Forecasting Sales in the Supply Chain Based on the LSTM Network: The Case of Furniture Industry

Abstract

Purpose: The aim of the article is to develop an algorithm for forecasting sales in the supply chain based on the LSTM network using historical sales data of a furniture industry company. Design/Methodology/Approach: Machine learning was used to analyze the data. The method of predicting the behavior of sales value in a specific time horizon in terms of a time series was presented. The LSTM network was used to predict sales. The network used is a special case of recursive neural networks with an important difference in the repeating module. Due to the fact that the activities are carried out on time series, the data was analyzed in terms of the stationarity of such series or trends and seasonal effects. The data used in the analysis includes the daily sales values of a group of certain furniture collections over a specified time horizon. The stationarity of the time series can have a significant impact on its properties and behavior prediction, where failure to bring the time series to the correct form of stationarity can lead to false results. Findings: The result of the research was the analysis of sales forecasting in the supply chain based on machine learning. As a result of the data transformations, the algorithm was able to recognize and learn long-term relationships. Practical Implications: The presented method of predicting the behavior of sales value in a specific time horizon allows for a look at the forecasting of demand in terms of the supply chain. The sales data of a company from the furniture industry were used for the analysis. Originality/Value: A novelty is the use of the LSTM network trained on real transaction data of a furniture company that has based its business on the supply chain and cooperates with its suppliers and recipients in Central and Eastern Europe.