AI-Driven Predictive Maintenance System for Retail

This document is a case study that outlines the development of an AI-driven Predictive Maintenance System tailored for a leading provider of store management technology focused on fresh foods. The objective was to enhance inventory management, maintain product safety, reduce waste, and improve operational efficiency in a sector where product shelf-life averages between 2-5 days. The solution integrated external data sources such as seasonal data, demographic insights, and promotions data into a user interface application, leveraging Azure Cloud infrastructure. MLOps on Azure were deployed to reduce manual dependencies and project lifecycle time, while an auto-training feature was implemented to address challenges like data drifting and prediction inaccuracies. The technology stack utilized Azure ML and Power BI, enabling advanced analytics and machine learning capabilities. The case study also presents significant benefits, including an 80% improvement in stockouts and a 70% control on wastage.