A Distributed Approach for Agile Supply Chain Decision-Making Based on Network Attributes

TL;DR

This paper explores how network attributes of supply chains influence the effectiveness of distributed decision-making strategies in managing disruptions, offering insights for designing resilient and agile supply chain responses.

Contribution

It introduces a novel framework analyzing supply chain performance based on network topology and agent capabilities within a distributed decision-making context.

Findings

Distributed decision-making can improve response times in disrupted supply chains.

Network structure significantly impacts supply chain resilience and performance.

Trade-offs exist between centralized and distributed approaches in terms of performance and communication.

Abstract

In recent years, the frequent occurrence of disruptions has had a negative impact on global supply chains. To stay competitive, enterprises strive to remain agile through the implementation of efficient and effective decision-making strategies in reaction to disruptions. A significant effort has been made to develop these agile disruption mitigation approaches, leveraging both centralized and distributed decision-making strategies. Though trade-offs of centralized and distributed approaches have been analyzed in existing studies, no related work has been found on understanding supply chain performance based on the network attributes of the disrupted supply chain entities. In this paper, we characterize supply chains from a capability and network topological perspective and investigate the use of a distributed decision-making approach based on classical multi-agent frameworks. The performance of the distributed framework is evaluated through a comprehensive case study that investigates the performance of the supply chain as a function of the network structure and agent attributes within the network in the presence of a disruption. Comparison to a centralized decision-making approach highlights trade-offs between performance, computation time, and network communication based on the decision-making strategy and network architecture. Practitioners can use the outcomes of our studies to design response strategies based on agent capabilities, network attributes, and desired supply chain performance.