Details
| Original language | English |
|---|---|
| Pages (from-to) | 131-139 |
| Number of pages | 9 |
| Journal | Production Engineering |
| Volume | 7 |
| Issue number | 2-3 |
| Publication status | Published - 1 Feb 2013 |
Abstract
The regeneration of complex capital goods is afflicted with a high degree of uncertainty. Neither the extent of the damage to the goods nor the resulting maintenance workload is known in advance, and that poses challenges for capacity planning. Data fusion in the form of Bayesian networks is used to prepare forecasts in order to estimate the workload in maintenance processes. The objective is to optimize the planability of the capacities required.
Keywords
- Bayesian networks, Capacity planning, Data fusion, Maintenance
ASJC Scopus subject areas
- Engineering(all)
- Mechanical Engineering
- Engineering(all)
- Industrial and Manufacturing Engineering
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In: Production Engineering, Vol. 7, No. 2-3, 01.02.2013, p. 131-139.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Validation of data fusion as a method for forecasting the regeneration workload for complex capital goods
AU - Eickemeyer, Steffen C.
AU - Borcherding, Tim
AU - Schäfer, Sebastian
AU - Nyhuis, Peter
N1 - Funding information: Acknowledgments The authors would like to thank the DFG research organization for providing funding for this research project within the scope of the Collaborative Research Centres (CRC) 871.
PY - 2013/2/1
Y1 - 2013/2/1
N2 - The regeneration of complex capital goods is afflicted with a high degree of uncertainty. Neither the extent of the damage to the goods nor the resulting maintenance workload is known in advance, and that poses challenges for capacity planning. Data fusion in the form of Bayesian networks is used to prepare forecasts in order to estimate the workload in maintenance processes. The objective is to optimize the planability of the capacities required.
AB - The regeneration of complex capital goods is afflicted with a high degree of uncertainty. Neither the extent of the damage to the goods nor the resulting maintenance workload is known in advance, and that poses challenges for capacity planning. Data fusion in the form of Bayesian networks is used to prepare forecasts in order to estimate the workload in maintenance processes. The objective is to optimize the planability of the capacities required.
KW - Bayesian networks
KW - Capacity planning
KW - Data fusion
KW - Maintenance
UR - http://www.scopus.com/inward/record.url?scp=84875612965&partnerID=8YFLogxK
U2 - 10.1007/s11740-013-0444-8
DO - 10.1007/s11740-013-0444-8
M3 - Article
AN - SCOPUS:84875612965
VL - 7
SP - 131
EP - 139
JO - Production Engineering
JF - Production Engineering
SN - 0944-6524
IS - 2-3
ER -