Abstract: In modern, globally interconnected markets, supply chain agility, or the competency for rapid adaptation, has become increasingly important. Traditional approaches to enhancing supply chain agility are certain to fall short because none of them capture either the dynamic or decentralized nature of modern supply chains. This paper examines how Agent-Based Models can act as a transformational approach toward evolutionary enhancements in supply chain agility. ABMs can model independent entities, or agents, at each level in the supply chain-suppliers, manufacturers, distributors, and customers-operating independently according to their own behaviours, goals, and interactions. In fact, through the modelling of agents and their interactions, ABMs provide a potent platform for testing how local decisions and behavior lead to emergent global outcomes, such as responsiveness, resilience, and adaptability of the supply chain. The adaptive aspect comes in with the adaptive learning mechanisms within the agents that are able to evolve strategies over time, given changes in the environmental conditions and agent interactions. The agent-based model is now applied to a wide range of scenarios, from routine fluctuations in demand to large-scale disruptions, and provides valuable insights into the conditions under which agility can be enhanced. These findings confirm that the evolutionary improvements induced by ABM can be associated with substantial gains in performance relevant to supply chain speed and flexibility in response to unforeseen events. The final part of the paper discusses the practical implications of real-world deployment and further research in deploying ABMs on supply chains.

Keywords: Agent-Based Models (ABM), Supply Chain Agility, Adaptive Learning, Complex Systems, Decentralized Decision-Making, Emergent Behavior

Pages: 1-15