Research on the robustness of interdependent supply networks with tunable parameters

Shi, Xiaohu; Deng, Dayong; Long, Wei; Li, Yanyan; Yu, Xiao-Hong

doi:10.1016/j.cie.2021.107431

Cited by 20 publications

(7 citation statements)

References 55 publications

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“…The clustering coefficient describes the likelihood of business transactions occurring between neighboring node firms in an SCN [60]. The scalar index can be used to analyze the degree distribution of an SCN [61], and the node type affects the normal operation of an SCN [30,31]. All of the above network characteristics exist in real-life SCNs and are important components of the structural properties of SCNs.…”

Section: Supply Chain Network Risk Propagationmentioning

confidence: 99%

Research on Supply Chain Network Resilience: Considering Risk Propagation and Node Type

Xue,

Li,

et al. 2024

Applied Sciences

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The aim of this study is to investigate the impact of network characteristics on supply chain network resilience (SCNR) when risk propagation occurs in supply chain networks (SCNs). The network characteristics we study here (e.g., clustering coefficient, scalar index, node type, etc.) are exceptionally important for real-life SCNs, but are not sufficiently considered in the previous research on SCNR. To this end, we first construct an SCN model with adjustable parameters for multiple network characteristics. Second, this SCN model is combined with a susceptible–infectious–susceptible model to construct an SCN risk propagation model. Third, we propose using the average (i.e., a novel SCNR metric considering node type) of the sizes of the maximum connected subgraphs (which contain all node types) over a period of time after risk propagation reaches a steady state. Fourth, the parameters of the SCN model are adjusted to generate SCNs with different network characteristics, and then the resilience of these SCNs is addressed accordingly. The simulation results mainly show the following: the larger the scalar index of an SCN is, the higher its resilience; the larger the clustering coefficient of an SCN is, the smaller its resilience; and the more uniform the distribution of node types is in an SCN, the higher its resilience. Our research work will help optimize SCNs’ structure, which has important implications for society and practice.

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Section: Supply Chain Network Risk Propagationmentioning

confidence: 99%

Research on Supply Chain Network Resilience: Considering Risk Propagation and Node Type

Xue,

Li,

et al. 2024

Applied Sciences

Self Cite

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“…Robustness when a first-order phase transition occurs (node removal rate at initial partition) is defined as first attack robustness (𝐴𝑅𝑜𝑏1), as shown in Equation (10), while robustness when a percolation transition occurs (node removal rate at maximum partition) is defined as second attack robustness (𝐴𝑅𝑜𝑏2), as shown in Equation (12), which is the same as Equation (6). 𝐴𝑅𝑜𝑏1 is the maximum value of 〈𝑠〉 at the initial 1% node removal.…”

Section: Robustness Against Attack Riskmentioning

confidence: 99%

“…The results showed that this network has first and second percolation transitions at a few points and an approximately 10% node removal rate for all removal rules, such as random, degree-descending, and degree-ascending rules. Shi et al 12 added several topological variables to the generation model proposed by Tang et al 11 , and the relationship between the topological values and robustness was evaluated by defining robustness as the node removal rate at which the occupancy of the LCC falls significantly. Adenso-Díaz et al 2 removed weighted edges, but not nodes, randomly or from higher weighted edges in a hierarchical SCN formed by four players to observe changes in the level of service of the SCN.…”

Section: Literature Reviewmentioning

confidence: 99%

Topological features for the robustness of global supply chain networks

Kawasaki,

Yotsushima

2023

Preprint

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The topological structure of modern global supply chain networks (SCNs) has become increasingly complex. In recent years, major disruption risk events haveemerged, disrupting complex SCNs. Since a global SCN is the aggregation of several sub-SCNs in terms of industries and countries (regions) and is temporally changing in its topological characteristics, robustness toward risk should be analysed by the community in consideration of its temporality. In this study, we aim (i) to propose a method to generate temporal SCNs of multiple communities, (ii) to evaluate the robustness of each SCN against error and attack risks, and (iii) to identify the topological features that influence the robustness of SCNs using real transaction data between firms. As a result, eight SCNs weredetected based on industries and countries, and the size of these SCNsincreased over time. The average shortest path length and degree distribution have similar impacts on each SCN, while the cluster structure diverges among SCNs. Regarding robustness against error and attack risk, unlike in existing studies, the SCNs are significantly partitioned at the initial firm removal rate. Only for attack risk, percolation transition was found at approximately 10% removal of firms. The relationship between robustness indicators and topological features is identified by panel data analysis, and we find that the significant topological features affecting robustness differ by type of risk.

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“…[49] examine the topological structure and COVID-19-related risk propagation in Thin-film-transistor liquid-crystal displays (TFT-LCD) supply networks from a dynamic perspective. (2) Network topology and robustness interact in mitigating disruptions [4,10,22,23,[34][35][36][37][50][51][52][53][54][55][56]. This aspect pays attention on analyzing the robustness of SCNs from the perspective of network structure, revealing the fragility, collapse conditions, and evolution of the SCN.…”

Section: Tablementioning

confidence: 99%

“…Similarly, the 2022 Russia-Ukraine conflict affected the global SCN, causing severe supply shortages or even disruptions in many industries such as food, energy, transportation, and manufacturing. Consequently, to respond to these disruptions, it is of vital importance to understand how to create a robust supply chain network [34][35][36][37]. A large body of literature has explored supply chain disruptions and the relationship between SCN structure and its robustness yet (see Section 2).…”

Section: Introductionmentioning

confidence: 99%

A Novel Supply Chain Network Evolving Model Under Random and Targeted Disruptions

Wang

Zhao²,

Sun

et al. 2023

Preprint

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Research on the robustness of interdependent supply networks with tunable parameters

Cited by 20 publications

References 55 publications

Research on Supply Chain Network Resilience: Considering Risk Propagation and Node Type

Research on Supply Chain Network Resilience: Considering Risk Propagation and Node Type

Topological features for the robustness of global supply chain networks

A Novel Supply Chain Network Evolving Model Under Random and Targeted Disruptions

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