The Potential of AutoML for Demand Forecasting

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

Authors

In demand forecasting, which can depend on various internal and external factors, machine learning (ML) methods can capture complex patterns and enable precise forecasts. Accurate forecasts facilitate targeted, demand-oriented planning and control of production and underline the importance of this task. The implementation of ML-algorithms requires knowledge of the specific domain as well as knowledge of data science and involves an elaborate set up process. This often makes the application of ML to potential industrial problems economically unattractive. The major skills shortage in the field of data science further exacerbates this. Automation and better accessibility of ML methods is therefore a key prerequisite for widespread use. This is where the principle of automated ML (AutoML) comes in, automating large parts of a ML pipeline and thus leading to a reduction in human labour input. Therefore, the aim of the publication is to investigate the extent to which AutoML solutions can generate added value for demand planning in the context of production planning and control. For this purpose, publicly available datasets deriving from Walmart as well as an anonymised manufacturing company are used for short-term and long-term forecasting. The AutoML tools from Microsoft, Dataiku and Google conduct these forecasts. Statistical models serve as benchmarks. The results show that the forecasting quality varies depending on the software, the input data and their demand patterns. Overall, the prepared models from Microsoft show the most accurate results in average and the potential of AutoML becomes particularly clear in the short-term forecast. This paper enriches the research field through its broad application, giving valuable insights into the use of AutoML tools for demand planning. The resulting understanding of limitations and benefits of AutoML tools for the case studies presented fosters their suitable application in practice.
Original languageEnglish
Title of host publicationConference on Production Systems and Logistics : International Conference, CPSL 2022, hosted at the University of British Columbia in Vancouver, Canada, 17th May 2022 – 20th May 2022, Proceedings
EditorsDavid Herberger, Marco Hübner
Number of pages11
Place of PublicationHannover
Publisherpublish-Ing.
Publication date2022
Pages682-692
DOIs
Publication statusPublished - 2022
Event3rd Conference on Production Systems and Logistics - CPSL 2022 - Vancouver, Canada
Duration: 17.05.202220.05.2022
https://cpsl-conference.com/wp-content/uploads/2022/01/CPSL-2022-Call-for-Papers-extended.pdf

Bibliographical note

Funding Information:
Funded by the Lower Saxony Ministry of Science and Culture under grant number ZN3489 within the Lower Saxony “Vorab” of the Volkswagen Foundation and supported by the Center for Digital Innovations (ZDIN).

Funding Information:
Funded by the Lower Saxony Ministry of Science and Culture under grant number ZN3489 within the Lower

Publisher Copyright:
© 2022, Publish-Ing in cooperation with TIB - Leibniz Information Centre for Science and Technology University Library. All rights reserved.

    Research areas

  • Engineering - AutoML, Demand forecasting, sales forecast, Machine learning, Manufacturing, Production planning

DOI