The integration of Artificial Intelligence (AI) and Machine Learning (ML) into supply chain management has brought about a paradigm shift in decisionmaking processes within the field of industrial engineering. Traditional supply chain systems relied heavily on historical data, manual processes and human judgment to make critical decisions regarding inventory, production schedules, logistics and demand forecasting. However, with the advancements in AI and ML technologies, industrial engineers now have access to powerful tools that enhance their ability to make data-driven decisions in real time, improve efficiency, reduce costs and ultimately increase competitiveness in the marketplace. AI and ML offer several capabilities that can optimize supply chain operations. One of the primary advantages is their ability to process and analyze large volumes of complex data in a fraction of the time it would take a human. AI algorithms can be applied to historical data, market trends and external factors to identify patterns and forecast future demand with high accuracy. This predictive capability allows industrial engineers to make more informed decisions regarding production, inventory levels and distribution strategies, ensuring that resources are allocated more efficiently and minimizing the risk of stockouts or overstocking.
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