Prediction of customer demand for perishable products in retail inventory management, using the hybrid prophet-XGBoost model during the post-COVID-19 period

Abstract

Retail inventory management (IM) presents challenges in decision-making, especially in determining optimal inventory levels for future customer demand alignment. This study focuses on predicting dairy product demand by a significant European retail company using historical order data. As dairy products are perishable and constitute a relevant portion of retail trade volume, accurate IM is crucial to prevent wastage and meet customer needs. However, external factors like COVID-19 may impact demand volatility and seasonal patterns. Given the absence of scientific studies on predicting post-COVID-19 demand, our research aims to fill this gap by proposing alternatives, such as excluding the pandemic period and developing a historical imputation. The study employs two predictive models, Prophet and XGBoost, known for their superior performance in predicting demand. Moreover, a hybrid approach combining both models is proposed to enhance prediction accuracy by leveraging the capacity of the Prophet model to handle seasonal and holiday-period effects and XGBoost’s regularization to prevent overfitting. The results demonstrate the feasibility of historical imputation and the hybrid model approach, improving significantly individual model performance. The principal application of the study is to propose an approach to predict the shipment of other products in a post-COVID-19 context.