From Fuzzy Maintenance, Repair and Overhaul Data to Reliable Capacity Planning

Eickemeyer, Steffen C.; Steinkamp, Simon; Schuster, Bernhardt; Schäfer, Sebastian

doi:10.1007/978-3-319-01964-2_24

Cited by 4 publications

(4 citation statements)

References 5 publications

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“…For the validation, the workloads of engines that are not used to feed the calculations of the Bayesian networks are estimated and compared with the real workloads of the engines. With regard to former validations, the percentage deviation between estimated and real workloads are categorized [2]: 0-9.9 % very high quality estimate 10-25 % satisfactory quality estimate > 25 % unsatisfactory quality estimate Table 2 lists the results of the validation of the two networks developed as well as the validation results that were achieved in the European plant [6]. The estimates are not only compared with the real workload but also with the existing planning data at the facilities to see if they can be beneficial to the company.…”

Section: Resultsmentioning

confidence: 99%

“…5. Schematic Bayesian network [6] The Bayesian networks are validated separately before their estimates are used as input parameters in order to make sure that the transfer to the new sociocultural environment has been successful. For the validation, the workloads of engines that are not used to feed the calculations of the Bayesian networks are estimated and compared with the real workloads of the engines.…”

Section: Resultsmentioning

confidence: 99%

“…The workload estimates of the Bayesian network are used as input parameters for the mathematical model, which then calculates the optimal capacity utilization. In this way, the two methods, which are found to be beneficial separately, can multiply their potential to create a reliable capacity planning approach [6].…”

Section: Methodology For Capacity Planningmentioning

confidence: 99%

“…The capacity planning in the field of MRO has hardly been studied scientifically. Currently no practical relevant, rule-based and thus transparent method or software application that is suitable for planning or controlling the regeneration of complex capital goods exists [5,6,7].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Reliable Capacity Planning Despite Uncertain Disassembly, Regeneration and Reassembly Workloads by Using Statistical and Mathematical Approaches – Validation in Subsidiaries of a Global MRO Company with Operations in Asia, Europe and North America

Eickemeyer

Steinkamp

Schuster³

et al. 2014

Procedia CIRP

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The MRO industry faces substantial challenges with regard to the capacity planning of disassembly and reassembly work. This is due to the unknown workloads when regenerating complex investment goods and is caused, in particular, by the uncertain degree of disassembly and the complex challenges of reassembly. Forecasting techniques based on Bayesian networks are developed along with mathematical models which optimize capacity utilization, job order and the resulting costs. The approaches are tested and validated in conjunction with an MRO company with global operations. The results show possibilities for enhancing the planning processes and are found to be transferable on an international scale regardless of sociocultural and process differences.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodology For Capacity Planningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Reliable Capacity Planning Despite Uncertain Disassembly, Regeneration and Reassembly Workloads by Using Statistical and Mathematical Approaches – Validation in Subsidiaries of a Global MRO Company with Operations in Asia, Europe and North America

Eickemeyer

Steinkamp

Schuster³

et al. 2014

Procedia CIRP

Self Cite

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show abstract

Decision models for capacity planning in a regeneration environment

Eickemeyer

Herde

Irudayaraj

et al. 2014

International Journal of Production Research

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Comprehensive Validation of the Regeneration Workload Forecast for Complex Capital Goods Using Data Mining

Eickemeyer¹,

Tucker²,

Steinkamp³

et al. 2015

IJBAMR

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Abstract-Capacity planning in the regeneration of complex capital goods faces major challenges because it is affected by a high level of uncertain workload information. A methodology is developed here to predict the regeneration workload on the basis of the CRISP-DM model using Bayesian networks. The forecasts are validated for the different capacity planning levels. The results support the conclusion that capacity planning can gain permanent benefits from the methodology developed.

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From Fuzzy Maintenance, Repair and Overhaul Data to Reliable Capacity Planning

Cited by 4 publications

References 5 publications

Reliable Capacity Planning Despite Uncertain Disassembly, Regeneration and Reassembly Workloads by Using Statistical and Mathematical Approaches – Validation in Subsidiaries of a Global MRO Company with Operations in Asia, Europe and North America

Reliable Capacity Planning Despite Uncertain Disassembly, Regeneration and Reassembly Workloads by Using Statistical and Mathematical Approaches – Validation in Subsidiaries of a Global MRO Company with Operations in Asia, Europe and North America

Decision models for capacity planning in a regeneration environment

Comprehensive Validation of the Regeneration Workload Forecast for Complex Capital Goods Using Data Mining

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