IDEAS home Printed from https://ideas.repec.org/a/pal/jorsoc/v60y2009i12d10.1057_jors.2008.147.html
   My bibliography  Save this article

Cost of ownership model for spare engines purchase for the Korean navy acquisition program

Author

Listed:
  • S Y Sohn

    (Yonsei University)

  • Y Kim

    (Yonsei University)

  • B T Kim

    (Yonsei University)

Abstract

Major weapon system acquisition programmes often require high initial purchase cost which can be a burden for the procurement of a highly reliable system. In order to avoid the tendency of acquiring a less expensive weapon system with lower performance, a cost of ownership (COO) model can be applied to assess the lifetime cost of the weapon system. In many existing cost estimation models for weapon systems, the failure rate of the system is assumed to be constant and the functional relationship between the initial purchase cost and maintenance cost is not well defined. In this paper, we propose a revised COO model where random effects models are employed to accommodate the variations of the system failure frequency and repair time. It is expected that our model can contribute to the cost-effective procurement of spare engines for the Korean Navy acquisition programme within the limited national defence budget.

Suggested Citation

  • S Y Sohn & Y Kim & B T Kim, 2009. "Cost of ownership model for spare engines purchase for the Korean navy acquisition program," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(12), pages 1674-1682, December.
  • Handle: RePEc:pal:jorsoc:v:60:y:2009:i:12:d:10.1057_jors.2008.147
    DOI: 10.1057/jors.2008.147
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/jors.2008.147
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1057/jors.2008.147?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. S Y Sohn & H Choi, 2006. "Random effects logistic regression model for data envelopment analysis with correlated decision making units," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(5), pages 552-560, May.
    2. S Y Sohn, 2006. "Random effects logistic regression model for ranking efficiency in data envelopment analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(11), pages 1289-1299, November.
    3. Jorgensen, Trond & Wallace, Stein W., 2000. "Improving project cost estimation by taking into account managerial flexibility," European Journal of Operational Research, Elsevier, vol. 127(2), pages 239-251, December.
    4. Sohn, So Young & Kim, Hong Sik, 2007. "Random effects logistic regression model for default prediction of technology credit guarantee fund," European Journal of Operational Research, Elsevier, vol. 183(1), pages 472-478, November.
    5. K B Yoon & S Y Sohn, 2007. "Forecasting both time varying MTBF of fighter aircraft module and expected demand of minor parts," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(6), pages 714-719, June.
    6. David Kirkpatrick, 2004. "Trends in the costs of weapon systems and the consequences," Defence and Peace Economics, Taylor & Francis Journals, vol. 15(3), pages 259-273.
    7. E Angelelli & M G Speranza, 2002. "The application of a vehicle routing model to a waste-collection problem: two case studies," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(9), pages 944-952, September.
    8. V Lotfi & J Sarkis & J H Semple, 1998. "Economic justification for incremental implementation of advanced manufacturing systems," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 49(8), pages 829-839, August.
    9. Kim, Yoonseong & Sohn, So Young, 2008. "Random effects model for credit rating transitions," European Journal of Operational Research, Elsevier, vol. 184(2), pages 561-573, January.
    10. Sohn, So Young & Chang, In Sang & Moon, Tae Hee, 2007. "Random effects Weibull regression model for occupational lifetime," European Journal of Operational Research, Elsevier, vol. 179(1), pages 124-131, May.
    11. Gong, Zhejun, 2008. "An economic evaluation model of supply chain flexibility," European Journal of Operational Research, Elsevier, vol. 184(2), pages 745-758, January.
    12. Sungcheol Yun & So Young Sohn & Youngjo Lee, 2006. "Modelling and estimating heavy-tailed non-homogeneous correlated queues: Pareto-inverse gamma HGLM with covariates," Journal of Applied Statistics, Taylor & Francis Journals, vol. 33(4), pages 417-425.
    13. Sohn, So Young & Yoon, Kyung Bok & Chang, In Sang, 2006. "Random effects model for the reliability management of modules of a fighter aircraft," Reliability Engineering and System Safety, Elsevier, vol. 91(4), pages 433-437.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mauro Lizot & Flavio Trojan & Paulo Afonso, 2021. "Combining Total Cost of Ownership and Multi-Criteria Decision Analysis to Improve Cost Management in Family Farming," Agriculture, MDPI, vol. 11(2), pages 1-20, February.
    2. Andrea Bacchetti & Stefano Bonetti & Marco Perona & Nicola Saccani, 2018. "Investment and Management Decisions in Aluminium Melting: A Total Cost of Ownership Model and Practical Applications," Sustainability, MDPI, vol. 10(9), pages 1-36, September.

    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. Nima Mirzaei & Béla Vizvári, 2015. "A New Approach to Reconstruction of Moody’s Rating System for Countries Investment Risk Rating," Journal of Empirical Economics, Research Academy of Social Sciences, vol. 4(3), pages 167-182.
    2. S Y Sohn & K B Yoon, 2010. "Dynamic preventive maintenance scheduling of the modules of fighter aircraft based on random effects regression model," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(6), pages 974-979, June.
    3. Freddy Hernández & Viviana Giampaoli, 2018. "The Impact of Misspecified Random Effect Distribution in a Weibull Regression Mixed Model," Stats, MDPI, vol. 1(1), pages 1-29, May.
    4. Jeon, Jeasu & Sohn, So Young, 2015. "Product failure pattern analysis from warranty data using association rule and Weibull regression analysis: A case study," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 176-183.
    5. Daniel Friesner & Ron Mittelhammer & Robert Rosenman, 2006. "Inferring the Latent Incidence of Inefficiency from DEA Estimates and Bayesian Priors," Working Papers 2006-8, School of Economic Sciences, Washington State University.
    6. Kim, Yoonseong & Sohn, So Young, 2008. "Random effects model for credit rating transitions," European Journal of Operational Research, Elsevier, vol. 184(2), pages 561-573, January.
    7. Friesner, Daniel & Mittelhammer, Ron & Rosenman, Robert, 2013. "Inferring the incidence of industry inefficiency from DEA estimates," European Journal of Operational Research, Elsevier, vol. 224(2), pages 414-424.
    8. Pearce, Joshua M. & Johnson, Sara J. & Grant, Gabriel B., 2007. "3D-mapping optimization of embodied energy of transportation," Resources, Conservation & Recycling, Elsevier, vol. 51(2), pages 435-453.
    9. Christine Duke & Cameron McKean, 2008. "Alternative methodologies for projecting defence spending," Economic Roundup, The Treasury, Australian Government, issue 2, pages 1-11, July.
    10. Lamas, Patricio & Goycoolea, Marcos & Pagnoncelli, Bernardo & Newman, Alexandra, 2024. "A target-time-windows technique for project scheduling under uncertainty," European Journal of Operational Research, Elsevier, vol. 314(2), pages 792-806.
    11. Das, Kanchan, 2011. "Integrating effective flexibility measures into a strategic supply chain planning model," European Journal of Operational Research, Elsevier, vol. 211(1), pages 170-183, May.
    12. Goldmann, Leonie & Crook, Jonathan & Calabrese, Raffaella, 2024. "A new ordinal mixed-data sampling model with an application to corporate credit rating levels," European Journal of Operational Research, Elsevier, vol. 314(3), pages 1111-1126.
    13. Myriam Ben Ayed & Adel Karaa & Jean‐Luc Prigent, 2018. "Duration Models For Credit Rating Migration: Evidence From The Financial Crisis," Economic Inquiry, Western Economic Association International, vol. 56(3), pages 1870-1886, July.
    14. Nicoleta BARBUTA-MISU, 2011. "A Specific Model for Assessing the Financial Performance:Case study on Building Sector Enterprises of Galati County - Romania," Risk in Contemporary Economy, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, pages 318-325.
    15. Gläser, Sina, 2022. "A waste collection problem with service type option," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1216-1230.
    16. Wienke, Andreas & Kuss, Oliver, 2009. "Random effects Weibull regression model for occupational lifetime," European Journal of Operational Research, Elsevier, vol. 196(3), pages 1249-1250, August.
    17. Epure, Mircea & Kerstens, Kristiaan & Prior, Diego, 2011. "Bank productivity and performance groups: A decomposition approach based upon the Luenberger productivity indicator," European Journal of Operational Research, Elsevier, vol. 211(3), pages 630-641, June.
    18. Matthies, Alexander B., 2013. "Statistical properties and stability of ratings in a subset of US firms," SFB 649 Discussion Papers 2013-002, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    19. Chan, Ngai Hang & Wong, Hoi Ying & Zhao, Jing, 2012. "Structural model of credit migration," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3477-3490.
    20. Hazır, Öncü & Ulusoy, Gündüz, 2020. "A classification and review of approaches and methods for modeling uncertainty in projects," International Journal of Production Economics, Elsevier, vol. 223(C).

    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:pal:jorsoc:v:60:y:2009:i:12:d:10.1057_jors.2008.147. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.palgrave-journals.com/ .

    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.