ANALYZING THE EFFICACY OF ML APPROACHES IN INVENTORY FORECASTING: AN IN-DEPTH COMPARISON OF LSTM, PROPHET, AND ENSEMBLE APPROACHES

In retail business, stock levels optimization is one of the significant aspects, where changes can have a great impact on profitability and customer satisfaction.for this purpose, this paper introduces the RetailRevolutionizer: A Machine Learning-based Inventory Demand Forecasting tool designed for compar-ison of three advanced models namely LSTM, Prophet, and Prophet + SARIMA, by evaluating each model's performance in terms of forecasting accuracy, RetailRevolutionizer provides insights into their strengths and limitations.The LSTM captures the long-term dependencies effectively, while SARIMA deals well with seasonality.Prophet specifically specializes in trend decomposition and anomaly detection The overall tool imple-mentation consists of data cleaning, trend analysis, study of sales distribution, and detecting anomalies.Through the trends for sales clustering and sales time-series decomposition, RetailRev-olutionizer delivers actionable insights into sales variability and peak periods.It has proven that improvements in every model are appreciated by the augmentation in forecasting accuracy and avoided cases of overstocking and under-stocking.This ensures better inventory turns and increases connectivity with customers' demand, making the overall operating efficiency better.