Article Details
  • Published Online:
    May  2025
  • Product Name:
    The IUP Journal of Operations Management
  • Product Type:
    Article
  • Product Code:
    IJOM050525
  • DOI:
    10.71329/IUPJOM/2025.24.2.86-99
  • Author Name:
    Rahul Basu
  • Availability:
    YES
  • Subject/Domain:
    Management
  • Download Format:
    PDF
  • Pages:
    86-99
Volume 24, Issue 2, May 2025
Optimizing Supply Chain Management Using AI, Neural Networks, and Taguchi Methods
Abstract

The paper investigates advancements in supply chain management, focusing on optimization techniques such as Taguchi method, neural networks, and ant colony optimization. By addressing critical challenges such as variability, task dependencies, and uncertainty, the study emphasizes minimizing project delays and improving decision-making. Simulations and experiments reveal reduced project durations and enhanced stability in critical paths. Neural networks, in particular, demonstrate their efficacy in predictive analytics, while Taguchi method offers robust task duration optimization.

Introduction

The global supply chain landscape is undergoing a transformation due to disruptions, digitalization, and the growing need for agile responses.