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

  • Steffen C. Eickemeyer Institute of Production Systems and Logistics (IFA), Leibniz University of Hanover, Hanover, Germany
  • Cameron Tucker George Washington University, Washington, D.C., USA
  • Simon Steinkamp Institute of Production Systems and Logistics (IFA), Leibniz University of Hanover, Hanover, Germany
  • Sebastian Schafer MTU Maintenance GmbH, Hanover, Germany
DOI: http://dx.doi.org/10.24178/ijbamr.2015.1.2.01

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.

Published
Sep 15, 2015
How to Cite
EICKEMEYER, Steffen C. et al. Comprehensive Validation of the Regeneration Workload Forecast for Complex Capital Goods Using Data Mining. International Journal of Business Administration and Management Research, [S.l.], v. 1, n. 2, p. 1-5, sep. 2015. ISSN 2412-4346. Available at: <https://www.researchplusjournal.com/index.php/IJBAMR/article/view/104>. Date accessed: 01 aug. 2026. doi: http://dx.doi.org/10.24178/ijbamr.2015.1.2.01.
Citation Formats
Issue
Vol 1 No 2 (2015): International Journal of Business Administration and Management Research
Section
Articles

Copyright & License

All Research Plus Journals (RPJ) publish open access articles under the terms of the Creative Commons Attribution (CC BY-SA 4.0) https://creativecommons.org/licenses/by-sa/4.0/ License which permits use, distribution and reproduction in any medium, provided the original work is properly cited & ShareAlike terms followed.

Copyright on any research article in a journal published by a RPJ is retained by the author(s). Authors grant RPJ a license to publish the article and identify itself as the original publisher. Upon author(s) by giving permission to RPJ either via RPJ journal portal or other channel to publish their research work in RPJ agrees to all the terms and conditions of https://creativecommons.org/licenses/by-sa/4.0/ License and terms & condition set by RPJ.

3rd party copyright

 It is the responsibility of author(s) to secure all necessary copyright permissions for the use of 3rd-party materials in their manuscript. 

Disclaimer

Research Plus Journals Open Access articles posted to repositories or websites are without warranty from RPJ of any kind, either express or implied, including, but not limited to, warranties of merchantability, fitness for a particular purpose, or non-infringement. To the fullest extent permitted by law RPJ disclaims all liability for any loss or damage arising out of, or in connection, with the use of or inability to use the content.