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

Priyanka Koushik

Abstract: This paper analyses the supply chain optimization using data-driven simulation and sensitivity analysis techniques. Data-driven 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 decision-making, 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.

Keywords: Supply chain optimization, data-driven simulation, sensitivity analysis, integration framework, decision support systems, resilience, advanced simulation, big data analytics