Managing Disruptions in Production with Machine Learning

Show simple item record

dc.identifier.uri http://dx.doi.org/10.15488/9678
dc.identifier.uri https://www.repo.uni-hannover.de/handle/123456789/9734
dc.contributor.author Samsonov, Vladimir
dc.contributor.author Enslin, Christmarie
dc.contributor.author Lütkehoff, Ben
dc.contributor.author Steinlein, Felix
dc.contributor.author Lütticke, Daniel
dc.contributor.author Stich, Volker
dc.date.accessioned 2020-03-16T15:21:41Z
dc.date.issued 2020
dc.identifier.citation 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 ger
dc.description.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. eng
dc.language.iso eng
dc.publisher Hannover : publish-Ing.
dc.relation.ispartof https://doi.org/10.15488/9640
dc.relation.ispartof Proceedings of the Conference on Production Systems and Logistics : CPSL 2020
dc.rights CC BY 3.0 DE
dc.rights.uri https://creativecommons.org/licenses/by/3.0/de/
dc.subject Disruption Management eng
dc.subject Machine Learning eng
dc.subject Production Control eng
dc.subject Visual Analytics eng
dc.subject Deviation Detection eng
dc.subject Similarity Analysis eng
dc.subject.classification Konferenzschrift ger
dc.subject.ddc 620 | Ingenieurwissenschaften und Maschinenbau ger
dc.title Managing Disruptions in Production with Machine Learning
dc.type bookPart
dc.type Text
dc.description.version publishedVersion
tib.accessRights frei zug�nglich


Files in this item

This item appears in the following Collection(s):

Show simple item record

 

Search the repository


Browse

My Account

Usage Statistics