Artificial Intelligence in Supply Chain Management: Enhancing Demand Forecasting, Inventory Optimization, and Operational Efficiency

Abstract

The study explored the impact of AI in Supply Chain Management, specifically on demand forecasting, inventory optimization, and operational efficiency. The data was collected using a structured questionnaire, where a quantitative cross-sectional survey design was used, with 300 professionals from the supply chain, procurement, logistics, inventory, warehousing and operations departments. The questionnaire had a 5-point Likert scale ranging from strongly disagree to strongly agree and measured the usage of Artificial Intelligence, demand forecasting, inventory optimization, and operational efficiency. Data were analyzed using descriptive statistical analysis, reliability analysis, Pearson correlation and linear regression analysis. The results showed high levels of AI utilization (M = 4.18, SD = 0.61), demand forecasting (M = 4.21, SD = 0.59), inventory optimization (M = 4.11, SD = 0.64), and operational efficiency (M = 4.23, SD = 0.58). Demand forecasting (β = .681, p < .001), Inventory optimization (β = .647, p < .001), and Operational efficiency (β = .710, p < .001) had high positive relationships with AI. The regression models accounted for 46.4%, 41.9% and 50.4% of the variance in the demand forecasting, inventory optimization and operational efficiency respectively. The results showed that the use of AI-powered technologies was linked to improved supply chain capabilities and more efficient operational processes. The study emphasized that data quality, technological infrastructure, organizational readiness, and employee skills were all important factors in the successful implementation of AI technology in the Supply Chain Management.

References

Arunkumar, O. N., & Divya, D. (2022). Deep learning techniques for demand forecasting: Review and future research opportunities. Information Resources Management Journal, 35(2), 1–24. https://doi.org/10.4018/IRMJ.291692

Cannas, V. G., Ciano, M. P., Saltalamacchia, M., & Secchi, R. (2024). Artificial intelligence in supply chain and operations management: A multiple case study research. International Journal of Production Research, 62(9), 3333–3360. https://doi.org/10.1080/00207543.2023.2232050

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Routledge. https://doi.org/10.4324/9780203771587

Culot, G., Podrecca, M., & Nassimbeni, G. (2024). Artificial intelligence in supply chain management: A systematic literature review of empirical studies and research directions. Computers in Industry, 162, 104132. https://doi.org/10.1016/j.compind.2024.104132

Douaoui, K., Oucheikh, R., Benmoussa, O., & Mabrouki, C. (2024). Machine learning and deep learning models for demand forecasting in supply chain management: A critical review. Applied System Innovation, 7(5), 93. https://doi.org/10.3390/asi7050093

Feizabadi, J. (2022). Machine learning demand forecasting and supply chain performance. International Journal of Logistics Research and Applications, 25(2), 119–142. https://doi.org/10.1080/13675567.2020.1803246

Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203

Hao, X., & Demir, E. (2024). Artificial intelligence in supply chain management: Enablers and constraints in pre-development, deployment, and post-development stages. Production Planning & Control, 36(6), 748–770. https://doi.org/10.1080/09537287.2024.2302482

Hendriksen, C. (2023). Artificial intelligence for supply chain management: Disruptive innovation or innovative disruption? Journal of Supply Chain Management, 59(3), 65–76. https://doi.org/10.1111/jscm.12304

Jackson, I., Ivanov, D., Dolgui, A., & Namdar, J. (2024). Generative artificial intelligence in supply chain and operations management: A capability-based framework for analysis and implementation. International Journal of Production Research, 62(17), 6120–6145. https://doi.org/10.1080/00207543.2024.2309309

Jia, F. (2024). AI adoption in supply chain management: A systematic literature review. Journal of Manufacturing Technology Management, 35(6), 1125–1150. https://doi.org/10.1108/JMTM-09-2023-0431

Khedr, A. M., & Rani, S. (2024). Enhancing supply chain management with deep learning and machine learning techniques: A review. Journal of Open Innovation: Technology, Market, and Complexity, 10(4), 100379. https://doi.org/10.1016/j.joitmc.2024.100379

Lin, S., Xin, L., & Zhang, Y. (2023). AI vs. human buyers: A study of Alibaba's inventory replenishment system. INFORMS Journal on Applied Analytics. https://doi.org/10.1287/inte.2023.1160

Mediavilla, M. A., Dietrich, F., & Palm, D. (2022). Review and analysis of artificial intelligence methods for demand forecasting in supply chain management. Procedia CIRP, 107, 1126–1131. https://doi.org/10.1016/j.procir.2022.05.119

Nitsche, A.-M., Franczyk, B., Schumann, C.-A., & Reuther, K. (2024). A decade of artificial intelligence for supply chain collaboration: Past, present, and future research agenda. Logistics Research, 17, Article 5. https://doi.org/10.23773/2024_5

Nunnally, J. (1978). Psychometric theory (2nd ed.). McGraw-Hill.

Patalas-Maliszewska, J., Szmoda, M., & Losyk, H. (2024). Integrating artificial intelligence into the supply chain in order to enhance sustainable production—A systematic literature review. Sustainability, 16(16), 7110. https://doi.org/10.3390/su16167110

Walter, A., Ahsan, K., & Rahman, S. (2025). Application of artificial intelligence in demand planning for supply chains: A systematic literature review. The International Journal of Logistics Management, 36(3), 672–719. https://doi.org/10.1108/IJLM-02-2024-0120

Zhu, X., Ninh, A., Zhao, H., & Liu, Z. (2021). Demand forecasting with supply-chain information and machine learning: Evidence in the pharmaceutical industry. Production and Operations Management, 30(9), 3231–3252. https://doi.org/10.1111/poms.13426