Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 688-695 |
| Seitenumfang | 8 |
| Fachzeitschrift | Procedia Manufacturing |
| Jahrgang | 43 |
| Frühes Online-Datum | 30 Apr. 2020 |
| Publikationsstatus | Veröffentlicht - 2020 |
| Veranstaltung | 17th Global Conference on Sustainable Manufacturing 2019 - Shanghai, China Dauer: 9 Okt. 2019 → 11 Okt. 2019 |
Abstract
Production planning based on uncertain load information may lead to low schedule adherence or low capacity utilization. Thus, maintenance, repair and overhaul (MRO) service providers are striving to improve their business processes to achieve high logistics efficiency. To estimate repair expenditures and material demands as early as possible, different approaches may be pursued. In this paper, the advancement of technological diagnostics to enable condition assessment without prior disassembly and the use of data mining to generate reliable forecasts are discussed. Thereby, the potential for planning MRO order processing is focused using the example of aircraft engines and rail vehicle transformers.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Wirtschaftsingenieurwesen und Fertigungstechnik
- Informatik (insg.)
- Artificial intelligence
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
in: Procedia Manufacturing, Jahrgang 43, 2020, S. 688-695.
Publikation: Beitrag in Fachzeitschrift › Konferenzaufsatz in Fachzeitschrift › Forschung › Peer-Review
}
TY - JOUR
T1 - Improving MRO order processing by means of advanced technological diagnostics and data mining approaches
AU - Seitz, Melissa
AU - Lucht, Torben
AU - Keller, Christian
AU - Ludwig, Christian
AU - Strobelt, Rainer
AU - Nyhuis, Peter
N1 - Funding Information: The authors kindly thank the German Research oF undation (DGF ) for the financial support to accomplish the research projects T3 “ Capacity planning and quotation costing for transformer regeneration by means of data mining” and D1 M“ odelling r egeneration supply chains” within the Collaborative Research Centre (CRC) 871 – Regeneration of Complex Capital Goods.
PY - 2020
Y1 - 2020
N2 - Production planning based on uncertain load information may lead to low schedule adherence or low capacity utilization. Thus, maintenance, repair and overhaul (MRO) service providers are striving to improve their business processes to achieve high logistics efficiency. To estimate repair expenditures and material demands as early as possible, different approaches may be pursued. In this paper, the advancement of technological diagnostics to enable condition assessment without prior disassembly and the use of data mining to generate reliable forecasts are discussed. Thereby, the potential for planning MRO order processing is focused using the example of aircraft engines and rail vehicle transformers.
AB - Production planning based on uncertain load information may lead to low schedule adherence or low capacity utilization. Thus, maintenance, repair and overhaul (MRO) service providers are striving to improve their business processes to achieve high logistics efficiency. To estimate repair expenditures and material demands as early as possible, different approaches may be pursued. In this paper, the advancement of technological diagnostics to enable condition assessment without prior disassembly and the use of data mining to generate reliable forecasts are discussed. Thereby, the potential for planning MRO order processing is focused using the example of aircraft engines and rail vehicle transformers.
KW - Complex Capital Goods
KW - Condition Assessment
KW - Data Mining
KW - MRO
KW - Order Processing
UR - http://www.scopus.com/inward/record.url?scp=85088526063&partnerID=8YFLogxK
U2 - 10.1016/j.promfg.2020.02.121
DO - 10.1016/j.promfg.2020.02.121
M3 - Conference article
AN - SCOPUS:85088526063
VL - 43
SP - 688
EP - 695
JO - Procedia Manufacturing
JF - Procedia Manufacturing
SN - 2351-9789
T2 - 17th Global Conference on Sustainable Manufacturing 2019
Y2 - 9 October 2019 through 11 October 2019
ER -