Demand Forecasting in Supply Chain Management for Rossmann Stores Using Weather Enhanced Deep Learning Model

원문 초록

Demand forecasting is one of the essential aspects of supply chain management, as it is linked with the financial performance of the organization. In the retail industry, it is essential to have more accurate forecasts to make suitable decisions. Therefore, the selection of the right forecasting method is considered vital and ideal to meet customer needs. More precisely, this research paper focuses on developing forecasting model for 1115 Rossmann stores located in Europe. Although, previously researchers have been working on developing models to forecast sales demand and to improve accuracy. However, it has been observed that few of the necessary conditions or situations were not being catered for in sales demand forecasting. Such as most researchers used univariate data of total sales for forecasting demand. The internal and external factors such as weather, promotional activity, location of the store, and holidays also play one of the primary roles when it comes to sales demand to forecast. Therefore, it is not specifically a univariate problem but a multivariate problem which have been analyzed in this research. In this research, multivariate dataset including weather variables, other important features have been used in predicting sales demand in supply chain management which helped to achieve better and reliable results. An enhanced deep learning model for sales Demand Forecasting using Weather Data (SDFW) is proposed using Gated Recurrent Unit (GRU) with Grid search. The proposed approach GRU with Grid search showed better performances as compared to previously suggested Long Short Term Memory (LSTM) model. Moreover, Gated Recurrent Unit (GRU) with Grid Search showed significant improvement in sales demand forecasting accuracy when considering weather-related data subsets. These findings will help the Rossmann retail industry in predicting the upcoming sales demand in a more efficient way, which will also optimize their inventory records.