IDEAS home Printed from https://ideas.repec.org/a/ibn/masjnl/v6y2012i6p12.html
   My bibliography  Save this article

An Optimized Unbiased GM (1, 1) Power Model for Forecasting MRO Spare Parts Inventory

Author

Listed:
  • Chen Bing
  • Sun Shouqun
  • Liu Gang

Abstract

With respect to the problem of complexity and uncertainty in the MRO (Maintenance, Repair and Overhaul) spare parts inventory, an optimized grey forecasting model is employed to forecast the demand of spare parts. The parameter g is optimized based on genetic algorithm (GA) method in the unbiased GM (1, 1) power model to minimize the ARPE (Average Relative Proportional Error) of accuracy. And the optimal model is used to forecast the prediction demand in a practical example. The experiment results indicate the forecasting accuracy can be accepted by using the optimized unbiased GM (1, 1) power model.

Suggested Citation

  • Chen Bing & Sun Shouqun & Liu Gang, 2012. "An Optimized Unbiased GM (1, 1) Power Model for Forecasting MRO Spare Parts Inventory," Modern Applied Science, Canadian Center of Science and Education, vol. 6(6), pages 1-12, June.
    • Handle: RePEc:ibn:masjnl:v:6:y:2012:i:6:p:12
    as

    Download full text from publisher

    File URL: https://ccsenet.org/journal/index.php/mas/article/download/17079/11379
    Download Restriction: no
    File URL: https://ccsenet.org/journal/index.php/mas/article/view/17079
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Wenbin & Syntetos, Aris A., 2011. "Spare parts demand: Linking forecasting to equipment maintenance," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 47(6), pages 1194-1209.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Van der Auweraer, Sarah & Boute, Robert N. & Syntetos, Aris A., 2019. "Forecasting spare part demand with installed base information: A review," International Journal of Forecasting, Elsevier, vol. 35(1), pages 181-196.
    2. Van der Auweraer, Sarah & Boute, Robert, 2019. "Forecasting spare part demand using service maintenance information," International Journal of Production Economics, Elsevier, vol. 213(C), pages 138-149.
    3. Zhu, Sha & Jaarsveld, Willem van & Dekker, Rommert, 2020. "Spare parts inventory control based on maintenance planning," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    4. Wang, Wenbin, 2012. "An overview of the recent advances in delay-time-based maintenance modelling," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 165-178.
    5. Zeynep Hilal Kilimci & A. Okay Akyuz & Mitat Uysal & Selim Akyokus & M. Ozan Uysal & Berna Atak Bulbul & Mehmet Ali Ekmis, 2019. "An Improved Demand Forecasting Model Using Deep Learning Approach and Proposed Decision Integration Strategy for Supply Chain," Complexity, Hindawi, vol. 2019, pages 1-15, March.
    6. Van der Auweraer, Sarah & Zhu, Sha & Boute, Robert N., 2021. "The value of installed base information for spare part inventory control," International Journal of Production Economics, Elsevier, vol. 239(C).
    7. Wenhan Fu & Sheng Jing & Qinming Liu & Hao Zhang, 2023. "Resilient Supply Chain Framework for Semiconductor Distribution and an Empirical Study of Demand Risk Inference," Sustainability, MDPI, vol. 15(9), pages 1-14, April.
    8. Romeijnders, Ward & Teunter, Ruud & van Jaarsveld, Willem, 2012. "A two-step method for forecasting spare parts demand using information on component repairs," European Journal of Operational Research, Elsevier, vol. 220(2), pages 386-393.
    9. Antti Puurunen & Jukka Majava & Hanna Kropsu-Vehkapera & Pekka Kess, 2013. "Applicaability of the (Q,R)-Model in Maintenance Meterials Management," Diversity, Technology, and Innovation for Operational Competitiveness: Proceedings of the 2013 International Conference on Technology Innovation and Industrial Management,, ToKnowPress.
    10. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    11. Zahedi-Hosseini, Farhad & Scarf, Philip & Syntetos, Aris, 2017. "Joint optimisation of inspection maintenance and spare parts provisioning: a comparative study of inventory policies using simulation and survey data," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 306-316.
    12. Johnson, Andrew & Carnovale, Steven & Song, Ju Myung & Zhao, Yao, 2021. "Drivers of fulfillment performance in mission critical logistics systems: An empirical analysis," International Journal of Production Economics, Elsevier, vol. 237(C).
    13. Bjarnason, Erik T.S. & Taghipour, Sharareh & Banjevic, Dragan, 2014. "Joint optimal inspection and inventory for a k-out-of-n system," Reliability Engineering and System Safety, Elsevier, vol. 131(C), pages 203-215.
    14. Sharma, Pankaj & Kulkarni, Makarand S & Yadav, Vikas, 2017. "A simulation based optimization approach for spare parts forecasting and selective maintenance," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 274-289.
    15. Dinis, Duarte & Barbosa-Póvoa, Ana & Teixeira, Ângelo Palos, 2022. "Enhancing capacity planning through forecasting: An integrated tool for maintenance of complex product systems," International Journal of Forecasting, Elsevier, vol. 38(1), pages 178-192.
    16. Li, Chongshou & Lim, Andrew, 2018. "A greedy aggregation–decomposition method for intermittent demand forecasting in fashion retailing," European Journal of Operational Research, Elsevier, vol. 269(3), pages 860-869.
    17. Boram Choi & Jong Hwan Suh, 2020. "Forecasting Spare Parts Demand of Military Aircraft: Comparisons of Data Mining Techniques and Managerial Features from the Case of South Korea," Sustainability, MDPI, vol. 12(15), pages 1-20, July.
    18. Hu, Qiwei & Boylan, John E. & Chen, Huijing & Labib, Ashraf, 2018. "OR in spare parts management: A review," European Journal of Operational Research, Elsevier, vol. 266(2), pages 395-414.
    19. Pennings, Clint L.P. & van Dalen, Jan & van der Laan, Erwin A., 2017. "Exploiting elapsed time for managing intermittent demand for spare parts," European Journal of Operational Research, Elsevier, vol. 258(3), pages 958-969.
    20. Boliang Lin & Jiaxi Wang & Huasheng Wang & Zhongkai Wang & Jian Li & Ruixi Lin & Jie Xiao & Jianping Wu, 2017. "Inventory-transportation integrated optimization for maintenance spare parts of high-speed trains," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-18, May.

    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ibn:masjnl:v:6:y:2012:i:6:p:12. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Canadian Center of Science and Education (email available below). General contact details of provider: https://edirc.repec.org/data/cepflch.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.