Supply chain management (SCM) connects various organizations that perform operations such as financing, sourcing, production, distribution, and retail. Artificial intelligence (AI) techniques like machine learning, deep learning, optimization, and robotics can assist this balancing act. A growing number of companies have adopted AI-enabled tools and systems to manage supply chains more effectively and gain competitive advantage. Intelligent and flexible decision-support systems have been developed for forecasting, inventory management, logistics, and supplier selection. By improving consistency and accuracy across multiple decision-making levels, such systems can lead to lower costs, reduced inventory, and greater service flexibility. Comparing traditional decision-support systems with AI-augmented tools highlights the complementarity between AI and other decision-support methods. Although organizations have invested heavily in enterprise resource planning, material requirements planning, and other systems that facilitate business process integration, additional improvement opportunities remain. AI and traditional systems are compatible, as AI principles can enhance capabilities through existing data channels and processes. This compatibility facilitates adoption; traditional systems remain viable while organizations explore AI applications and experiment with emerging paradigms and technologies.

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