Data-Driven Simulation: Integrating Sensitivity Analysis into Supply Chain Optimization

This paper analyses the supply chain optimization using data-driven simulation and sensitivity analysis techniques.Datadriven simulation methods like agent-based modelling (ABM), discrete event simulation (DES), and system dynamics modelling, as well as sensitivity analysis methods like one-factor-at-a-time (OFAT), Monte Carlo simulation, and design of experiments, are discussed.Systematic assessment, optimization, and decision-making based on performance measures require the integration framework, which combines sensitivity analysis with simulation tools like DES or ABM.This in the end emphasizes how these approaches improve decisionmaking, system resilience, and supply chain performance.Advanced simulation, dynamic sensitivity analysis, real-time decision support, big data analytics, and industry-specific applications are future research areas.