MUMBAI, India, Nov. 2 -- Intellectual Property India has published a patent application (202641124508 A) filed by Mlr Institute Of Technology; and Marri Laxman Reddy Institute Of Technology And Management on October 17, 2026, for Machine Learning-Based Material Demand Forecasting And Supply Chain Optimization System For Quick Commerce Applications.
Inventors include Dr. J Mahalakshmi; Ms. M. Srinija; Ms. K. Meghana; and Mr. Vishnu Sreyash.
The application for the patent was published on October 30, 2026, under issue no. 44/2026.
Abstract: The present invention relates to a machine learning-based material demand forecasting and supply chain optimization system for quick-commerce applications. The system is configured to predict product demand and optimize inventory management and replenishment operations using historical sales data, customer orders, inventory levels, product information, pricing, promotional activities, seasonal variations, and other demand-related parameters. The system comprises a data acquisition module configured to collect relevant operational data, a data preprocessing module configured to process and organize the collected data, and a machine learning-based forecasting module configured to identify demand patterns and predict future product requirements for different products and fulfilment locations. An inventory optimization module processes the predicted demand to determine inventory levels, replenishment quantities, safety stock requirements, and product allocation. The system further comprises an alert and recommendation module configured to generate demand forecasts, inventory alerts, and replenishment recommendations, and a visualization interface configured to display forecasting results and supply chain performance information. The system facilitates data-driven inventory planning, reduces the risk of stockouts and overstocking, minimizes inventory-related costs and material wastage, and improves product availability and operational efficiency in quick-commerce environments.
Disclaimer: Curated by HT Syndication.
