The Potential of AutoML for Demand Forecasting

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Kramer, K.J.; Behn, N.; Schmidt, M.: The Potential of AutoML for Demand Forecasting. In: Herberger, D.; Hübner, M. (Eds.): Proceedings of the Conference on Production Systems and Logistics: CPSL 2022. Hannover : publish-Ing., 2022, S. 682-692. DOI:

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Sum total of downloads: 321

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.
License of this version: CC BY 3.0 DE
Document Type: BookPart
Publishing status: publishedVersion
Issue Date: 2022
Appears in Collections:Proceedings CPSL 2022

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pos. country downloads
total perc.
1 image of flag of Germany Germany 125 38.94%
2 image of flag of United States United States 37 11.53%
3 image of flag of Turkey Turkey 24 7.48%
4 image of flag of Brazil Brazil 21 6.54%
5 image of flag of India India 15 4.67%
6 image of flag of Poland Poland 11 3.43%
7 image of flag of Philippines Philippines 9 2.80%
8 image of flag of France France 7 2.18%
9 image of flag of Austria Austria 6 1.87%
10 image of flag of China China 5 1.56%
    other countries 61 19.00%

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