply side, and supply chain structure • Unclear level of definition • Empirical • Propose a SCVI (supply chain vulnerability index) metric that can be used to assess the vulnerability of supply chains and compare vulnerabilities of supply chains across industries • A related concept, “vulnerability” is discussed • No operational measures for resilience • Level of analysis: multiple & mixed (a firm, supply chain, industry, and entire economy) • Survey • Resilience not discussed Jüttner and Maklan (2011) • Disruptions “imply a certain level of turbulence [Hamel and Valikangas, 2003] and uncertainty in the supply chain [van der Vorst and Beulens, 2002]” (p. 247) • No operational measures for disruption • Network-level definition • Qualitative • Suggest that supply chain risk and knowledge management enhance resilience by improving flexibility, visibility, velocity and collaboration capabilities at the supply chain/network level • Resilience defined by flexibility, velocity, visibility, and collaboration capabilities (adapted from Ponomarov and Holcomb, 2009) • Resilience measured as flexibility capability, velocity capability, visibility capability, & collaboration capability • Network-level analysis with emphasis on a focal firm • Singe case study (a firm with its three supply chains) Nair and Vidal (2011) • Disruption not formally defined • Disruption as random failure and targeted attack on network nodes (firms) for their inventory levels, backorders, and total costs • Network-level definition • Analytical • Certain established network characteristics (such as average path length, clustering coefficient, size of the largest connected component) are associated with the robustness of supply networks (to random failures/targeted attacks on demand and uptime of nodes) • Resilience (robustness) not formally defined • Resilience (robustness) as multiple network attributes such as average path length, clustering coefficient, size of the largest connected component (LCC), and max. distance between nodes in the LCC • Firm-level analysis • Simulation (agent-based modeling) Zhao et al. (2011) • Disruptions “affect the normal operations” (p. 1) and are either random or targeted • No operational measures for disruption • Network-level definition • Analytical • Suggest centrality as a measure for a node’s importance....

    [...]

  • ...…al., 2010; Wagner and Neshat, 2010), conceptual (e.g., Christopher and Peck, 2004; Kovács and Tatham, 2009; Tang, 2006), qualitative (e.g., Craighead et al., 2007; Jüttner et al., 2003; Sheffi and Rice, 2005), and simulation/modeling (e.g., Nair and Vidal, 2011; Wu et al., 2007; Zhao et al., 2011)....

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  • ...This also conflicts some research on supply network resilience that argues for the association between the well-established network metrics and resilience (e.g., Nair and Vidal, 2011)....

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References
More filters
Journal ArticleDOI
01 May 2007
TL;DR: Two principles explaining how to use the shared information to reduce the amplification of order variability induced by lead times are described, which are proposed as a cause of the bullwhip effect.
Abstract: The bullwhip effect is an amplification of the variability of the orders placed by companies in a supply chain. This variability reduces the efficiency of supply chains, since it incurs costs due to higher inventory levels and supply chain agility reduction. Eliminating the bullwhip effect is surely simple; every company just has to order following the market demand, i.e., each company should use a lot-for-lot type of ordering policy. However, many reasons, such as inventory management, lot-sizing, and market, supply, or operation uncertainties, motivate companies not to use this strategy. Therefore, the bullwhip effect cannot be totally eliminated. However, it can be reduced by information sharing, which is the form of collaboration considered in this paper. More precisely, we study how to separate demand into original demand and adjustments. We describe two principles explaining how to use the shared information to reduce the amplification of order variability induced by lead times, which we propose as a cause of the effect. Simulations confirm the value of these two principles with regard to costs and customer service levels

149 citations

Journal ArticleDOI
TL;DR: No area within the scope of operations research and the management sciences has been affected more by advances in computing technology than simulation, and this assertion is affirmed in the review of progress in those technical areas that collectively define the art and science of simulation.
Abstract: Simulation is introduced in terms of its different forms and uses, but the focus on discrete event modeling for systems analysis is dominant as it has been during the evolution of the technique within operations research and the management sciences. This evolutionary trace of over almost fifty years notes the importance of bidirectional influences with computer science, probability and statistics, and mathematics. No area within the scope of operations research and the management sciences has been affected more by advances in computing technology than simulation. This assertion is affirmed in the review of progress in those technical areas that collectively define the art and science of simulation. A holistic description of the field must include the roles of professional societies, conferences and symposia, and publications. The closing citation of a scientific value judgment from over 30 years in the past hopefully provides a stimulus for contemplating what lies ahead in the next 50 years.

148 citations


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  • ...3.2 Experimental design The development of the simulation model and the analysis of the data gathered from simulation runs follow the systematic approach suggested in the literature (Kelton 1997, Sargent 1998, Nance and Sargent 2002, Law 2004)....

    [...]

Journal ArticleDOI
TL;DR: In this article, the problem of managing demand risk in tactical supply chain planning for a particular global consumer electronics company is considered, where the company follows a deterministic replenishment-and-planning process despite considerable demand uncertainty.
Abstract: We consider the problem of managing demand risk in tactical supply chain planning for a particular global consumer electronics company. The company follows a deterministic replenishment-and-planning process despite considerable demand uncertainty. As a possible way to formally address uncertainty, we provide two risk measures, “demand-at-risk” (DaR) and “inventory-at-risk” (IaR) and two linear programming models to help manage demand uncertainty. The first model is deterministic and can be used to allocate the replenishment schedule from the plants among the customers as per the existing process. The other model is stochastic and can be used to determine the “ideal” replenishment request from the plants under demand uncertainty. The gap between the output of the two models as regards requested replenishment and the values of the risk measures can be used by the company to reallocate capacity among different products and to thus manage demand/inventory risk.

133 citations

Proceedings ArticleDOI
05 Dec 2004
TL;DR: This tutorial gives a state-of-the-art presentation of what the practitioner really needs to know to be successful in simulation output-data analysis.
Abstract: One of the most important but neglected aspects of a simulation study is the proper design and analysis of simulation experiments. In this tutorial we give a state-of-the-art presentation of what the practitioner really needs to know to be successful. We will discuss how to choose the simulation run length, the warmup-period duration (if any), and the required number of model replications (each using different random numbers). The talk concludes with a discussion of three critical pitfalls in simulation output-data analysis.

79 citations

Proceedings ArticleDOI
01 Dec 1997
TL;DR: Methods to help design the run for simulation models and interpreting their results effectively are described, in general terms, for several different purposes.
Abstract: This paper describe , in general terms, methods to help design the run for simulation models and interpreting their out ut. Statistical methods are described for several different purposes, and related problems like comparison, variance reduction, sensitivity estimation, etamodeling, and optimization are mentioned. The main point is to call attention to the challenges and ( pportunities in using simulation models carefully an( i effectively.

66 citations

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Q1. What are the contributions in "Supply network topology and robustness against disruptions – an investigation using multiagent model" ?

In this paper, the authors examined the impact of supply network characteristics, such as average path length, clustering coefficient, size of the largest connected component, and maximum distance between two nodes in the largest connecting component, on performance measured in terms of inventory levels, backorders and total costs within a supply network.