Methodology for Simulation and Analysis of Complex Adaptive Supply Network Structure and Dynamics Using Information Theory

Abstract

Abstract:Supply networks existing today in many industries can behave as complex adaptive systems making them more difficult to analyze and assess. Being able to fully understand both the complex static and dynamic structures of a complex adaptive supply network (CASN) are key to being able to make more informed management decisions and prioritize resources and production throughout the network. Previous efforts to model and analyze CASN have been impeded by the complex, dynamic nature of the systems. However, drawing from other complex adaptive systems sciences, information theory provides a model-free methodology removing many of those barriers, especially concerning complex network structure and dynamics. With minimal information about the network nodes, transfer entropy can be used to reverse engineer the network structure while local transfer entropy can be used to analyze the network structure's dynamics. Both simulated and real-world networks were analyzed using this methodology. Applying the methodology to CASNs allows the practitioner to capitalize on observations from the highly multidisciplinary field of information theory which provides insights into CASN's self-organization, emergence, stability/instability, and distributed computation. This not only provides managers with a more thorough understanding of a system's structure and dynamics for management purposes, but also opens up research opportunities into eventual strategies to monitor and manage emergence and adaption within the environment.

Toward a Theory of Industrial Supply Networks: A Multi-Level Perspective via Network Analysis

“…On the other hand, as network is a nonlinear system, even given the same number of nodes and edges, networks can represent completely different structures such as regular, random, scale-free network and star networks [36]. In this context, evaluating the complexity of such networks is a major challenge and a formidable task in the SNs, as it pushes the limits of the ability of the SC managers to manage material and information flows from their suppliers to their customers [12,13,35].…”

“…It easily distinguished that regular networks represented a low complexity (entropy), whereas random networks represented a high complexity (entropy) [36]. Network entropy can also be considered as a quantitative measure of network robustness, which has been widely applied in economic, scientific, social and biological networks [35,39,40] and accounts for a relatively positive correlation with the network robustness.…”

“…Finally, we evaluated the SN resilience of distinctive communities from a vertex-level perspective. This paper employed the entropy [33][34][35][36][37][38][39][40], which is generated by node degree [41]. Several elimination scenarios of random failures and targeted attacks (i.e., degree-prior and P-prior) were simulated to investigate the complexity and robustness of the SN by sequentially removing the firms [10,18,42,43].…”

Heterogeneity and Environmental Preferences Shape the Evolution of Cooperation in Supply Networks

“…e heterogeneous environment signifies that when the focal firm's payoff is higher than the supplier's, it will increase the focal firm's fitness, for the focal firm is a more influential leader in the supply networks, which usually obtain additional payoff [41,42]. On the contrary, when the focal firm's payoff is less than the supplier's income, it will decrease.…”