Articles
| Open Access |
DOI:
https://doi.org/10.55640/ijefms/Volume10Issue04-02
Enhancing Supply Chain Decision-Making with Large Language Models: A Comparative Study of Ai-Driven Optimization
Sakib Salam Jamee , Department of Management Information Systems, University of Pittsburgh, PA, USA Md Refat Hossain , Master of Business Administration, Westcliff University, USA Mahabub Hasan , Master’s In Information Systems, Touro University, New York, USA Mohammad Kawsur Sharif , Department of Business Administration and Management, Washington University of Virginia, USA Md Sayem Khan , Master of Science in Project Management, Saint Francis College (SFC), Brooklyn, New York, USA Md Iftakhayrul Islam , MBA in Management Information Systems, International American University, USA Shaidul Islam Suhan , MBA in Business analytics, International American University, USAAbstract
This study explores the potential of Large Language Models (LLMs) in optimizing supply chain decision-making by comparing their performance with traditional machine learning models, including Random Forest (RF), Gradient Boosting Machines (GBM), Support Vector Machines (SVM), and Deep Neural Networks (DNN). The evaluation focuses on four key supply chain tasks: demand forecasting, supplier selection, inventory management, and logistics optimization. Results indicate that LLMs significantly outperform traditional models, particularly in tasks involving both structured and unstructured data. The LLM achieved superior accuracy in demand forecasting, supplier selection, and logistics optimization, demonstrating its capability to analyze complex, multi-dimensional data from sources such as transactional records, supplier feedback, and market trends. Although the LLM required more computational resources, its overall performance highlighted its potential to revolutionize supply chain management. The findings suggest that LLMs offer a promising approach to optimizing supply chain decisions, improving efficiency, reducing costs, and enhancing overall decision-making accuracy. Future research should focus on addressing the computational challenges and exploring broader applications of LLMs in supply chain contexts.
Keywords
Large Language Models, supply chain optimization, machine learning, demand forecasting, supplier selection
References
Phan, H. T. N. (2024). EARLY DETECTION OF ORAL DISEASES USING MACHINE LEARNING: A COMPARATIVE STUDY OF PREDICTIVE MODELS AND DIAGNOSTICACCURACY. International Journal of Medical Science and Public Health Research, 5(12), 107-118.
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. In Advances in neural information processing systems (Vol. 33, pp. 1877-1901).
Huang, Y., Zhang, Y., & Cheng, H. (2021). Leveraging IoT and NLP for real-time logistics optimization. International Journal of Supply Chain Management, 12(2), 110-121.
Nguyen, H. T., Nguyen, T. L., & Pham, D. T. (2021). Application of large language models for demand forecasting in supply chain management. Journal of Business and Industrial Marketing, 36(5), 786-799. https://doi.org/10.1108/JBIM-10-2020-0359
Shishika, R., Selvam, T., & Karthikeyan, T. (2018). Random forest models for supplier selection: A case study in global supply chains. International Journal of Logistics Systems and Management, 30(3), 281-299. https://doi.org/10.1504/IJLSM.2018.091539
Simchi-Levi, D., Kaminsky, P., & Simchi-Levi, E. (2003). Designing and managing the supply chain: Concepts, strategies, and case studies (2nd ed.). McGraw-Hill.
Xie, J., Zhang, J., & Liu, X. (2020). Hybrid support vector machine and genetic algorithm for demand forecasting. Journal of the Operational Research Society, 71(8), 1222-1234. https://doi.org/10.1080/01605682.2019.1661798
Zhang, X., Li, X., & Zhao, Y. (2022). Using large language models for supplier selection and performance evaluation in supply chain management. Supply Chain Management: An International Journal, 27(4), 478-491. https://doi.org/10.1108/SCM-11-2021-0503
Rahman, M. M., Akhi, S. S., Hossain, S., Ayub, M. I., Siddique, M. T., Nath, A., ... & Hassan, M. M. (2024). EVALUATING MACHINE LEARNING MODELS FOR OPTIMAL CUSTOMER SEGMENTATION IN BANKING: A COMPARATIVE STUDY. The American Journal of Engineering and Technology, 6(12), 68-83.
Akhi, S. S., Shakil, F., Dey, S. K., Tusher, M. I., Kamruzzaman, F., Jamee, S. S., ... & Rahman, N. (2025). Enhancing Banking Cybersecurity: An Ensemble-Based Predictive Machine Learning Approach. The American Journal of Engineering and Technology, 7(03), 88-97.
Pabel, M. A. H., Bhattacharjee, B., Dey, S. K., Jamee, S. S., Obaid, M. O., Mia, M. S., ... & Sharif, M. K. (2025). BUSINESS ANALYTICS FOR CUSTOMER SEGMENTATION: A COMPARATIVE STUDY OF MACHINE LEARNING ALGORITHMS IN PERSONALIZED BANKING SERVICES. American Research Index Library, 1-13.
Das, P., Pervin, T., Bhattacharjee, B., Karim, M. R., Sultana, N., Khan, M. S., ... & Kamruzzaman, F. N. U. (2024). OPTIMIZING REAL-TIME DYNAMIC PRICING STRATEGIES IN RETAIL AND E-COMMERCE USING MACHINE LEARNING MODELS. The American Journal of Engineering and Technology, 6(12), 163-177.
Hossain, M. N., Hossain, S., Nath, A., Nath, P. C., Ayub, M. I., Hassan, M. M., ... & Rasel, M. (2024). ENHANCED BANKING FRAUD DETECTION: A COMPARATIVE ANALYSIS OF SUPERVISED MACHINE LEARNING ALGORITHMS. American Research Index Library, 23-35.
Rishad, S. S. I., Shakil, F., Tisha, S. A., Afrin, S., Hassan, M. M., Choudhury, M. Z. M. E., & Rahman, N. (2025). LEVERAGING AI AND MACHINE LEARNING FOR PREDICTING, DETECTING, AND MITIGATING CYBERSECURITY THREATS: A COMPARATIVE STUDY OF ADVANCED MODELS. American Research Index Library, 6-25.
Uddin, A., Pabel, M. A. H., Alam, M. I., KAMRUZZAMAN, F., Haque, M. S. U., Hosen, M. M., ... & Ghosh, S. K. (2025). Advancing Financial Risk Prediction and Portfolio Optimization Using Machine Learning Techniques. The American Journal of Management and Economics Innovations, 7(01), 5-20.
Ahmed, M. P., Das, A. C., Akter, P., Mou, S. N., Tisha, S. A., Shakil, F., ... & Ahmed, A. (2024). HARNESSING MACHINE LEARNING MODELS FOR ACCURATE CUSTOMER LIFETIME VALUE PREDICTION: A COMPARATIVE STUDY IN MODERN BUSINESS ANALYTICS. American Research Index Library, 06-22.
Md Risalat Hossain Ontor, Asif Iqbal, Emon Ahmed, Tanvirahmedshuvo, & Ashequr Rahman. (2024). LEVERAGING DIGITAL TRANSFORMATION AND SOCIAL MEDIA ANALYTICS FOR OPTIMIZING US FASHION BRANDS’ PERFORMANCE: A MACHINE LEARNING APPROACH. International Journal of Computer Science & Information System, 9(11), 45–56. https://doi.org/10.55640/ijcsis/Volume09Issue11-05
Rahman, A., Iqbal, A., Ahmed, E., & Ontor, M. R. H. (2024). PRIVACY-PRESERVING MACHINE LEARNING: TECHNIQUES, CHALLENGES, AND FUTURE DIRECTIONS IN SAFEGUARDING PERSONAL DATA MANAGEMENT. International journal of business and management sciences, 4(12), 18-32.
Iqbal, A., Ahmed, E., Rahman, A., & Ontor, M. R. H. (2024). ENHANCING FRAUD DETECTION AND ANOMALY DETECTION IN RETAIL BANKING USING GENERATIVE AI AND MACHINE LEARNING MODELS. The American Journal of Engineering and Technology, 6(11), 78-91.
Nguyen, Q. G., Nguyen, L. H., Hosen, M. M., Rasel, M., Shorna, J. F., Mia, M. S., & Khan, S. I. (2025). Enhancing Credit Risk Management with Machine Learning: A Comparative Study of Predictive Models for Credit Default Prediction. The American Journal of Applied sciences, 7(01), 21-30.
Bhattacharjee, B., Mou, S. N., Hossain, M. S., Rahman, M. K., Hassan, M. M., Rahman, N., ... & Haque, M. S. U. (2024). MACHINE LEARNING FOR COST ESTIMATION AND FORECASTING IN BANKING: A COMPARATIVE ANALYSIS OF ALGORITHMS. Frontline Marketing,Management and Economics Journal, 4(12), 66-83.
Hossain, S., Siddique, M. T., Hosen, M. M., Jamee, S. S., Akter, S., Akter, P., ... & Khan, M. S. (2025). Comparative Analysis of Sentiment Analysis Models for Consumer Feedback: Evaluating the Impact of Machine Learning and Deep Learning Approaches on Business Strategies. Frontline Social Sciences and History Journal, 5(02), 18-29.
Nath, F., Chowdhury, M. O. S., & Rhaman, M. M. (2023). Navigating produced water sustainability in the oil and gas sector: A Critical review of reuse challenges, treatment technologies, and prospects ahead. Water, 15(23), 4088.
Hossain, S., Siddique, M. T., Hosen, M. M., Jamee, S. S., Akter, S., Akter, P., ... & Khan, M. S. (2025). Comparative Analysis of Sentiment Analysis Models for Consumer Feedback: Evaluating the Impact of Machine Learning and Deep Learning Approaches on Business Strategies. Frontline Social Sciences and History Journal, 5(02), 18-29.
Chowdhury, O. S., & Baksh, A. A. (2017). IMPACT OF OIL SPILLAGE ON AGRICULTURAL PRODUCTION. Journal of Nature Science & Sustainable Technology, 11(2).
Nath, F., Asish, S., Debi, H. R., Chowdhury, M. O. S., Zamora, Z. J., & Muñoz, S. (2023, August). Predicting hydrocarbon production behavior in heterogeneous reservoir utilizing deep learning models. In Unconventional Resources Technology Conference, 13–15 June 2023 (pp. 506-521). Unconventional Resources Technology Conference (URTeC).
Ahmmed, M. J., Rahman, M. M., Das, A. C., Das, P., Pervin, T., Afrin, S., ... & Rahman, N. (2024). COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR BANKING FRAUD DETECTION: A STUDY ON PERFORMANCE, PRECISION, AND REAL-TIME APPLICATION. American Research Index Library, 31-44.
Shakil, F., Afrin, S., Al Mamun, A., Alam, M. K., Hasan, M. T., Vansiya, J., & Chandi, A. (2025). HYBRID MULTI-MODAL DETECTION FRAMEWORK FOR ADVANCED PERSISTENT THREATS IN CORPORATE NETWORKS USING MACHINE LEARNING AND DEEP LEARNING. American Research Index Library, 6-20.
Rishad, S. S. I., Shakil, F., Tisha, S. A., Afrin, S., Hassan, M. M., Choudhury, M. Z. M. E., & Rahman, N. (2025). LEVERAGING AI AND MACHINE LEARNING FOR PREDICTING, DETECTING, AND MITIGATING CYBERSECURITY THREATS: A COMPARATIVE STUDY OF ADVANCED MODELS. American Research Index Library, 6-25.
Das, A. C., Rishad, S. S. I., Akter, P., Tisha, S. A., Afrin, S., Shakil, F., ... & Rahman, M. M. (2024). ENHANCING BLOCKCHAIN SECURITY WITH MACHINE LEARNING: A COMPREHENSIVE STUDY OF ALGORITHMS AND APPLICATIONS. The American Journal of Engineering and Technology, 6(12), 150-162.
Al-Imran, M., Ayon, E. H., Islam, M. R., Mahmud, F., Akter, S., Alam, M. K., ... & Aziz, M. M. (2024). TRANSFORMING BANKING SECURITY: THE ROLE OF DEEP LEARNING IN FRAUD DETECTION SYSTEMS. The American Journal of Engineering and Technology, 6(11), 20-32.
Akhi, S. S., Shakil, F., Dey, S. K., Tusher, M. I., Kamruzzaman, F., Jamee, S. S., ... & Rahman, N. (2025). Enhancing Banking Cybersecurity: An Ensemble-Based Predictive Machine Learning Approach. The American Journal of Engineering and Technology, 7(03), 88-97.
Pabel, M. A. H., Bhattacharjee, B., Dey, S. K., Jamee, S. S., Obaid, M. O., Mia, M. S., ... & Sharif, M. K. (2025). BUSINESS ANALYTICS FOR CUSTOMER SEGMENTATION: A COMPARATIVE STUDY OF MACHINE LEARNING ALGORITHMS IN PERSONALIZED BANKING SERVICES. American Research Index Library, 1-13.
Siddique, M. T., Jamee, S. S., Sajal, A., Mou, S. N., Mahin, M. R. H., Obaid, M. O., ... & Hasan, M. (2025). Enhancing Automated Trading with Sentiment Analysis: Leveraging Large Language Models for Stock Market Predictions. The American Journal of Engineering and Technology, 7(03), 185-195.
Article Statistics
Downloads
Copyright License
Copyright (c) 2025 Sakib Salam Jamee, Md Refat Hossain, Mahabub Hasan, Mohammad Kawsur Sharif, Md Sayem Khan, Md Iftakhayrul Islam, Shaidul Islam Suhan
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright and Ethics:
- Authors are responsible for obtaining permission to use any copyrighted materials included in their manuscript.
- Authors are also responsible for ensuring that their research was conducted in an ethical manner and in compliance with institutional and national guidelines for the care and use of animals or human subjects.
- By submitting a manuscript to International Journal of Economics Finance & Management Science (IJEFMS), authors agree to transfer copyright to the journal if the manuscript is accepted for publication.