Towards a use of network analysis: quantifying the complexity of Supply Chain Networks

Battini, Daria; Persona, Alessandro; Allesina, Stefano

doi:10.1504/ijecrm.2007.014427

Cited by 33 publications

(31 citation statements)

References 26 publications

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“…Network ties could be represented by the number of joint programs or of shared patents, level of trust, or perceived transactional risks. Future studies therefore can incorporate the relative strength of supply ties using SNA as the method can effectively illustrate networks with “weighted” links (Borgatti and Li, 2009; Battini et al, 2007). Exchange ties involving a multi‐level interface will have differential impact compared to other comparable supply ties based only on a single type of transaction.…”

Section: Discussionmentioning

confidence: 99%

Structural investigation of supply networks: A social network analysis approach

Kim

Choi

Yan

et al. 2010

J of Ops Management

586

665

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a b s t r a c t A system of interconnected buyers and suppliers is better modeled as a network than as a linear chain. In this paper we demonstrate how to use social network analysis to investigate the structural characteristics of supply networks. Our theoretical framework relates key social network analysis metrics to supply network constructs. We apply this framework to the three automotive supply networks reported in Choi and Hong (2002). Each of the supply networks is analyzed in terms of both materials flow and contractual relationships. We compare the social network analysis results with the case-based interpretations in Choi and Hong (2002) and conclude that our framework can both supplement and complement case-based analysis of supply networks.

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Section: Discussionmentioning

confidence: 99%

Structural investigation of supply networks: A social network analysis approach

Kim

Choi

Yan

et al. 2010

J of Ops Management

586

665

View full text Add to dashboard Cite

show abstract

“…Another important network‐level measure is SCS complexity. Complexity has been conceptualized and operationalized from numerous perspectives, such as vertical, horizontal, and spatial complexity [Choi and Hong, 2002; Danese, 2010], information‐theoretic entropy of the system [Battini, Persona, and Allesina, 2007; Basole and Rouse, 2008], and the number of connections among SCS entities weighted according to their position in the network [Caridi et al, 2010]. For large‐scale sociotechnical systems, such as SCSs, the structural embeddedness and interactions among components (e.g., suppliers,firms, suppliers, facilities, and customers) can significantly impact system complexity [Osorio, Dori, and Sussman, 2011].…”

Section: Integrative Frameworkmentioning

confidence: 99%

Network analysis of supply chain systems: A systematic review and future research

Bellamy

Basole

2012

Systems Engineering

139

100

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Supply chains are continuously evolving and adapting systems driven by complex sociotechnical interfirm interactions. Traditional engineering and operations management modeling approaches have primarily focused on technical issues and are not well suited to effectively capture the many complex structural and behavioral aspects of supply chain systems (SCSs). There is growing recognition by the supply chain community of the significant benefits a network analytic lens can provide to understand, design, and manage SCSs. We systematically review and analyze the relevant literature and, drawing on a multidisciplinary theoretical foundation, develop an integrative framework. Our framework identifies three distinct, but interdependent themes that characterize the study of SCSs: SCS network structure (i.e., system architecture), SCS network dynamics (i.e., system behavior), and SCS network strategy (i.e., system policy and control). We elaborate on these themes, review key findings, identify the current limitations and knowledge gaps, and discuss the fundamental benefits derived from adopting an integrated SCSs perspective. We conclude with future research directions for network analysis in SCS design and management, in particular, and complex enterprise systems, in general. and Sussman, 2011; Bartolomei et al., 2012], a comprehensive understanding of the performance and behavior of supply chain systems (SCSs) therefore requires consideration of both technical and social issues. Traditional engineering and operations management modeling approaches have primarily focused on technical issues [Min and Zhou, 2002]. These approaches, however, are not well suited to effectively capture and describe the structural and behavioral complexities inherent in SCSs.An emerging interdisciplinary lens promising to overcome this theoretical and methodological gap is the use of network analysis approaches [Basole et al., 2011]. Network analysis draws on theories from the social, organizational, and complexity sciences and leverages graph theoretic methods to model, analyze, and visualize the structure, dynamics, and strategies that shape SCSs. There has been a surge in scholarly studies modeling an SCS as a complex network of interactions between system entities since the seminal work by Choi and colleagues [Choi, Dooley, and Rungtusanatham, 2001;Choi and Hong, 2002] and more recently Borgatti and Li [2009]. However, there is no organizing framework to facilitate an understanding of the plethora of supply chain management (SCM) issues examined using network analysis. Moreover, previous work on the use of network analysis in systems engineering (SE) is quite sparse. Notable exceptions include the work by Batallas and Yassine [2006], Braha and Bar-Yam [2006], Collins, Yassine, and Borgatti [2009], and Bartolomei et al. [2012], where the focus is almost entirely on product development; there are no SE studies that have used network analysis for the study of SCSs. Consequently, there is a window of opportunity to review and illustrate the va...

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“…Many authors noticed that if a researcher tries to analyze potential failures/disruptions of all the suppliers in a SC or their absolute majority, he/she encounters a simply impractical and unrealistic problem demanding an astronomic amount of time and budget. Moreover, the supply chain control of the root node of the supply chain is much more important than the control of any of its successors ( [6,8]). …”

Section: Basic Definitionsmentioning

confidence: 99%

“…This paper analyzes the structural complexity of the supply chain affected by technological, organizational and environmental adverse events in the SCs, consequences of which lead, as a result, to violations of correct functioning of the SC. The detailed definition and analysis of the supply-chain structural complexity can be found in [2][3][4][5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Entropy-Based Algorithm for Supply-Chain Complexity Assessment

Kriheli

Levner

2018

Algorithms

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This paper considers a graph model of hierarchical supply chains. The goal is to measure the complexity of links between different components of the chain, for instance, between the principal equipment manufacturer (a root node) and its suppliers (preceding supply nodes). The information entropy is used to serve as a measure of knowledge about the complexity of shortages and pitfalls in relationship between the supply chain components under uncertainty. The concept of conditional (relative) entropy is introduced which is a generalization of the conventional (non-relative) entropy. An entropy-based algorithm providing efficient assessment of the supply chain complexity as a function of the SC size is developed.

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Towards a use of network analysis: quantifying the complexity of Supply Chain Networks

Cited by 33 publications

References 26 publications

Structural investigation of supply networks: A social network analysis approach

Structural investigation of supply networks: A social network analysis approach

Network analysis of supply chain systems: A systematic review and future research

Entropy-Based Algorithm for Supply-Chain Complexity Assessment

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