Advanced Analytics for Retail Inventory and Demand Forecasting

Abstract

Achieving operational efficiency and enhancing customer satisfaction levels in the retail sector is directly dependent on efficient inventory management and accurate demand forecasting. The following study employs advanced analytics techniques, such as time series forecasting and machine learning, to bolster these essential functions. By leveraging on historical sales data from 45 retail stores sourced by Kaggle, this paper has constructed predictive models with the aim to optimize inventory levels and forecast demand with precision. The Seasonal AutoRegressive Integrated Moving Average with Exogenous Regressors (SARIMAX) model employed in this study adequately captures linear dependencies and seasonal patterns, while Long Short-Term Memory (LSTM) networks are responsible for the management of intricate, non-linear dependencies. The findings from this study depict the significant seasonal trends, the impact of economic factors, the impact of economic variables and the effectiveness of hybrid models in improving forecast accuracy. The integration of such advanced methodologies clearly highlights their massive potential in improving aspects such as inventory management and operational efficiency in the retail domain.