IDEAS home Printed from https://ideas.repec.org/a/wly/syseng/v21y2018i4p307-321.html
   My bibliography  Save this article

Managing system obsolescence via multicriteria decision making

Author

Listed:
  • Oluwatomi Adetunji
  • John Bischoff
  • Christopher J. Willy

Abstract

Obsolescence occurs when system elements become outdated, and it leads to operational, logistical, reliability, and cost implications. In the U.S. military, this problem is a result of the U.S. Department of Defense's (DoD) departure from Military Specification (MILSPEC) standards in 1994 and transition to the use of Commercial Off the Shelf products. Obsolescence costs the DoD more than $750 million annually. The current risk management tools for obsolescence are based on a quantitative approach that uses cost optimization, and expert judgment is not used as a critical criterion. A review of the literature has revealed that during the design phase of technological systems, there is limited knowledge and a lack of training associated with mitigating obsolescence, and multicriteria decision‐making (MCDM) methods are not currently used to mitigate the risk of obsolescence. Thus, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS, which is a MCDM method) and Monte Carlo simulations are proposed as the foundation for this work. This paper adds to the methodology by introducing an expert judgment criterion. A case study was conducted using military and civilian experts. Expert validation showed that the TOPSIS model successfully identified the best system for mitigating obsolescence. This model can be used by system designers and other decision makers to conduct trade studies in obsolescence management.

Suggested Citation

  • Oluwatomi Adetunji & John Bischoff & Christopher J. Willy, 2018. "Managing system obsolescence via multicriteria decision making," Systems Engineering, John Wiley & Sons, vol. 21(4), pages 307-321, July.
    • Handle: RePEc:wly:syseng:v:21:y:2018:i:4:p:307-321
    DOI: 10.1002/sys.21436
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/sys.21436
    Download Restriction: no
    File URL: https://libkey.io/10.1002/sys.21436?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
    ---><---

    References listed on IDEAS

    as
    1. Pannell, David J., 1997. "Sensitivity analysis of normative economic models: theoretical framework and practical strategies," Agricultural Economics, Blackwell, vol. 16(2), pages 139-152, May.
    2. Anthony O'Hagan & Matt Stevenson & Jason Madan, 2007. "Monte Carlo probabilistic sensitivity analysis for patient level simulation models: efficient estimation of mean and variance using ANOVA," Health Economics, John Wiley & Sons, Ltd., vol. 16(10), pages 1009-1023, October.
    3. Ernest H Forman & Mary Ann Selly, 2001. "Decision by Objectives:How to Convince Others That You are Right," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 4281, August.
    4. Anthony O'Hagan & Matt Stevenson & Jason Madan, 2007. "Monte Carlo probabilistic sensitivity analysis for patient level simulation models: efficient estimation of mean and variance using ANOVA," Health Economics, John Wiley & Sons, Ltd., vol. 16(10), pages 1009-1023.
    5. Keeney,Ralph L. & Raiffa,Howard, 1993. "Decisions with Multiple Objectives," Cambridge Books, Cambridge University Press, number 9780521438834, September.
    6. Weber, Martin & Borcherding, Katrin, 1993. "Behavioral influences on weight judgments in multiattribute decision making," European Journal of Operational Research, Elsevier, vol. 67(1), pages 1-12, May.
    7. Johanna Bragge & Pekka Korhonen & Hannele Wallenius & Jyrki Wallenius, 2010. "Bibliometric Analysis of Multiple Criteria Decision Making/Multiattribute Utility Theory," Lecture Notes in Economics and Mathematical Systems, in: Matthias Ehrgott & Boris Naujoks & Theodor J. Stewart & Jyrki Wallenius (ed.), Multiple Criteria Decision Making for Sustainable Energy and Transportation Systems, pages 259-268, Springer.
    8. Andrea Saltelli, 2002. "Sensitivity Analysis for Importance Assessment," Risk Analysis, John Wiley & Sons, vol. 22(3), pages 579-590, June.
    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. Georgios K. Koulinas & Olympia E. Demesouka & Konstantinos A. Sidas & Dimitrios E. Koulouriotis, 2021. "A TOPSIS—Risk Matrix and Monte Carlo Expert System for Risk Assessment in Engineering Projects," Sustainability, MDPI, vol. 13(20), pages 1-14, October.
    2. Imen Zaabar & Raul Arango-Miranda & Yvan Beauregard & Marc Paquet, 2021. "A Sustainable Multicriteria Decision Framework for Obsolescence Resolution Strategy Selection," Sustainability, MDPI, vol. 13(15), pages 1-16, August.
    3. Ates, Aylin & Acur, Nuran, 2022. "Making obsolescence obsolete: Execution of digital transformation in a high-tech manufacturing SME," Journal of Business Research, Elsevier, vol. 152(C), pages 336-348.
    4. Jung-Fa Tsai & Chin-Po Wang & Ming-Hua Lin & Shih-Wei Huang, 2021. "Analysis of Key Factors for Supplier Selection in Taiwan’s Thin-Film Transistor Liquid-Crystal Displays Industry," Mathematics, MDPI, vol. 9(4), pages 1-18, February.

    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. Daniel R. Georgiadis & Thomas A. Mazzuchi & Shahram Sarkani, 2013. "Using multi criteria decision making in analysis of alternatives for selection of enabling technology," Systems Engineering, John Wiley & Sons, vol. 16(3), pages 287-303, September.
    2. Ed Cook & Jason R. W. Merrick, 2023. "Technology Implementation at Capital One," Interfaces, INFORMS, vol. 53(3), pages 178-191, May.
    3. P. S. Nagpaul & Santanu Roy, 2003. "Constructing a multi-objective measure of research performance," Scientometrics, Springer;Akadémiai Kiadó, vol. 56(3), pages 383-402, March.
    4. Jonathan Karnon & James Stahl & Alan Brennan & J. Jaime Caro & Javier Mar & Jörgen Möller, 2012. "Modeling Using Discrete Event Simulation," Medical Decision Making, , vol. 32(5), pages 701-711, September.
    5. Chang, Yu-Hern & Yeh, Chung-Hsing, 2001. "Evaluating airline competitiveness using multiattribute decision making," Omega, Elsevier, vol. 29(5), pages 405-415, October.
    6. C. L. Smith & E. Borgonovo, 2007. "Decision Making During Nuclear Power Plant Incidents—A New Approach to the Evaluation of Precursor Events," Risk Analysis, John Wiley & Sons, vol. 27(4), pages 1027-1042, August.
    7. V. J. Roelofs & M. C. Kennedy, 2011. "Sensitivity Analysis and Estimation of Extreme Tail Behavior in Two‐Dimensional Monte Carlo Simulation," Risk Analysis, John Wiley & Sons, vol. 31(10), pages 1597-1609, October.
    8. Oakley, Jeremy E. & Brennan, Alan & Tappenden, Paul & Chilcott, Jim, 2010. "Simulation sample sizes for Monte Carlo partial EVPI calculations," Journal of Health Economics, Elsevier, vol. 29(3), pages 468-477, May.
    9. Schuwirth, N. & Reichert, P. & Lienert, J., 2012. "Methodological aspects of multi-criteria decision analysis for policy support: A case study on pharmaceutical removal from hospital wastewater," European Journal of Operational Research, Elsevier, vol. 220(2), pages 472-483.
    10. Gilberto Montibeller & Detlof von Winterfeldt, 2015. "Cognitive and Motivational Biases in Decision and Risk Analysis," Risk Analysis, John Wiley & Sons, vol. 35(7), pages 1230-1251, July.
    11. Robin L. Dillon & Vicki M. Bier & Richard Sheffield John & Abdullah Althenayyan, 2023. "Closing the Gap Between Decision Analysis and Policy Analysts Before the Next Pandemic," Decision Analysis, INFORMS, vol. 20(2), pages 109-132, June.
    12. Helen A. Dakin & José Leal & Andrew Briggs & Philip Clarke & Rury R. Holman & Alastair Gray, 2020. "Accurately Reflecting Uncertainty When Using Patient-Level Simulation Models to Extrapolate Clinical Trial Data," Medical Decision Making, , vol. 40(4), pages 460-473, May.
    13. Joke Bilcke & Philippe Beutels & Marc Brisson & Mark Jit, 2011. "Accounting for Methodological, Structural, and Parameter Uncertainty in Decision-Analytic Models," Medical Decision Making, , vol. 31(4), pages 675-692, July.
    14. Marta Soares & Luísa Canto e Castro, 2012. "Continuous Time Simulation and Discretized Models for Cost-Effectiveness Analysis," PharmacoEconomics, Springer, vol. 30(12), pages 1101-1117, December.
    15. Jonah Popp & John A. Nyman & Xianghua Luo & Jill Bengtson & Katherine Lust & Lawrence An & Jasjit S. Ahluwalia & Janet L. Thomas, 2018. "Cost-effectiveness of enhancing a Quit-and-Win smoking cessation program for college students," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 19(9), pages 1319-1333, December.
    16. Alfonso Mateos & Antonio Jiménez-Martín & Sixto Ríos-Insua, 2015. "A Group Decision-Making Methodology with Incomplete Individual Beliefs Applied to e-Democracy," Group Decision and Negotiation, Springer, vol. 24(4), pages 633-653, July.
    17. Marta O Soares & L Canto e Castro, 2010. "Simulation or cohort models? Continuous time simulation and discretized Markov models to estimate cost-effectiveness," Working Papers 056cherp, Centre for Health Economics, University of York.
    18. Marttunen, Mika & Haag, Fridolin & Belton, Valerie & Mustajoki, Jyri & Lienert, Judit, 2019. "Methods to inform the development of concise objectives hierarchies in multi-criteria decision analysis," European Journal of Operational Research, Elsevier, vol. 277(2), pages 604-620.
    19. Scholten, Lisa & Schuwirth, Nele & Reichert, Peter & Lienert, Judit, 2015. "Tackling uncertainty in multi-criteria decision analysis – An application to water supply infrastructure planning," European Journal of Operational Research, Elsevier, vol. 242(1), pages 243-260.
    20. Marta O. Soares & Luísa Canto e Castro, 2012. "Continuous Time Simulation and Discretized Models for Cost-Effectiveness Analysis," PharmacoEconomics, Springer, vol. 30(12), pages 1101-1117, December.

    More about this item

    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:wly:syseng:v:21:y:2018:i:4:p:307-321. 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: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1002/(ISSN)1520-6858 .

    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.