dc.identifier.uri |
http://dx.doi.org/10.15488/15978 |
|
dc.identifier.uri |
https://www.repo.uni-hannover.de/handle/123456789/16104 |
|
dc.contributor.author |
Seitz, Melissa
|
|
dc.contributor.author |
Lucht, Torben
|
|
dc.contributor.author |
Keller, Christian
|
|
dc.contributor.author |
Ludwig, Christian
|
|
dc.contributor.author |
Strobelt, Rainer
|
|
dc.contributor.author |
Nyhuis, Peter
|
|
dc.date.accessioned |
2024-01-19T10:19:26Z |
|
dc.date.available |
2024-01-19T10:19:26Z |
|
dc.date.issued |
2020 |
|
dc.identifier.citation |
Seitz, M.; Lucht, T.; Keller, C.; Ludwig, C.; Strobelt, R. et al.: Improving MRO order processing by means of advanced technological diagnostics and data mining approaches. In: Procedia Manufacturing 43 (2020), S. 688-695. DOI: https://doi.org/10.1016/j.promfg.2020.02.121 |
|
dc.description.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. |
eng |
dc.language.iso |
eng |
|
dc.publisher |
Amsterdam [u.a.] : Elsevier |
|
dc.relation.ispartofseries |
Procedia Manufacturing 43 (2020) |
|
dc.rights |
CC BY-NC-ND 4.0 Unported |
|
dc.rights.uri |
https://creativecommons.org/licenses/by-nc-nd/4.0 |
|
dc.subject |
Complex Capital Goods |
eng |
dc.subject |
Condition Assessment |
eng |
dc.subject |
Data Mining |
eng |
dc.subject |
MRO |
eng |
dc.subject |
Order Processing |
eng |
dc.subject.classification |
Konferenzschrift |
ger |
dc.subject.ddc |
620 | Ingenieurwissenschaften und Maschinenbau
|
|
dc.title |
Improving MRO order processing by means of advanced technological diagnostics and data mining approaches |
eng |
dc.type |
Article |
|
dc.type |
Text |
|
dc.relation.essn |
2351-9789 |
|
dc.relation.doi |
https://doi.org/10.1016/j.promfg.2020.02.121 |
|
dc.bibliographicCitation.volume |
43 |
|
dc.bibliographicCitation.firstPage |
688 |
|
dc.bibliographicCitation.lastPage |
695 |
|
dc.description.version |
publishedVersion |
|
tib.accessRights |
frei zug�nglich |
|