Optimizing Inventory Process using Physics Informed ML

Improving asset efficiency, through research in inventory management across various MSME segments The project is particularly suited for manufacturing MSMEs with multi-stage production processes, such as foundries, machining units, textiles, food processing, and FMCG sectors. These segments often grapple with balancing raw material procurement, work-in-progress bottlenecks, and finished goods demand volatility. The model’s compartmental structure aligns with their production lifecycle, enabling precise tracking and optimization of inventory across stages. For instance, foundries managing metal casting (raw → molten metal → finished parts) or food processors handling perishable ingredients benefit from AI- driven demand forecasting and cost-minimization. These industries face high risks of stockouts, overstocking, or resource wastage, making the framework’s real-time adjustments and holistic cost optimization critical for enhancing operational efficiency and resilience.

“This AI-driven inventory management solution can significantly reduce operational costs (15– 20%) by optimizing stock levels and procurement strategies. Its real-time adaptability, powered by IoT integration, can ensure swift responses to demand shifts and supply chain disruptions.”
Dr. Manoj Kumar
Assistant Professor School of Engineering and Science , IIT-Madras Zanzibar

Key Features of the Project

AI Pontryagin Optimization: Combines Pontryagin’s Maximum Principle with neural networks for dynamic, system-wide inventory control across raw materials, work-in progress, and finished goods.

Holistic Compartmental Modeling: Simultaneously tracks all inventory stages using epidemiology-inspired models, avoiding siloed decision-making.

Real-Time Supply Chain Synchronization: Coordinates production, inventory, and delivery plans globally with alerts for stockouts, overstocking, or disruptions.

LSTM Demand Forecasting: Predicts demand trends via Long Short-Term Memory networks to optimize

Scroll to Top