Predictive Analytics for Demand Forecasting - A Comparison of SARIMA and LSTM in Retail SCM

Research output: Contribution to journalConference articlepeer-review

2 Citations (Scopus)

Abstract

The application of predictive analytics (PA) in Supply Chain Management (SCM) has received growing attention over the last years, especially in demand forecasting. The purpose of this paper is to provide an overview of approaches in retail SCM and compare the quality of two selected methods. The data used comprises more than 37 months of actual retail sales data from an Austrian retailer. Based on this data, SARIMA and LSTM models were trained and evaluated. Both models produced reasonable to good results. In general, LSTM performed better for products with stable demand, while SARIMA showed better results for products with seasonal behavior. In addition, we compared results with SARIMAX by adding the external factor of promotions and found that SARIMAX performed significantly better for products with promotions. To further improve forecasting quality on the store level, we suggest hybrid approaches by training SARIMA(X) and LSTM on similar, pre-clustered store groups.

Original languageEnglish
Pages (from-to)993-1003
Number of pages11
JournalProcedia Computer Science
Volume200
DOIs
Publication statusPublished - 2022
Event3rd International Conference on Industry 4.0 and Smart Manufacturing, ISM 2021 - Linz, Austria
Duration: 19 Nov 202121 Nov 2021

Keywords

  • Demand Forecasting
  • Machine Learning
  • Predictive Analytics
  • Supply Chain Management

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