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AI-driven decision support system improves stockouts and safety stock in supply chains, indicating greater efficiency.
The Self-Optimizing Supply Chain Agent (SOSA) is an AI-driven decision-support system that enhances manufacturing efficiency through autonomous forecasting, inventory optimization, and supplier performance evaluation. The project integrates machine learning techniques, particularly Linear Regression, within a Streamlit-based ERP-style dashboard to deliver actionable insights in real time. SOSA addresses supply chain inefficiencies such as stockouts, supplier delays, and demand volatility by forecasting demand, computing safety stock, and generating procurement alerts. Its modular architecture and light-mode UI make it ideal for scalable deployment and academic demonstration, while its data-driven approach provides the foundation for future integration with enterprise ERP/WMS systems.
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Sabarinath et al. (2025) studied this question.
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