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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

Standard

Part based decentralized information handling for process improvements along the supply chain. / Schmidt, Matthias; Hartmann, Wiebke; Nyhuis, Peter.
Industrial Engineering and Engineering Management 2008. IEEE - Institute of Electrical and Electronics Engineers Inc., 2008. p. 1073-1077 (IEEM).

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

Harvard

Schmidt, M, Hartmann, W & Nyhuis, P 2008, Part based decentralized information handling for process improvements along the supply chain. in Industrial Engineering and Engineering Management 2008. IEEM, IEEE - Institute of Electrical and Electronics Engineers Inc., pp. 1073-1077, International Conference on Industrial Engineering and Engineering Management - 2008, Singapore, 08.12.08. https://doi.org/10.1109/IEEM.2008.4738035

APA

Schmidt, M., Hartmann, W., & Nyhuis, P. (2008). Part based decentralized information handling for process improvements along the supply chain. In Industrial Engineering and Engineering Management 2008 (pp. 1073-1077). (IEEM). IEEE - Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IEEM.2008.4738035

Vancouver

Schmidt M, Hartmann W, Nyhuis P. Part based decentralized information handling for process improvements along the supply chain. In Industrial Engineering and Engineering Management 2008. IEEE - Institute of Electrical and Electronics Engineers Inc. 2008. p. 1073-1077. (IEEM). doi: 10.1109/IEEM.2008.4738035

Bibtex

@inbook{6778a867623a44c591150c24bb667775,
title = "Part based decentralized information handling for process improvements along the supply chain",
abstract = "{"}Gentelligence{\textregistered}{"} is a neologism which consists of the two words {"}genetic{"} and {"}intelligent{"}. Gentelligent {\textregistered} 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{\textregistered} 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{\textregistered} Parts as well as the corresponding data structure and targeted process improvements.",
keywords = "Information processing and engineering, Intelligent systems, Supply chain management",
author = "Matthias Schmidt and Wiebke Hartmann and Peter Nyhuis",
year = "2008",
month = dec,
day = "1",
doi = "10.1109/IEEM.2008.4738035",
language = "English",
isbn = "978-1-4244-2629-4 ",
series = "IEEM",
publisher = "IEEE - Institute of Electrical and Electronics Engineers Inc.",
pages = "1073--1077",
booktitle = "Industrial Engineering and Engineering Management 2008",
address = "United States",
note = "International Conference on Industrial Engineering and Engineering Management - 2008, IEEM 2008 ; Conference date: 08-12-2008 Through 11-12-2008",
url = "https://www.tib.eu/en/search/id/TIBKAT%3A59962924X/IEEM-2008-the-IEEE-International-Conference-on/",

}

RIS

TY - CHAP

T1 - Part based decentralized information handling for process improvements along the supply chain

AU - Schmidt, Matthias

AU - Hartmann, Wiebke

AU - Nyhuis, Peter

PY - 2008/12/1

Y1 - 2008/12/1

N2 - "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.

AB - "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.

KW - Information processing and engineering

KW - Intelligent systems

KW - Supply chain management

UR - http://www.scopus.com/inward/record.url?scp=62949227657&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/6c0d8bb4-9f6a-31a3-bfad-17d415ad65c4/

U2 - 10.1109/IEEM.2008.4738035

DO - 10.1109/IEEM.2008.4738035

M3 - Article in conference proceedings

AN - SCOPUS:62949227657

SN - 978-1-4244-2629-4

T3 - IEEM

SP - 1073

EP - 1077

BT - Industrial Engineering and Engineering Management 2008

PB - IEEE - Institute of Electrical and Electronics Engineers Inc.

T2 - International Conference on Industrial Engineering and Engineering Management - 2008

Y2 - 8 December 2008 through 11 December 2008

ER -

Other publications by the same author(s)

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

Case study on delivery time determination using a machine learning approach in small batch production companies

Rokoss, A., Syberg, M., Tomidei, L., Hülsing, C., Deuse, J. & Schmidt, M., 12.2024, In: Journal of Intelligent Manufacturing. 35, 8, p. 3937-3958 22 p.

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

DOI

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