An agent-based network analytic perspective on the evolution of complex adaptive supply chain networks

• Main Authors: PONNANBALAM, Loganathan, Tan, A., FU, Xiuju, YIN, Xiaofeng, WANG, Zhaoxia, GOH, Rick S. M.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2013
• Subjects: adaptive systems; agent-based model; Complex adaptive supply network; network evolution; structural characteristics of supply network; supply chain; Numerical Analysis and Scientific Computing; OS and Networks
• Online Access: https://ink.library.smu.edu.sg/sis_research/6840; https://ink.library.smu.edu.sg/context/sis_research/article/7843/viewcontent/2013_An_agent_based_network_analytic_perspective_on_the_evolution_of_complex_adaptive_supply_chain_networks1.pdf
• Institution: Singapore Management University

Supply chain networks of modern era are complex adaptive systems that are dynamic and highly interdependent in nature. Business continuity of these complex systems depend vastly on understanding as to how the supply chain network evolves over time (based on the policies it adapts), and identifying the susceptibility of the evolved networks to external disruptions. The objective of this article is to illustrate as to how an agent-based network analytic perspective can aid this understanding on the network-evolution dynamics, and identification of disruption effects on the evolved networks. To this end, we developed a 4-tier agent based supply chain model and simulated the evolution of the supply chain network based on two different partner selection scenarios. Network-evolution diagrams, change in structural characteristics over time and effect of disruptions on the critical nodes for the two different partner selection scenarios are presented. The networkevolution characteristics (social network analysis based node/network level metrics) over time have been quantified and the vulnerability of the evolved networks, due to disruptions that result in reduction in production of the network's critical producer node, has been identified