Assessing the Integration of Artificial Intelligence and Machine Learning for Demand Forecasting and Inventory Optimization in Retail Supply Chains
Keywords:
Artificial Intelligence, Machine Learning, Demand Forecasting, Inventory Optimization, Retail Supply Chain, Predictive Analytics, Data-Driven Decision MakingAbstract
The retail supply chain faces ongoing challenges with accurate demand forecasting and efficient inventory management due to volatile market conditions, seasonal variability, and consumer behavior shifts. This paper explores the integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies into retail supply chains, with a focus on their applications in demand forecasting and inventory optimization. By reviewing existing literature and synthesizing data-driven insights, the paper demonstrates how AI/ML-based systems outperform traditional forecasting models, resulting in improved operational efficiency, reduced stockouts, and minimized holding costs. Through analysis of key studies and visualization of industry case data, we illustrate the growing significance of AI/ML in supply chain modernization.
References
Choi, T.-M., Wallace, S. W., & Wang, Y. (2018). Big Data Analytics in Operations Management. Production and Operations Management, 27(10), 1868–1889.
Waller, M. A., & Fawcett, S. E. (2013). Data Science, Predictive Analytics, and Big Data: A Revolution that will Transform Supply Chain Design and Management. Journal of Business Logistics, 34(2), 77–84.
Carbonneau, R., Laframboise, K., & Vahidov, R. (2008). Application of Machine Learning Techniques for Supply Chain Demand Forecasting. European Journal of Operational Research, 184(3), 1140–1154.
Ghosh, A. (2015). Predictive Analytics in Retail Supply Chain: A Framework for Inventory Optimization. International Journal of Supply Chain Management, 4(3), 22–29.
Ramanathan, U. (2010). The Role of Expert Systems and AI in Inventory Decision-Making. Expert Systems with Applications, 37(10), 7503–7510.
Kaur, H., & Singh, S. P. (2018). Heuristic modeling for sustainable inventory management: A hybrid machine learning approach. Computers & Industrial Engineering, 125, 588–600.
Aburto, L., & Weber, R. (2007). Improved supply chain forecasting using a hybrid intelligent model. Applied Soft Computing, 7(1), 136–144.
Wang, G., Gunasekaran, A., Ngai, E. W. T., & Papadopoulos, T. (2016). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics, 176, 98–110.
Choudhary, A., Harding, J. A., & Tiwari, M. K. (2009). Data mining in manufacturing: A review based on the kind of knowledge. Journal of Intelligent Manufacturing, 20(5), 501–521.
Yu, H., Yan, H., & Cheng, T. C. E. (2009). Modeling the benefits of information sharing in a retail supply chain. International Journal of Production Research, 47(7), 1977–1999.
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). Statistical and Machine Learning forecasting methods: Concerns and ways forward. PLOS ONE, 13(3), e0194889.
Zhang, G., Eddy Patuwo, B., & Hu, M. Y. (1998). Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting, 14(1), 35–62.
Dey, L., & Chakraborty, S. (2020). Application of machine learning techniques in supply chain management: A review and bibliometric analysis. Expert Systems with Applications, 160, 113749.
Min, H. (2010). Artificial intelligence in supply chain management: Theory and applications. International Journal of Logistics: Research and Applications, 13(1), 13–39.
Ivanov, D., & Dolgui, A. (2020). A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. Production Planning & Control, 32(9), 775–788.
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Copyright (c) 2021 Yuri Luiz (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
