Inventory Sales and Demand Forecasting Using Machine Learning

Demand forecasting is a well-liked inventory management tool many companies are considering due to its influence on daily operations. To improve inventory management and forecast future customer demand, supermarket run-centers, like Big Marts, now track sales data for every item. Anomalies and general patterns are commonly discovered by searching through the data warehouse's data storage. Using a variety of machine-learning approaches, Big Mart and other merchants can utilize the gathered data to predict future sales volume. This study developed a predictive model that predicted sales for businesses such as Big-Mart using Random Forest (RFR) and Ridge regression (RR) techniques. It is crucial first to examine the data and its qualities to model the most accurate forecasts, as no single forecasting approach performs better in every circumstance. Our results demonstrate the superior performance of RFR Regression compared to the RR approach with MAPE of 6.01%, RMSE of 1.93%, MAE of 1.75%, and R2 of 0.821. These findings can enable managers to design precise inventory management strategies that will enhance productivity across all service areas in the future, as they are able to juxtapose these findings with actual business operations.