Modeling topologically resilient supply chain networks

Abstract

The ubiquity of supply chains along with their increasingly interconnected structure has ignited interest in studying supply chain networks through the lens of complex adaptive systems. A particularly important characteristic of supply chains is the desirable goal of sustaining their operation when exposed to unexpected perturbations. Applied network science methods can be used to analyze topological properties of supply chains and propose models for their growth. Network models focusing on the critical aspect of supply chain resilience may provide insights into the design of supply networks that may quickly recover from disruptions. This is vital for understanding both static and dynamic structures of complex supply networks, and enabling management to make informed decisions and prioritizing particular operations. This paper proposes an action-based perspective for creating a compact probabilistic model for a given real-world supply network. The action-based model consists of a set of rules (actions) that a firm may use to connect with other firms, such that the synthesized networks are topologically resilient. Additionally, it captures the heterogeneous roles of different firms by incorporating domain specific constraints. Results analyzing the resilience of networks subjected to node disruptions show that networks synthesized using the proposed model can generally outperform its real-world counterpart.

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

“…[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.…”

Exploring the raison d’etre behind metric selection in network analysis: a systematic review

“…For example, Lao et al (2016) use degree centrality to weight edges in their network to represent air passengers, thus providing a measure of a city's centrality. Arora and Ventresca (2018) use Betweenness and Closeness centrality for preferential linking in the synthesis of resilient Supply Chain Networks (SCN), where centrality measures act as proxies for price, performance and quality. Mu et al (2020) examine the spatial distribution of green space and physical factors to explore alternative green space planning strategies using Degree, Closeness and Betweenness.…”

Enhanced Variable Neighborhood Search-Based Recovery Supplier Selection for Post-Disruption Supply Networks

“…Then, assumptions are presented. To simulate supplier disruptions, the node failure model is adopted [17,31,32,38], which describes the disruption of a supplier node as the total failure of it. Besides, this study is focused on the disruption management of supply networks from a macro system level, the autonomous individual behaviors from a micro level are not considered.…”

“…As some researchers defined, supply networks are interconnected structures that emerge from a largely downstream exchange of goods between firms (i.e., manufacturers, distributors, retailers, etc.) that are involved in creating a set of final products [17]. For example, the supply network of a large-scale enterprise may have multiple manufacturers and these manufacturers are connected with multiple suppliers by various product supply-demand relations.…”