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Optimizing Supply Chain Management with ChatGPT: An Analytical and Empirical Multi-Methodological Study

Author

Listed:
  • Akash Kadam

    (Department of Mechanical and Industrial Engineering, Texas A&M University-Kingsville, , Kingsville, TX, 78363, USA)

  • Harshad Pitkar

    (Cummins Inc, Columbus, IN 47201, USA)

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.

Suggested Citation

  • Akash Kadam & Harshad Pitkar, 2025. "Optimizing Supply Chain Management with ChatGPT: An Analytical and Empirical Multi-Methodological Study," Journal of Computer Science and Technology Studies, Al-Kindi Center for Research and Development, vol. 7(1), pages 337-350, January.
    • Handle: RePEc:alk:jcsts0:v:7:y:2025:i:1:id:8916
    DOI: 10.32996/jcsts.2025.7.1.25
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