Impact of Generative AI on Operational Excellence in Supply Chain Management: A Quantitative Analysis

Abstract

This study investigates the transformative impact of Generative Artificial Intelligence (GenAI) on operational excellence within supply chain management (SCM), particularly in manufacturing organizations. As supply chains face increasing complexity and volatility, GenAI offers powerful capabilities to enhance efficiency, agility, and decision-making. Using a quantitative research design, the study compares pre- and post-implementation performance metrics across 100 manufacturing firms. Key areas examined include cost reduction, inventory optimization, production scheduling, and risk mitigation. The analysis employs simple linear regression to test two hypotheses related to GenAI’s influence on operational cost and performance. Results reveal a statistically significant positive relationship between GenAI adoption and operational excellence, with an R-squared value of 0.838 indicating strong predictive power. Organizations that integrated GenAI experienced improvements in lead time reduction, on-time delivery rates, and inventory turnover. The findings support the resource-based view and dynamic capabilities theory, suggesting GenAI as a strategic asset that fosters competitive advantage through enhanced supply chain capabilities. This research contributes to both academic literature and managerial practice by offering empirical evidence on the benefits of GenAI in achieving supply chain excellence. The study also highlights critical success factors and suggests directions for future research in AI-driven supply chain transformation.