Milk booth prediction using data science

The dairy supply chain, particularly in regional markets, frequently faces operational challenges stemming from outdated manual processes and erratic retailer demand patterns. These inefficiencies can lead to both overproduction and understocking, ultimately affecting profitability and resource utilization. In response to these persistent issues, this paper presents the development and implementation of a real-time, application-driven solution designed to modernize the supply chain for milk product distributors. The proposed system integrates Firebase for robust cloud-based storage and realtime database functionalities, paired with a React based frontend to enable a seamless, interactive user experience. This technology stack allows suppliers to monitor supply movements, retailer orders, and inventory changes in real time. One of the core strengths of the system lies in its predictive analytics capability, which employs time-series forecasting algorithms to anticipate retailer demands on both a weekly and monthly basis. These forecasts support proactive inventory management and demand planning, ensuring optimal stock levels and minimizing waste.