Autonomous inventory Intelligence: ML-driven predictive and prescriptive analytics for supply chain optimization

Duttyal, Shikha (2025) Autonomous inventory Intelligence: ML-driven predictive and prescriptive analytics for supply chain optimization. World Journal of Advanced Engineering Technology and Sciences, 15 (3). pp. 1739-1746. ISSN 2582-8266

Artificial intelligence and machine learning technologies have transformed supply chain management through the integration of predictive demand forecasting with prescriptive inventory optimization. Modern ML algorithms process diverse data streams—from historical sales and promotions to external factors like weather patterns and market trends—to generate significantly more accurate demand predictions than conventional methods. Building on these forecasts, prescriptive analytics dynamically optimize inventory parameters across multi-echelon supply chains, simulating scenarios to balance service levels against holding costs. These integrated systems enable real-time automation of procurement decisions with continuous model refinement through feedback loops. Implementations across retail, manufacturing, and logistics sectors demonstrate substantial improvements in operational metrics, with various platforms offering distinctive capabilities for specific industry contexts. The evaluation of performance outcomes identifies key integration challenges with existing ERP ecosystems while highlighting operational resilience benefits in dynamic global markets. The transition toward autonomous supply chain management represents a fundamental advancement in operational capability that addresses contemporary volatility in global supply networks.