Abstract
Operating in an environment characterised by uncertainty poses challenges for companies. machine learning (ML) methods, with the inclusion of external factors, offer the possibility of producing long-term demand forecasts more precisely than conventional statistical forecasting methods. In this paper, the potential of ML with the inclusion of leading indicators (e. g. economic data) for the demand forecasts of one product of a chemical company is shown.
| Translated title of the contribution | ML-based Demand Forecast with External Factors |
|---|---|
| Original language | German |
| Journal | ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb |
| Volume | 118 |
| Issue number | 5 |
| Pages (from-to) | 324-329 |
| Number of pages | 6 |
| ISSN | 0947-0085 |
| DOIs | |
| Publication status | Published - 16.05.2023 |
Bibliographical note
Publisher Copyright:© 2023 Walter de Gruyter GmbH, Berlin/Boston, Germany.
Research areas and keywords
- Engineering
- Demand Forecasting
- time series analysis
- artificial intelligence
- Machine Learning
- External factors
ASJC Scopus Subject Areas
- Strategy and Management
- Engineering(all)
- Management Science and Operations Research
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