Supply Chain Demand Forecasting & Inventory Optimization

This dataset and analysis were developed as part of a self-initiated Supply Chain Analytics project focused on demand forecasting and inventory optimisation for a retail distribution network. The objective was to demonstrate how open data and free analytical tools can improve forecasting accuracy, reduce stockouts, and optimise reorder policies — without any financial investment. Key Activities • Data cleaning and preparation of order, shipment, and inventory records • Exploratory analysis of sales trends and seasonality • Development of a simple forecasting model (moving average / ARIMA) • Calculation of optimal reorder points and safety-stock levels • Visualisation of performance improvements using Excel and Python dashboards Tools Used : Excel · Google Sheets · Python (pandas, matplotlib) Dataset Source Reference : SMART Supply Chain for Big Data Analysis – Mendeley Outcome : Created a repeatable learning framework for students and professionals interested in practical supply-chain analytics using free resources.