Towards a Decision-Support Framework: AI Applications in Supply Chain Optimization

The complexity of modern supply networks requires decision tools that combine predictive power with human judgment. This paper presents a synthesized, original framework for integrating artificial intelligence into supply chain decision making. It examines AI applications in demand forecasting, inventory control, logistics, supplier risk evaluation, and production scheduling, then proposes a four-layer decision-support architecture that balances automated analytics with managerial oversight. The framework emphasizes explainability, data governance, and workforce readiness to mitigate ethical and operational risks. Practical recommendations guide organizations seeking resilient, sustainable, and transparent AI-assisted supply chains.