Multi-Modal Sensing for Real-Time Operations Monitoring in Physical Retail: A Framework Combining Computer Vision, IoT Telemetry, and Transactional Signals

Physical retail stores continue to generate a substantial share of total revenue in many categories, but the operational visibility available to store managers still lags far behind what digital channels provide. Workforce activity, opening and closing routines, customer density, checkout flow, fitting-room dynamics, and after-hours store activity are typically inferred from manual observation or sales proxies rather than measured continuously. As a consequence, structural inefficiencies often remain invisible until they show up as lost sales or compliance incidents. This study reports on a multi-modal sensing framework that narrows this gap by repurposing in-store infrastructure already in place for unrelated reasons: security cameras, point-of-sale terminals, door and turnstile counters, energy and lighting sensors, fitting-room occupancy signals, and the corporate time-attendance system. Signals from these sources feed a shared operational layer connected to a real-time alerting and feedback mechanism. The framework was deployed in twelve pilot stores of a large omnichannel apparel and footwear retailer over a six-month window, under a privacy-preserving, edge-inference architecture. A directional, observational analysis indicates that idle staff time during peak hours fell by around 20%, coverage of previously underserved zones grew by close to 30%, unrecorded early closures dropped sharply (about 81%), average checkout wait times shortened by roughly a quarter, fitting-room abandonment decreased by approximately 20%, and staff time-attendance discrepancies fell by close to 60%. Two stores diverged from this pattern, illustrating that camera placement and sensor coverage materially affect what such a framework can deliver.