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

Publikation: Beitrag in Fachzeitschrift › Konferenzaufsatz in Fachzeitschrift › Forschung › Peer-Review

Autoren

  • Steffen C. Eickemeyer
  • Simon Steinkamp
  • Bernhardt Schuster
  • Frank Bodenhage
  • Peter Nyhuis

Organisationseinheiten

Externe Organisationen

  • MTU Maintenance Canada Ltd.
  • MTU Maintenance Zhuhai Co. Ltd.
Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Seiten (von - bis)252-257
Seitenumfang6
FachzeitschriftProcedia CIRP
Jahrgang23
AusgabenummerC
PublikationsstatusVeröffentlicht - 29 Dez. 2014
Veranstaltung5th CIRP Conference on Assembly Technologies and Systems, CATS 2014 - Dresden, Deutschland
Dauer: 12 Mai 2014 → 14 Mai 2014

Abstract

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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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, Steffen C.; Steinkamp, Simon; Schuster, Bernhardt et al.
in: Procedia CIRP, Jahrgang 23, Nr. C, 29.12.2014, S. 252-257.

Publikation: Beitrag in Fachzeitschrift › Konferenzaufsatz in Fachzeitschrift › Forschung › Peer-Review

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abstract = "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.",
keywords = "Bayesian networks, Capacity planning, Complex capital goods, Damage library, Data mining, Disassembly, Forecast, Maintenance, Mixed-integer linear programming, MRO, Reassembly, Regeneration",
author = "Eickemeyer, {Steffen C.} and Simon Steinkamp and Bernhardt Schuster and Frank Bodenhage and Peter Nyhuis",
note = "Funding information: The authors would like to thank the German Research Foundation (DFG) for providing funding for this research project within the scope of the CRC 871 program. The authors would also like to thank the Graduate Academy of Leibniz University Hannover for providing funding for the validation of the theoretical results in the global industry.; 5th CIRP Conference on Assembly Technologies and Systems, CATS 2014 ; Conference date: 12-05-2014 Through 14-05-2014",
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T2 - 5th CIRP Conference on Assembly Technologies and Systems, CATS 2014

AU - Eickemeyer, Steffen C.

AU - Steinkamp, Simon

AU - Schuster, Bernhardt

AU - Bodenhage, Frank

AU - Nyhuis, Peter

N1 - Funding information: The authors would like to thank the German Research Foundation (DFG) for providing funding for this research project within the scope of the CRC 871 program. The authors would also like to thank the Graduate Academy of Leibniz University Hannover for providing funding for the validation of the theoretical results in the global industry.

PY - 2014/12/29

Y1 - 2014/12/29

N2 - 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.

AB - 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.

KW - Bayesian networks

KW - Capacity planning

KW - Complex capital goods

KW - Damage library

KW - Data mining

KW - Disassembly

KW - Forecast

KW - Maintenance

KW - Mixed-integer linear programming

KW - MRO

KW - Reassembly

KW - Regeneration

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DO - 10.1016/j.procir.2014.10.097

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JO - Procedia CIRP

JF - Procedia CIRP

SN - 2212-8271

IS - C

Y2 - 12 May 2014 through 14 May 2014

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