Hummel, V.; Schuhmacher, J.: Method for Semi-Automated Improvement of Smart Factories Using Synthetic Data and Cause-Effect-Relationships. In: Herberger, D.; Hübner, M. (Eds.): Proceedings of the Conference on Production Systems and Logistics: CPSL 2023 - 2. Hannover : publish-Ing., 2023, S. 371-381. DOI: https://doi.org/10.15488/15310
Abstract: | |
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. | |
License of this version: | CC BY 3.0 DE |
Document Type: | BookPart |
Publishing status: | publishedVersion |
Issue Date: | 2023 |
Appears in Collections: | Proceedings CPSL 2023 - 2 Proceedings CPSL 2023 - 2 |
pos. | country | downloads | ||
---|---|---|---|---|
total | perc. | |||
1 | ![]() |
Germany | 54 | 58.06% |
2 | ![]() |
United States | 14 | 15.05% |
3 | ![]() |
China | 4 | 4.30% |
4 | ![]() |
India | 3 | 3.23% |
5 | ![]() |
France | 3 | 3.23% |
6 | ![]() |
South Africa | 2 | 2.15% |
7 | ![]() |
Taiwan | 2 | 2.15% |
8 | ![]() |
Greece | 2 | 2.15% |
9 | ![]() |
No geo information available | 1 | 1.08% |
10 | ![]() |
Netherlands | 1 | 1.08% |
other countries | 7 | 7.53% |
Hinweis
Zur Erhebung der Downloadstatistiken kommen entsprechend dem „COUNTER Code of Practice for e-Resources“ international anerkannte Regeln und Normen zur Anwendung. COUNTER ist eine internationale Non-Profit-Organisation, in der Bibliotheksverbände, Datenbankanbieter und Verlage gemeinsam an Standards zur Erhebung, Speicherung und Verarbeitung von Nutzungsdaten elektronischer Ressourcen arbeiten, welche so Objektivität und Vergleichbarkeit gewährleisten sollen. Es werden hierbei ausschließlich Zugriffe auf die entsprechenden Volltexte ausgewertet, keine Aufrufe der Website an sich.