DEVELOPMENT OF AN INTERACTIVE DASHBOARD AND EST-BASED FORECASTING MODEL FOR REAL-TIME DECISION MAKING IN SUPPLY CHAIN MANAGEMENT.

Abstract: The objective of the project is to develop an Interactive Dashboard for Supplier Weight and Delivery Weight Forecasting using an ETS (Error, Trend, Season) model built using Python to support data-driven decision making within a large engineering manufacturing organisation. The study comprises two integrated components designed to illustrate how visualization and forecasting can facilitate improvements in supply chain monitoring and planning. The organization relies on consistent supplier deliveries and accurate weight forecasting based on historical data for its large-scale manufacturing operations. The manual evaluation of supplier on-time delivery performance and weight of deliveries was often ineffectual and could lead to poor speculation .An Interactive Dashboard was generated in Power BI to visualize supplier performance and delivery timelines. A Forecasting Model was developed in Python with an ETS model predicting the weight of delivery in the future based on historical trends. Combining the analytical power of the Python statistical tool with the data visualization through Microsoft Power BI will help provide actionable insights to improve supply chain management processes and how decisions will be made in the future. The use of an interactive dashboard allowed all users to see how suppliers are performing and the trend regarding weights, and with Python's ETS modelling, a clear prediction of the future delivery weights enables faster decision-making and would lead to increased transparency while using data-informed planning methods to transform supply chain operations.

Keywords: Interactive dashboard, time series forecasting, ETS model, supply chain management, Power BI, Python.