Downloads: 5 | Views: 100 | Weekly Hits: ⮙5 | Monthly Hits: ⮙5

Review Papers | Science and Technology | United States of America | Volume 13 Issue 5, May 2024 | Popularity: 5.9 / 10

Rate This Article!      

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

Edition: Volume 13 Issue 5, May 2024

Pages: 875 - 884

Make Sure to Disable the Pop-Up Blocker of Web Browser

Click to Cite this Article

Similar Articles