Forecasting Irregular Demand Using Single Hidden Layer Neural Networks

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

Abstract

The inventory management and production planning of parts with irregular demand patterns are challenging for manufacturing companies. These patterns often occur in the strategically critical spare parts sector, where the inventory and capital commitment costs are high. For this reason, an accurate forecast can improve service levels and ensure efficient stock keeping. For this problem, time-series-based forecasting methods are often used to predict future demands. Furthermore, the research of recent years in terms of stochastic forecasting also focused on Artificial Intelligence (AI) methods, mainly Artificial Neural Networks (ANN). In contrast to previous studies, this paper compares the prediction results of various ANN configurations and classical forecasting methods for all of the different demand categories according to Syntetos et al. [1], which means that erratic, lumpy, smooth, and intermittent demands are regarded separately. This study compares eleven statistical forecasting configurations with eight single hidden layer neural network configurations. Furthermore, the influence of the number of hidden neurons on the prediction performance is investigated with the learning algorithms Backpropagation (BP) and Levenberg-Marquardt (LM) by evaluating them separately, which has not been covered in the context of all irregular demand categories yet. The study is based on actual demand data from 29 spare parts of a mechanical engineering company.

Details

Original languageEnglish
Article number6
Number of pages13
JournalLogistics Research
Volume15
Issue number1
Publication statusPublished - 15 Jul 2022
Peer-reviewedYes

External IDs

Scopus 85134522414
dblp journals/lr/HoffmannLM22
Mendeley 3579e3bc-55fe-32f5-9a12-eb7034fae47f

Keywords

DFG Classification of Subject Areas according to Review Boards

Subject groups, research areas, subject areas according to Destatis

Keywords

  • Artificial Intelligence, Artificial Neural Network, Irregular demand forecasting, Spare parts management