Retail Demand Forecasting and Reorder Assistant
Temporal Fusion Transformer (TFT) & Prophet time-series sales forecasting model for retail stock optimization.
Project Overview
Evaluates historical SKU sales data, seasonal trends, weather variables, and promotional events to predict product demand 30 days ahead, minimizing stockouts and overstock holding costs. ### Practical Student Engineering Solution Fully functional hardware demonstration prototype equipped with dedicated microcontrollers, precision sensors, actuators, and an interactive cloud/mobile telemetry dashboard. ### Key Learning Outcomes - Embedded C / MicroPython firmware architecture and sensor interfacing - IoT telemetry protocols (MQTT / HTTP / BLE / LoRaWAN) - Power regulation, hardware debugging, and PCB design principles - Comprehensive technical documentation aligned with IEEE academic standards
Modern commercial and industrial infrastructure requires automated, real-time sensing and telemetry. Conventional manual monitoring suffers from high latency, human error, and lack of predictive fault visibility for Retail Demand Forecasting and Reorder Assistant.
A turnkey engineering prototype integrating high-precision sensor modules, dedicated microcontroller unit, and cloud telemetry protocols to automate measurement, trigger alert thresholds, and provide remote dashboard control.