Developing Demand Forecasting and Inventory Optimization Using Retail POS Data

Objective: To build a predictive demand-forecasting model and recommend optimal inventory levels for a retail chain using POS transaction data.

Indian retail chains face significant challenges in predicting product demand accurately and optimizing inventory, leading to either stockouts or overstocking. With the proliferation of digital POS systems, there is an opportunity to leverage transaction data to improve operational efficiency.

The project approach involves extracting and analyzing historical POS data from an Indian retail chain, applying statistical and machine learning techniques to forecast demand, and using supply chain frameworks to recommend inventory levels. The methodology includes data cleaning, feature engineering, model development, validation, and scenario analysis.

Deliverables include a robust demand forecasting model, inventory optimization recommendations, dashboards for visual insights, and a business report detailing actionable strategies. The analysis will cover seasonality, trends, and SKU-level granularity.

The business impact is reduced inventory holding costs, improved product availability, and data-driven decision-making for procurement managers. The recommendations will inform inventory planning and replenishment strategies, ultimately enhancing profitability and customer satisfaction.