Validation of data fusion as a method for forecasting the regeneration workload for complex capital goods

Eickemeyer, Steffen C.; Borcherding, Tim; Schäfer, Sebastian; Nyhuis, Peter

doi:10.1007/s11740-013-0444-8

Cited by 21 publications

(11 citation statements)

References 15 publications

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“…Bayesian networks have proved to be suitable for this kind of problem. It has been shown that for this approach Bayesian networks are superior to artificial neuronal networks, Fuzzy logic and other potential techniques in the field of data fusion [2]. Bayesian networks use the statistics of conditional probabilities to calculate an estimated value, which is dependent on input parameters.…”

Section: Methodology For Capacity Planningmentioning

confidence: 99%

“…The employees working in this field are highly qualified and specially accredited. Therefore, the supply of new employees is limited [2]. At the same time, the wages for such labour are high.…”

Section: Introductionmentioning

confidence: 99%

“…Instead, the employees have to improvise or even build special tools [1]. The information available is imprecise and therefore it is not easy to predict the correct workload for an engine [2].…”

Section: Introductionmentioning

confidence: 99%

“…A database containing data about former regenerations of similar capital goods can be accessed using data mining techniques in order to increase the information content. Bayesian networks represent one way of using the existing fuzzy information to estimate the potential workload of complex capital goods [2]. Fig.…”

Section: Introductionmentioning

confidence: 99%

“…Capacities have to be utilized according to the existing workload. As the capacities consist mainly of highly qualified, accredited personnel [4], it takes time to augment the workforce because the number of available employees is limited [2]. Capacities can be adjusted by moving personnel from one department with too much capacity to another with a temporary lack of capacity.…”

Section: Introductionmentioning

confidence: 99%

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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: Methodology For Capacity Planningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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