TY  - CONF
A1  - Samsonov, Vladimir
A1  - Enslin, Chrismarie
A1  - Luetkehoff, Ben
A1  - Steinlein, Felix
A1  - Lütticke, Daniel
A1  - Stich, Volker
A2  - Nyhuis, P.
A2  - Herberger, D.
A2  - Hübner, M.
T1  - Managing Disruptions in Production with Machine Learning
T2  - Proceedings of the Conference on Production Systems and Logistics : CPSL 2020
N2  - 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.
KW  - Disruption Management
KW  - Machine Learning
KW  - Production Control
KW  - Visual Analytics
KW  - Deviation Detection
KW  - Similarity Analysis
KW  - Decision Support
KW  - Assembly
KW  - Assistance Systems
KW  - Mixed-Model Assembly
Y1  - 2023
UR  - https://epub.fir.de/frontdoor/index/index/docId/2553
UR  - https://www.repo.uni-hannover.de/handle/123456789/9734
SP  - 360
EP  - 368
PB  - publish-Ing.
CY  - Hannover
ER  -