High Frequency Demand Forecasting : The Case of a Swedish Pharmacy Retailer

Abstract: Predicting future sales can bring many advantages to retailers with regards to organizational performance. Using big data to make accurate forecasts can enable retailer to improve their operational performance and profitability substantially by reducing lost sales, inventory levels and labor costs. Previous research within the field of retail forecasting has mostly been dedicated to forecasting on lower time granularities such as weekly and monthly. However, despite the high practicality for retailers, forecasts on higher frequencies have not been properly covered by the current literature. This study aims to investigate how to forecast future sales using high-frequency data for a Swedish pharmacy retail chain. The forecasts are made on a daily and sub-daily time granularity using time series models SARIMA, Holt-Winter’s method and Facebook Prophet. The results show that Facebook Prophet was the most practical model and had the highest forecasting accuracy both on a daily and sub-daily frequency according to the error metrics MAPE, MAE and RMSE.