Optimizing Supply Chain Management with ChatGPT: An Analytical and Empirical Multi-Methodological Study
Year: 2025
Type: article
Abstract: This work explores how a leading language model, ChatGPT, can improve every aspect of supply chain management (SCM) by using a multi-methodological approach: quantitative analysis, qualitative case studies, and simulation models, to set goals that delve into the efficiency of ChatGPT in enhancing demand forecasting accuracy, improving decision-making processes, and highlighting the best practices for its deployment across different SCM tasks. Empirical results indicate that ChatGPT significantly increases the accuracy of the forecast, and the efficiency of decision-making compared to traditional methods. Qualitative insights reflect positive feedback from supply chain professionals, and best practices identified in areas such as predictive maintenance, and automation of customer service. The key findings have a great number of implications for SCM practitioners, theorists, and policymakers, indicating the potential of the model for transforming supply chain operations while pointing at avenues for future research on AI integration and its impact assessment.
Authors: Akash Kadam, Harshad Pitkar
Institutions: Texas A&M University – Kingsville, Cummins (United States)
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4408482630