Managing Disruptions in Production with Machine Learning

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Samsonov, Vladimir; Enslin, Christmarie; Lütkehoff, Ben; Steinlein, Felix; Lütticke, Daniel; Stich, Volker: Managing Disruptions in Production with Machine Learning. In: Nyhuis, P.; Herberger, D.; Hübner, M. (Eds.): Proceedings of the Conference on Production Systems and Logistics : CPSL 2020. Hannover : publish-Ing., 2020, S. 360-368. DOI: https://doi.org/10.15488/9678

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Sum total of downloads: 787




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Abstract: 
Changing customer demands lead to increasing product varieties and decreasing delivery times, which in turn pose great challenges for production companies. Combined with high market volatility, they lead to increasingly complex and diverse production processes. Thus, the susceptibility to disruptions in manufacturing rises, turning the task of Production Planning and Control (PPC) into a complex, dynamic and multidimensional problem. Addressing PPC challenges such as disruption management in an efficient and timely manner requires a high level of manual human intervention. In times of digitization and Industry 4.0, companies strive to find ways to guide their workers in this process of disruption management or automate it to eliminate human intervention altogether. This paper presents one possible application of Machine Learning (ML) in disruption management on a real-life use case in mixed model continuous production, specifically in the final assembly. The aim is to ensure high-quality online decision support for PPC tasks. This paper will therefore discuss the use of ML to anticipate production disruptions, solutions to efficiently highlight and convey the relevant information, as well as the generation of possible reaction strategies. Additionally, the necessary preparatory work and fundamentals are covered in the discussion, providing guidelines for production companies towards consistent and efficient disruption management.
License of this version: CC BY 3.0 DE
Document Type: BookPart
Publishing status: publishedVersion
Issue Date: 2020
Appears in Collections:Proceedings CPSL 2020
Proceedings CPSL 2020

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downloads by country:

pos. country downloads
total perc.
1 image of flag of Germany Germany 462 58.70%
2 image of flag of United States United States 46 5.84%
3 image of flag of Russian Federation Russian Federation 46 5.84%
4 image of flag of Czech Republic Czech Republic 41 5.21%
5 image of flag of India India 23 2.92%
6 image of flag of Indonesia Indonesia 14 1.78%
7 image of flag of France France 12 1.52%
8 image of flag of Poland Poland 10 1.27%
9 image of flag of United Kingdom United Kingdom 10 1.27%
10 image of flag of Canada Canada 9 1.14%
    other countries 114 14.49%

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