IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v341y2024i1d10.1007_s10479-024-05998-3.html
   My bibliography  Save this article

A novel improved FMEA method using data envelopment analysis method and 2-tuple fuzzy linguistic model

Author

Listed:
  • Kuei-Hu Chang

    (R.O.C. Military Academy)

  • Yi-Jun Chen

    (R.O.C. Military Academy)

  • Chung-Cheng Liao

    (R.O.C. Military Academy)

Abstract

Since the failure mode and effects analysis (FMEA) technique has the advantage of simple and fast calculation, the FMEA method is widely and commonly used in solving risk assessment issues. The traditional FMEA method uses the product of risk assessment factors (occurrence, severity, and detection) to calculate risk priority number (RPN) for risk ranking. Although the RPN approach is widely adopted by the military and industry, it cannot process uncertain and incomplete information, it does not consider the relative importance of three risk assessment factors and in some situations, it loses some valuable information provided by experts. This results in the same RPN value which cannot provide the accurate risk level. In order to effectively solve these problems, this paper combined the data envelopment analysis (DEA) method and 2-tuple fuzzy linguistic representation model (2-tuple FLRM) and proposed a new model, called the 2-tuple DEA method, for ranking the risk of product (system) failures. In the numerical verification section, this paper applied the risk assessment of crawler crane to verify the rationality and correctness of the 2-tuple DEA approach. The calculation results confirm that the proposed 2-tuple DEA approach provides a more accurate failure risk ranking and retains all valuable information.

Suggested Citation

  • Kuei-Hu Chang & Yi-Jun Chen & Chung-Cheng Liao, 2024. "A novel improved FMEA method using data envelopment analysis method and 2-tuple fuzzy linguistic model," Annals of Operations Research, Springer, vol. 341(1), pages 485-507, October.
    • Handle: RePEc:spr:annopr:v:341:y:2024:i:1:d:10.1007_s10479-024-05998-3
    DOI: 10.1007/s10479-024-05998-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-024-05998-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-024-05998-3?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. Juan Aparicio & Jesus T. Pastor & Jose L. Sainz-Pardo & Fernando Vidal, 2020. "Estimating and decomposing overall inefficiency by determining the least distance to the strongly efficient frontier in data envelopment analysis," Operational Research, Springer, vol. 20(2), pages 747-770, June.
    2. Kuei-Hu Chang & Yung-Chia Chang & Kai Chain & Hsiang-Yu Chung, 2016. "Integrating Soft Set Theory and Fuzzy Linguistic Model to Evaluate the Performance of Training Simulation Systems," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-29, September.
    3. Minguito, Glenda & Banluta, Jenith, 2023. "Risk management in humanitarian supply chain based on FMEA and grey relational analysis," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
    4. Telles, Eduardo Santos & Lacerda, Daniel Pacheco & Morandi, Maria Isabel Wolf Motta & Piran, Fabio Antonio Sartori, 2020. "Drum-buffer-rope in an engineering-to-order system: An analysis of an aerospace manufacturer using data envelopment analysis (DEA)," International Journal of Production Economics, Elsevier, vol. 222(C).
    5. Habib Zare & Madjid Tavana & Abbas Mardani & Sepideh Masoudian & Mahyar Kamali Saraji, 2019. "A hybrid data envelopment analysis and game theory model for performance measurement in healthcare," Health Care Management Science, Springer, vol. 22(3), pages 475-488, September.
    6. Tziogkidis, Panagiotis & Philippas, Dionisis & Leontitsis, Alexandros & Sickles, Robin C., 2020. "A data envelopment analysis and local partial least squares approach for identifying the optimal innovation policy direction," European Journal of Operational Research, Elsevier, vol. 285(3), pages 1011-1024.
    7. Per J. Agrell & Pontus Mattsson & Jonas Månsson, 2020. "Impacts on efficiency of merging the Swedish district courts," Annals of Operations Research, Springer, vol. 288(2), pages 653-679, May.
    8. Somayeh Razipour-GhalehJough & Farhad Hosseinzadeh Lotfi & Gholamreza Jahanshahloo & Mohsen Rostamy-malkhalifeh & Hamid Sharafi, 2020. "Finding closest target for bank branches in the presence of weight restrictions using data envelopment analysis," Annals of Operations Research, Springer, vol. 288(2), pages 755-787, May.
    9. Leonardo Tomazeli Duarte & Alex Pincelli Mussio & Cristiano Torezzan, 2020. "Dealing with missing information in data envelopment analysis by means of low-rank matrix completion," Annals of Operations Research, Springer, vol. 286(1), pages 719-732, March.
    10. Adil Baykasoğlu & İlker Gölcük, 2020. "Comprehensive fuzzy FMEA model: a case study of ERP implementation risks," Operational Research, Springer, vol. 20(2), pages 795-826, June.
    11. Ai-Bing Ji & Hao Chen & Yanhua Qiao & Jiahong Pang, 2019. "Data envelopment analysis with interactive fuzzy variables," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(9), pages 1502-1510, September.
    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. Kao, Chiang, 2024. "Maximum slacks-based measure of efficiency in network data envelopment analysis: A case of garment manufacturing," Omega, Elsevier, vol. 123(C).
    2. Yunyao Li & Yanji Ma, 2022. "Research on Industrial Innovation Efficiency and the Influencing Factors of the Old Industrial Base Based on the Lock-In Effect, a Case Study of Jilin Province, China," Sustainability, MDPI, vol. 14(19), pages 1-23, October.
    3. Iraklis Kollias & John Leventides & Vassilios G. Papavassiliou, 2024. "On the solution of games with arbitrary payoffs: An application to an over‐the‐counter financial market," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 1877-1895, April.
    4. Aparicio, Juan & Ortiz, Lidia & Santín, Daniel, 2021. "Comparing group performance over time through the Luenberger productivity indicator: An application to school ownership in European countries," European Journal of Operational Research, Elsevier, vol. 294(2), pages 651-672.
    5. Amir Moradi-Motlagh & Ali Emrouznejad, 2022. "The origins and development of statistical approaches in non-parametric frontier models: a survey of the first two decades of scholarly literature (1998–2020)," Annals of Operations Research, Springer, vol. 318(1), pages 713-741, November.
    6. Ricardo Pinto & Isabel Lourenço & Ana Simões, 2022. "Does Innovation Spur Integrated Reporting?," Sustainability, MDPI, vol. 15(1), pages 1-20, December.
    7. Marwa Hasni & Safa Bhar Layeb & Najla Omrane Aissaoui & Aymen Mannai, 2022. "Hybrid model for a cross‐department efficiency evaluation in healthcare systems," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(5), pages 1311-1329, July.
    8. Chen, Xiaoqing & Kerstens, Kristiaan & Tsionas, Mike, 2024. "Does productivity change at all in Swedish district courts? Empirical analysis focusing on horizontal mergers," Socio-Economic Planning Sciences, Elsevier, vol. 91(C).
    9. Duras, Toni & Javed, Farrukh & Månsson, Kristofer & Sjölander, Pär & Söderberg, Magnus, 2023. "Using machine learning to select variables in data envelopment analysis: Simulations and application using electricity distribution data," Energy Economics, Elsevier, vol. 120(C).
    10. Giacalone, Massimiliano & Nissi, Eugenia & Cusatelli, Carlo, 2020. "Dynamic efficiency evaluation of Italian judicial system using DEA based Malmquist productivity indexes," Socio-Economic Planning Sciences, Elsevier, vol. 72(C).
    11. Xin Tian & Qiang Mai & Qinan Zhang & Mingshu Lyu & Shiyao Li, 2024. "Analyzing provincial imbalances in green innovation development in china: multi-way efficiency analysis and geodetector approach," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(10), pages 26115-26146, October.
    12. Hahn, G.J. & Brandenburg, M. & Becker, J., 2021. "Valuing supply chain performance within and across manufacturing industries: A DEA-based approach," International Journal of Production Economics, Elsevier, vol. 240(C).
    13. Fabio Antonio Sartori Piran & Alaércio De Paris & Daniel Pacheco Lacerda & Luis Felipe Riehs Camargo & Rosiane Serrano & Ricardo Augusto Cassel, 2020. "Overall Equipment Effectiveness: Required but not Enough—An Analysis Integrating Overall Equipment Effect and Data Envelopment Analysis," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 21(2), pages 191-206, June.
    14. Jiguang Wang & Yushang Hu & Weihua Qu & Liuxin Ma, 2022. "Research on Emergency Supply Chain Collaboration Based on Tripartite Evolutionary Game," Sustainability, MDPI, vol. 14(19), pages 1-25, September.
    15. Monge, Juan F. & Ruiz, José L., 2023. "Setting closer targets based on non-dominated convex combinations of Pareto-efficient units: A bi-level linear programming approach in Data Envelopment Analysis," European Journal of Operational Research, Elsevier, vol. 311(3), pages 1084-1096.
    16. Juan Aparicio & José L. Zofío, 2020. "New Definitions of Economic Cross-efficiency," International Series in Operations Research & Management Science, in: Juan Aparicio & C. A. Knox Lovell & Jesus T. Pastor & Joe Zhu (ed.), Advances in Efficiency and Productivity II, pages 11-32, Springer.
    17. Kristiaan KERSTENS & Xiaoqing CHEN, 2022. "Evaluating Horizontal Mergers in Swedish District Courts Using Plant Capacity Concepts: With a Focus on Nonconvexity," Working Papers 2022-EQM-02, IESEG School of Management.
    18. Kuei-Hu Chang, 2019. "A novel supplier selection method that integrates the intuitionistic fuzzy weighted averaging method and a soft set with imprecise data," Annals of Operations Research, Springer, vol. 272(1), pages 139-157, January.
    19. Daniel Feliciano & Laura López-Torres & Daniel Santín, 2021. "One Laptop per Child? Using Production Frontiers for Evaluating the Escuela 2.0 Program in Spain," Mathematics, MDPI, vol. 9(20), pages 1-17, October.
    20. Alfnes, Erlend & Gosling, Jonathan & Naim, Mohamed & Dreyer, Heidi C., 2023. "Rearticulating supply chain design and operation principles to mitigate uncertainty in the Norwegian engineer-to-order shipbuilding sector," International Journal of Production Economics, Elsevier, vol. 262(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:spr:annopr:v:341:y:2024:i:1:d:10.1007_s10479-024-05998-3. 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.springer.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.