Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Advances in Production Management Systems. Towards Smart Production Management Systems |
| Untertitel | IFIP WG 5.7 International Conference, APMS 2019, Proceedings, Part II |
| Herausgeber/-innen | Farhad Ameri, Kathryn E. Stecke, Gregor von Cieminski, Dimitris Kiritsis |
| Seiten | 583-590 |
| Seitenumfang | 8 |
| Auflage | 1. |
| ISBN (elektronisch) | 978-3-030-29996-5 |
| Publikationsstatus | Veröffentlicht - 24 Aug. 2019 |
| Veranstaltung | IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2019 - Austin, USA / Vereinigte Staaten Dauer: 1 Sept. 2019 → 5 Sept. 2019 |
Publikationsreihe
| Name | IFIP Advances in Information and Communication Technology |
|---|---|
| Band | 567 |
| ISSN (Print) | 1868-4238 |
| ISSN (elektronisch) | 1868-422X |
Abstract
With regard to the recommissioning of damage caused inoperable complex capital goods, a high logistics efficiency is a very important competitive factor for regeneration service providers. Consequently, fast processing as well as a high schedule reliability need to be realized. However, since the required regeneration effort for future damages may vary and is usually indefinite at the time of planning, capacity planning for the regeneration of complex capital goods has to deal with a high degree of uncertainty. Regarding this challenge, the evaluation of prior regeneration process data by means of data mining offers great potential for the determination of load forecasts. This paper depicts the development of a data mining approach to support capacity planning for the regeneration for complex capital goods focusing on rail vehicle transformers as a sample of application.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Information systems
- Informatik (insg.)
- Computernetzwerke und -kommunikation
- Entscheidungswissenschaften (insg.)
- Informationssysteme und -management
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- BibTex
- RIS
Advances in Production Management Systems. Towards Smart Production Management Systems: IFIP WG 5.7 International Conference, APMS 2019, Proceedings, Part II. Hrsg. / Farhad Ameri; Kathryn E. Stecke; Gregor von Cieminski; Dimitris Kiritsis. 1. Aufl. 2019. S. 583-590 (IFIP Advances in Information and Communication Technology; Band 567).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - A Data Mining Approach to Support Capacity Planning for the Regeneration of Complex Capital Goods
AU - Seitz, Melissa
AU - Sobotta, Maren
AU - Nyhuis, Peter
N1 - Funding information: The authors kindly thank the German Research Foundation (DFG) for the financial support to accomplish the research projects T3 “Capacity planning and quotation costing for transformer regeneration by means of data mining” within the Collaborative Research Centre (CRC) 871–Regeneration of Complex Capital Goods.
PY - 2019/8/24
Y1 - 2019/8/24
N2 - With regard to the recommissioning of damage caused inoperable complex capital goods, a high logistics efficiency is a very important competitive factor for regeneration service providers. Consequently, fast processing as well as a high schedule reliability need to be realized. However, since the required regeneration effort for future damages may vary and is usually indefinite at the time of planning, capacity planning for the regeneration of complex capital goods has to deal with a high degree of uncertainty. Regarding this challenge, the evaluation of prior regeneration process data by means of data mining offers great potential for the determination of load forecasts. This paper depicts the development of a data mining approach to support capacity planning for the regeneration for complex capital goods focusing on rail vehicle transformers as a sample of application.
AB - With regard to the recommissioning of damage caused inoperable complex capital goods, a high logistics efficiency is a very important competitive factor for regeneration service providers. Consequently, fast processing as well as a high schedule reliability need to be realized. However, since the required regeneration effort for future damages may vary and is usually indefinite at the time of planning, capacity planning for the regeneration of complex capital goods has to deal with a high degree of uncertainty. Regarding this challenge, the evaluation of prior regeneration process data by means of data mining offers great potential for the determination of load forecasts. This paper depicts the development of a data mining approach to support capacity planning for the regeneration for complex capital goods focusing on rail vehicle transformers as a sample of application.
KW - Capacity planning
KW - Complex capital goods
KW - Data base
KW - Data mining
KW - Logistics efficiency
UR - http://www.scopus.com/inward/record.url?scp=85072947413&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-29996-5_67
DO - 10.1007/978-3-030-29996-5_67
M3 - Conference contribution
AN - SCOPUS:85072947413
SN - 978-3-030-29995-8
SN - 978-3-030-29998-9
T3 - IFIP Advances in Information and Communication Technology
SP - 583
EP - 590
BT - Advances in Production Management Systems. Towards Smart Production Management Systems
A2 - Ameri, Farhad
A2 - Stecke, Kathryn E.
A2 - von Cieminski, Gregor
A2 - Kiritsis, Dimitris
T2 - IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2019
Y2 - 1 September 2019 through 5 September 2019
ER -