Impact of AI-Driven Demand Forecasting and Inventory Optimization on Supply Chain Efficiency and Operational Cost Reduction

Abstract

Background: Artificial intelligence (AI) is playing an increasingly important role in supply chain management by improving demand-forecasting accuracy and supporting better inventory decisions. Traditional forecasting techniques can struggle to accommodate demand uncertainty, which may lead to stockouts, overstocking, and higher operating costs. AI-powered forecasting models and intelligent inventory-optimization technologies can help organizations improve efficiency, reduce waste, and become more resilient to supply chain disruptions. However, empirical evidence linking forecasting capability to cost outcomes through inventory decisions remains limited. Aim: This study explores the relationships among AI-driven demand forecasting, inventory optimization, supply chain efficiency, and operational cost reduction. It also examines whether inventory optimization acts as a bridge between AI-driven forecasting and operational cost reduction. Method: This study used a quantitative, cross-sectional explanatory survey. A structured questionnaire was administered to 300 supply chain respondents from manufacturing (n = 156) and retail (n = 144) organizations, including supply chain professionals, logistics managers, inventory managers, and procurement specialists. Data were analyzed using descriptive statistics and PLS-SEM with bootstrapping to test the relationships among AI-driven demand forecasting, inventory optimization, supply chain efficiency, and operational cost reduction, with inventory optimization examined as a mediator. Results: Respondents reported a 29.4% improvement in forecasting accuracy, a 22.7% decrease in inventory holding costs, and a 31.5% decrease in stockout incidents. Overall supply chain efficiency increased by 27.9%; 84.2% of respondents agreed that AI-based forecasting enhanced inventory planning and decision-making, and operational costs decreased by 19.8%. In the PLS-SEM model, AI-driven demand forecasting was positively associated with inventory optimization (β = 0.71, p < 0.001) and supply chain efficiency (β = 0.63, p < 0.001). Inventory optimization was positively associated with supply chain efficiency (β = 0.58, p < 0.001) and operational cost reduction (β = 0.34, p < 0.001). Inventory optimization partially mediated the relationship between AI-driven demand forecasting and operational cost reduction (indirect effect = 0.24). Conclusion: AI-driven demand forecasting and inventory optimization were significantly and positively associated with improved supply chain performance, including better forecasting, more effective inventory utilization, and lower operational costs. Given the cross-sectional design and perceptual measures, these findings should be interpreted as associations rather than evidence of cause and effect. References Ban, G.-Y., & Rudin, C. (2019). The big data newsvendor: Practical insights from machine learning. Operations Research, 67(1), 90–108. https://doi.org/10.1287/opre.2018.1757 Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108 Baryannis, G., Validi, S., Dani, S., & Antoniou, G. (2019). Supply chain risk management and artificial intelligence: State of the art and future research directions. International Journal of Production Research, 57(7), 2179–2202. https://doi.org/10.1080/00207543.2018.1530476 Belhadi, A., Mani, V., Kamble, S. S., Khan, S. A. R., & Verma, S. (2024). Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: An empirical investigation. Annals of Operations Research, 333(2), 627–652. https://doi.org/10.1007/s10479-021-03956-x Bertsimas, D., & Kallus, N. (2020). From predictive to prescriptive analytics. Management Science, 66(3), 1025–1044. https://doi.org/10.1287/mnsc.2018.3253 Boute, R. N., Gijsbrechts, J., van Jaarsveld, W., & Van Mieghem, J. A. (2022). Deep reinforcement learning for inventory control: A roadmap. European Journal of Operational Research, 298(2), 401–412. https://doi.org/10.1016/j.ejor.2021.07.016 Chae, B., Olson, D. L., & Sheu, C. (2014). The impact of supply chain analytics on operational performance: A resource-based view. International Journal of Production Research, 52(16), 4695–4710. https://doi.org/10.1080/00207543.2013.861616 Christopher, M., & Holweg, M. (2011). “Supply chain 2.0”: Managing supply chains in the era of turbulence. International Journal of Physical Distribution & Logistics Management, 41(1), 63–82. https://doi.org/10.1108/09600031111101439 Dubey, R., Gunasekaran, A., Childe, S. J., Blome, C., & Papadopoulos, T. (2020). Big data and predictive analytics and manufacturing performance: Integrating institutional theory, resource-based view and big data culture. British Journal of Management, 31(2), 341–361. https://doi.org/10.1111/1467-8551.12355 Dubey, R., Gunasekaran, A., Childe, S. J., Fosso Wamba, S., Roubaud, D., & Foropon, C. (2021). Empirical investigation of data analytics capability and organizational flexibility as complements to supply chain resilience. International Journal of Production Research, 59(1), 110–128. https://doi.org/10.1080/00207543.2019.1582820 Eroglu, C., & Hofer, C. (2011). Lean, leaner, too lean? The inventory-performance link revisited. Journal of Operations Management, 29(4), 356–369. https://doi.org/10.1016/j.jom.2010.05.002 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 Fildes, R., Ma, S., & Kolassa, S. (2022). Retail forecasting: Research and practice. International Journal of Forecasting, 38(4), 1283–1318. https://doi.org/10.1016/j.ijforecast.2019.06.004 Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104 Galbraith, J. R. (1974). Organization design: An information processing view. Interfaces, 4(3), 28–36. https://doi.org/10.1287/inte.4.3.28 Gaur, V., Fisher, M. L., & Raman, A. (2005). An econometric analysis of inventory turnover performance in retail services. Management Science, 51(2), 181–194. https://doi.org/10.1287/mnsc.1040.0298 Gijsbrechts, J., Boute, R. N., Van Mieghem, J. A., & Zhang, D. J. (2022). Can deep reinforcement learning improve inventory management? Performance on lost sales, dual-sourcing, and multi-echelon problems. Manufacturing & Service Operations Management, 24(3), 1349–1368. https://doi.org/10.1287/msom.2021.1064 Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Sage. 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 Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8 Ivanov, D. (2020). Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transportation Research Part E: Logistics and Transportation Review, 136, Article 101922. https://doi.org/10.1016/j.tre.2020.101922 Ivanov, D., & Dolgui, A. (2020). Viability of intertwined supply networks: Extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak. International Journal of Production Research, 58(10), 2904–2915. https://doi.org/10.1080/00207543.2020.1750727 Lee, H. L., Padmanabhan, V., & Whang, S. (1997). Information distortion in a supply chain: The bullwhip effect. Management Science, 43(4), 546–558. https://doi.org/10.1287/mnsc.43.4.546 Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2020). The M4 Competition: 100,000 time series and 61 forecasting methods. International Journal of Forecasting, 36(1), 54–74. https://doi.org/10.1016/j.ijforecast.2019.04.014 Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2022). M5 accuracy competition: Results, findings, and conclusions. International Journal of Forecasting, 38(4), 1346–1364. https://doi.org/10.1016/j.ijforecast.2021.11.013 Min, H. (2010). Artificial intelligence in supply chain management: Theory and applications. International Journal of Logistics Research and Applications, 13(1), 13–39. https://doi.org/10.1080/13675560902736537 Modgil, S., Singh, R. K., & Hannibal, C. (2022). Artificial intelligence for supply chain resilience: Learning from Covid-19. The International Journal of Logistics Management, 33(4), 1246–1268. https://doi.org/10.1108/IJLM-02-2021-0094 Nitzl, C., Roldan, J. L., & Cepeda, G. (2016). Mediation analysis in partial least squares path modeling: Helping researchers discuss more sophisticated models. Industrial Management & Data Systems, 116(9), 1849–1864. https://doi.org/10.1108/IMDS-07-2015-0302 Oroojlooyjadid, A., Snyder, L. V., & Takac, M. (2020). Applying deep learning to the newsvendor problem. IISE Transactions, 52(4), 444–463. https://doi.org/10.1080/24725854.2019.1632502 Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879 Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891. https://doi.org/10.3758/BRM.40.3.