A Supply Chain Analytics Framework for Selective Supplier Rewiring Using Agent-Based Network Simulation

Sustainable supply chains require analytical methods that explain how firms adapt supplier relationships under disruption while balancing resilience, resource efficiency, and environmental exposure. This study develops an agent-based network simulation and supply network analytics model to examine how selective supplier rewiring may shape sustainable supply network resilience. The model represents firms as autonomous agents embedded in a directed logistics supply network. Agents adjust sourcing ties under disruption using decision rules based on supplier dependency, relational embeddedness, supplier reliability, capacity, network proximity, and supplier sustainability performance. The simulation is used as a theory-elaborating computational experiment rather than as an empirical validation design. Under the specified model assumptions, selective rewiring generates patterns of more balanced structural adaptation than random or unconstrained supplier switching. Local sourcing decisions produce measurable changes in density, clustering, modularity, path length, recovery, and sustainability exposure. The sustainability-constrained scenario illustrates how supplier replacement rules are associated with a higher simulated sustainability profile of active ties while preserving recovery performance under the specified model assumptions. The study contributes to supply chain analytics by linking micro-level supplier adaptation rules with macro-level network transformation. The results should be interpreted as simulation-based pattern-generation outcomes that require future empirical validation using real-world longitudinal supply network data.