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

How to Cite?: Priyanka Koushik, "Data-Driven Simulation: Integrating Sensitivity Analysis into Supply Chain Optimization", Volume 13 Issue 5, May 2024, International Journal of Science and Research (IJSR), Pages: 875-884, https://www.ijsr.net/getabstract.php?paperid=SR24511175813, DOI: https://dx.doi.org/10.21275/SR24511175813

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