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Method for Semi-Automated Improvement of Smart Factories Using Synthetic Data and Cause-Effect-Relationships

  • Smart factories, driven by the integration of automation and digital technologies, have revolutionized industrial production by enhancing efficiency, productivity, and flexibility. However, the optimization and continuous improvement of these complex systems present numerous challenges, especially when real-world data collection is time-consuming, expensive, or limited. In this paper, we propose a novel method for semi-automated improvement of smart factories using synthetic data and cause-effect-relations, while incorporating the aspect of self-organization. The method leverages the power of synthetic data generation techniques to create representative datasets that mimic the behaviour of real-world manufacturing systems. These synthetic datasets serve together with the cause-and-effect relationships as a valuable resource for factory optimization, as they enable extensive experimentation and analysis without the constraints of limited or costly real-world data. Furthermore, the method embraces the concept of self organization within smart factories. By allowing the system to adapt and optimize itself based on feedback from the synthetic data, cause-effect-relationships, the factory can dynamically reconfigure and adjust its processes. To facilitate the improvement process, the method integrates the synthetic data with advanced analytics and machine learning algorithms as well as and the cause-and-effect relationships. This synergy between human expertise and technological advancements represents a compelling path towards a truly optimized smart factory of the future.

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Author of HS ReutlingenHummel, Vera; Schuhmacher, Jan
Erschienen in:Proceedings of the Conference on Production Systems and Logistics: CPSL 2023-2
Publisher:Technische Informationsbibliothek
Place of publication:Hannover
Document Type:Conference proceeding
Publication year:2023
Page Number:11
First Page:371
Last Page:381
DDC classes:620 Ingenieurwissenschaften und Maschinenbau
Open access?:Ja
Licence (German):License Logo  Creative Commons - Namensnennung