The Potential of AutoML for Demand Forecasting
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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Conference 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. ed. / David Herberger; Marco Hübner. Hannover: publish-Ing., 2022. p. 682-692 (Proceedings of the Conference on Production Systems and Logistics; Vol. 3).
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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TY - CHAP
T1 - The Potential of AutoML for Demand Forecasting
AU - Kramer, Kathrin
AU - Behn, Niclas
AU - Schmidt, Matthias
N1 - 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.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Engineering
KW - AutoML
KW - Demand forecasting
KW - sales forecast
KW - Machine learning
KW - Manufacturing
KW - Production planning
UR - http://www.scopus.com/inward/record.url?scp=85164413933&partnerID=8YFLogxK
U2 - 10.15488/12162
DO - 10.15488/12162
M3 - Article in conference proceedings
T3 - Proceedings of the Conference on Production Systems and Logistics
SP - 682
EP - 692
BT - Conference on Production Systems and Logistics
A2 - Herberger, David
A2 - Hübner, Marco
PB - publish-Ing.
CY - Hannover
T2 - 3rd Conference on Production Systems and Logistics - CPSL 2022
Y2 - 17 May 2022 through 20 May 2022
ER -