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Part based decentralized information handling for process improvements along the supply chain

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

Authors

"Gentelligence®" is a neologism which consists of the two words "genetic" and "intelligent". Gentelligent ® Parts are basically components which are able to save information. The special and specific property is that they have both the ability to save and store information and to handing it over to the next component generation. Lifecycle information is saved in a continuous process. Due to these characteristics, Gentelligent® Parts can serve as a decentralized Enterprise Information System (EIS). As a result, it is possible to identify extensive potential for improving logistic and technical processes arising along the product evolutionary process and during the usage phase. This article provides insight into both the principle of Gentelligent® Parts as well as the corresponding data structure and targeted process improvements.

Original languageEnglish
Title of host publication Industrial Engineering and Engineering Management 2008
Number of pages5
PublisherIEEE - Institute of Electrical and Electronics Engineers Inc.
Publication date01.12.2008
Pages1073-1077
ISBN (print)978-1-4244-2629-4
ISBN (electronic)978-1-4244-2630-0
DOIs
Publication statusPublished - 01.12.2008
Externally publishedYes
EventInternational Conference on Industrial Engineering and Engineering Management - 2008 - , Singapore
Duration: 08.12.2008 → 11.12.2008
https://www.tib.eu/en/search/id/TIBKAT%3A59962924X/IEEM-2008-the-IEEE-International-Conference-on/

    Research areas

  • Information processing and engineering, Intelligent systems, Supply chain management

Other publications by the same author(s)

Field-Based Assessment of Key Influence Factors for Throughput Time Prediction in Job Shop Manufacturing

Reinhold, J., Rokoss, A. & Schmidt, M., 2026, Advances in Production Management Systems. Cyber-Physical-Human Production Systems: Human-AI Collaboration and Beyond - 44th IFIP WG 5.7 International Conference, APMS 2025, Proceedings. Mizuyama, H., Morinaga, E., Kaihara, T., Nonaka, T., von Cieminski, G. & Romero, D. (eds.). Springer Science and Business Media Deutschland, p. 201-215 15 p. (IFIP Advances in Information and Communication Technology; vol. 769 IFIPAICT).

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

Implementierung von Produktrückführungsprozessen in bestehende Produktionsabläufe: Herausforderungen und Anforderungen

Sandner, S., Nübel, M., Schulz, J., Metternich, J. & Schmidt, M., 20.06.2025, In: ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb. 120, 6, p. 397-403 7 p.

Research output: Journal contributions › Journal articles › Research › peer-review

Machine Learning For Determining Planned Order Lead Times In Job Shop Production: A Systematic Review Of Input Factors And Applied Methods

Wolter, F., Rokoss, A. & Schmidt, M., 2025, Conference on Production Systems and Logistics: University of San Ignacio de Loyola Lima, Peru, 18th – 21th March 2025, Proceedings. Herberger, D. & Hübner, M. (eds.). Offenburg: publish-Ing., p. 374-384 11 p. 32. (Proceedings of the ... Conference on Production Systems and Logistics; no. 7).

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

Understanding Resulting Interdependencies Within Production Planning And Control In Hybrid Manufacturing/Remanufacturing Systems

Schulz, J., Hofmann, H., Rokoss, A. & Schmidt, M., 05.2025, Proceedings of the Conference on Production Systems and Logistics: CPSL 2025: 18th – 21th March 2025, University of San Ignacio de Loyola Lima, Peru. Herberger, D. & Hübner, M. (eds.). Offenburg: publish-Ing., p. 114 - 126 13 p. (Proceedings of the ... Conference on Production Systems and Logistics; vol. 7).

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

Analysis of the relevance of models, influencing factors and the point in time of the forecast on the prediction quality in order-related delivery time determination using machine learning

Rokoss, A., Popkes, L. & Schmidt, M., 2024, Proceedings of the CPSL 2024. Herberger, D. & Hübner, M. (eds.). Hannover: publish-Ing., p. 415-431 17 p. (Proceedings of the Conference on Production Systems and Logistics; vol. 2024).

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

DOI

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