Abstract
Industry 4.0 and recent advances in modern devices, the increase in computing capacity, new tools, techniques, and technologies in areas of development in Data Science and Artificial Intelligence, have opened new opportunities to solve complex problems that had not been possible to address due to limitations in the use of predecessor technological tools. Machine learning algorithms and techniques developed for traditional data or large-scale intelligent data analysis in industrial applications, provide a powerful tool for forecasting and planning demand to reduce uncertainty and improve customer service level and control inventories to reduce costs. This article analyzes the contributions of the literature that address the problems of Supply Chain Inventory Management with the use of a Demand-Driven methodology supported using new technologies mainly by Machine Learning and in certain cases the use of Big Data. The demand-driven methodology offers a market-sensitive approach to managing inventory, addressing supply and demand uncertainties, and enhancing the traditional methodology for forecasting demand. The literature review covers current problems, explaining the contexts, cases, and applications of the topics in real world problems and their implementation with their results. The Literature Review Methodology is explained and finally we analyze and discuss the findings of the literature and future research opportunities.
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Garinian, E.O.M., Fierro, T.E.S., Saucedo, J.A.M., Aguilar, R.R. (2023). Machine Learning Applications for Demand Driven in Supply Chain: Literature Review. In: Smart Applications with Advanced Machine Learning and Human-Centred Problem Design. ICAIAME 2021. Engineering Cyber-Physical Systems and Critical Infrastructures, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-031-09753-9_59
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DOI: https://doi.org/10.1007/978-3-031-09753-9_59
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