Estimated Stock Percentage Prediction

Identifying Unique Challenges

The retail company faced stock shortages, spoilage issues, and inaccurate inventory forecasting, especially for temperature-sensitive products. Existing systems lacked real-time insights and failed to adapt to environmental fluctuations during storage and distribution.

Meeting User Needs

This model delivered predictive inventory analytics powered by machine learning, boosting sales by 51% and significantly improving customer satisfaction. It also included seamless integration with IoT temperature sensors to monitor stock conditions in real-time, enabling proactive decision-making.

Accessibility & Optimisation

The model was designed to be lightweight, scalable, and compatible with IoT infrastructure. It ensures easy deployment across warehouses or retail chains, offering real-time data visualisation and alerts without requiring technical intervention.