Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Proceedings of the Conference on Production Systems and Logistics |
| Seiten | 525-534 |
| Seitenumfang | 10 |
| Publikationsstatus | Veröffentlicht - 2022 |
| Veranstaltung | 3rd Conference on Production Systems and Logistics, CPSL 2022 - University of British Columbia, Vancouver, Kanada Dauer: 17 Mai 2022 → 20 Mai 2022 |
Abstract
Despite a high degree of uncertainty about the scope of future orders and the corresponding capacity and material demands, Maintenance, Repair & Overhaul (MRO) service providers face high expectations regarding due date reliability by their customers. To meet these requirements while at the same time keeping delivery times short, the availability of the required spare parts or pool parts is an essential success factor. As these cannot be kept in stock in large quantities due to their high monetary value, reliable spare parts demand forecasts are of vital importance for the profitability of MRO service providers. As a result of a high degree of information uncertainty and the mostly lumpy demand patterns, conventional time-based and statistical methods do not show sufficient forecasting quality for application in the MRO industry. Data-based approaches incorporating machine learning methods offer promising capabilities to achieve improved predictive accuracy but still need to be adequately linked to production planning and control to realize their full potential. This paper first analyses potential approaches to spare parts demand forecasting in the MRO industry, focusing on forecast accuracy and potential for integration into material and production planning. Based on this, a classification of demand forecasting approaches is presented and an approach for order-based material demand forecasting with two-step feature selection is proposed. Finally, the presented approach is applied on a real dataset provided by an MRO service provider.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Wirtschaftsingenieurwesen und Fertigungstechnik
- Ingenieurwesen (insg.)
- Maschinenbau
- Betriebswirtschaft, Management und Rechnungswesen (insg.)
- Technologie- und Innovationsmanagement
- Betriebswirtschaft, Management und Rechnungswesen (insg.)
- Strategie und Management
Ziele für nachhaltige Entwicklung
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
Proceedings of the Conference on Production Systems and Logistics. 2022. S. 525-534.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Spare Parts Demand Forecasting in Maintenance, Repair & Overhaul
AU - Lucht, Torben
AU - Alieksieiev, Volodymyr
AU - Kämpfer, Tim
AU - Nyhuis, Peter
N1 - Funding Information: Funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) ”SFB 871/3”-119193472.
PY - 2022
Y1 - 2022
N2 - Despite a high degree of uncertainty about the scope of future orders and the corresponding capacity and material demands, Maintenance, Repair & Overhaul (MRO) service providers face high expectations regarding due date reliability by their customers. To meet these requirements while at the same time keeping delivery times short, the availability of the required spare parts or pool parts is an essential success factor. As these cannot be kept in stock in large quantities due to their high monetary value, reliable spare parts demand forecasts are of vital importance for the profitability of MRO service providers. As a result of a high degree of information uncertainty and the mostly lumpy demand patterns, conventional time-based and statistical methods do not show sufficient forecasting quality for application in the MRO industry. Data-based approaches incorporating machine learning methods offer promising capabilities to achieve improved predictive accuracy but still need to be adequately linked to production planning and control to realize their full potential. This paper first analyses potential approaches to spare parts demand forecasting in the MRO industry, focusing on forecast accuracy and potential for integration into material and production planning. Based on this, a classification of demand forecasting approaches is presented and an approach for order-based material demand forecasting with two-step feature selection is proposed. Finally, the presented approach is applied on a real dataset provided by an MRO service provider.
AB - Despite a high degree of uncertainty about the scope of future orders and the corresponding capacity and material demands, Maintenance, Repair & Overhaul (MRO) service providers face high expectations regarding due date reliability by their customers. To meet these requirements while at the same time keeping delivery times short, the availability of the required spare parts or pool parts is an essential success factor. As these cannot be kept in stock in large quantities due to their high monetary value, reliable spare parts demand forecasts are of vital importance for the profitability of MRO service providers. As a result of a high degree of information uncertainty and the mostly lumpy demand patterns, conventional time-based and statistical methods do not show sufficient forecasting quality for application in the MRO industry. Data-based approaches incorporating machine learning methods offer promising capabilities to achieve improved predictive accuracy but still need to be adequately linked to production planning and control to realize their full potential. This paper first analyses potential approaches to spare parts demand forecasting in the MRO industry, focusing on forecast accuracy and potential for integration into material and production planning. Based on this, a classification of demand forecasting approaches is presented and an approach for order-based material demand forecasting with two-step feature selection is proposed. Finally, the presented approach is applied on a real dataset provided by an MRO service provider.
KW - Artificial Neural Networks
KW - forecasting
KW - Machine Learning
KW - MRO
KW - spare parts demand
UR - http://www.scopus.com/inward/record.url?scp=85164404425&partnerID=8YFLogxK
U2 - 10.15488/12179
DO - 10.15488/12179
M3 - Conference contribution
AN - SCOPUS:85164404425
SP - 525
EP - 534
BT - Proceedings of the Conference on Production Systems and Logistics
T2 - 3rd Conference on Production Systems and Logistics, CPSL 2022
Y2 - 17 May 2022 through 20 May 2022
ER -