IDEAS home Printed from https://ideas.repec.org/a/aml/intbrm/v2y2011i1p19-32.html
   My bibliography  Save this article

Measurement and Comparison of Productivity Performance Under Fuzzy Imprecise Data

Author

Listed:
  • Avninder Gill

    (Thompson Rivers University, Canada)

Abstract

The creation of goods and services requires changing the expended resources into the output goods and services. How efficiently we transform these input resources into goods and services depends on the productivity of the transformation process. However, it has been observed there is always a vagueness or imprecision associated with the values of inputs and outputs. Therefore, it becomes hard for a productivity measurement expert to specify the amount of resources and the outputs as exact scalar numbers. The present paper, applies fuzzy set theory to measure and compare productivity performance of transformation processes when numerical data cannot be specified in exact terms. The approach makes it possible to measure and compare productivity of organizational units (including non-government and non-profit entities) when the expert inputs can not be specified as exact scalar quantities. The model has been applied to compare productivity of different branches of a company.

Suggested Citation

  • Avninder Gill, 2011. "Measurement and Comparison of Productivity Performance Under Fuzzy Imprecise Data," International Journal of Business Research and Management (IJBRM), Computer Science Journals (CSC Journals), vol. 2(1), pages 19-32, April.
    • Handle: RePEc:aml:intbrm:v:2:y:2011:i:1:p:19-32
    as

    Download full text from publisher

    File URL: https://www.cscjournals.org/manuscript/Journals/IJBRM/Volume2/Issue1/IJBRM-10.pdf
    Download Restriction: no
    File URL: https://www.cscjournals.org/library/manuscriptinfo.php?mc=IJBRM-10
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Agrell, Per J. & Martin West, B., 2001. "A caveat on the measurement of productive efficiency," International Journal of Production Economics, Elsevier, vol. 69(1), pages 1-14, January.
    2. Thijs Raa, 2005. "Aggregation of Productivity Indices: The Allocative Efficiency Correction," Journal of Productivity Analysis, Springer, vol. 24(2), pages 203-209, October.
    3. Zhu, Joe, 2003. "Imprecise data envelopment analysis (IDEA): A review and improvement with an application," European Journal of Operational Research, Elsevier, vol. 144(3), pages 513-529, February.
    4. Konstantinos Triantis & Olivier Girod, 1998. "A Mathematical Programming Approach for Measuring Technical Efficiency in a Fuzzy Environment," Journal of Productivity Analysis, Springer, vol. 10(1), pages 85-102, July.
    5. William W. Cooper & Kyung Sam Park & Gang Yu, 2001. "An Illustrative Application of Idea (Imprecise Data Envelopment Analysis) to a Korean Mobile Telecommunication Company," Operations Research, INFORMS, vol. 49(6), pages 807-820, December.
    6. Suwignjo, P. & Bititci, U. S & Carrie, A. S, 2000. "Quantitative models for performance measurement system," International Journal of Production Economics, Elsevier, vol. 64(1-3), pages 231-241, March.
    7. Hannula, Mika, 2002. "Total productivity measurement based on partial productivity ratios," International Journal of Production Economics, Elsevier, vol. 78(1), pages 57-67, July.
    8. Sudit, Ephraim F., 1995. "Productivity measurement in industrial operations," European Journal of Operational Research, Elsevier, vol. 85(3), pages 435-453, September.
    9. Garrigosa, E Genescá & Tatjé, E Grifell, 1992. "Profits and total factor productivity: A comparative analysis," Omega, Elsevier, vol. 20(5-6), pages 553-568.
    10. Chiou, Wen-Chih & Kuo, Hsiu-Wei & Lu, Iuan-Yuan, 1999. "A technology oriented productivity measurement model," International Journal of Production Economics, Elsevier, vol. 60(1), pages 69-77, April.
    11. David M. Miller & P. Mohan Rao, 1989. "Analysis of Profit-Linked Total-Factor Productivity Measurement Models at the Firm Level," Management Science, INFORMS, vol. 35(6), pages 757-767, June.
    12. Diewert, W Erwin, 1978. "Superlative Index Numbers and Consistency in Aggregation," Econometrica, Econometric Society, vol. 46(4), pages 883-900, July.
    13. ,, 2000. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 16(2), pages 287-299, April.
    14. Konstantinos Triantis, 2003. "Fuzzy non-radial data envelopment analysis (DEA)measures of technical efficiency in support of an integrated performance measurement system," International Journal of Automotive Technology and Management, Inderscience Enterprises Ltd, vol. 3(3/4), pages 328-353.
    15. William W. Cooper & Kyung Sam Park & Gang Yu, 1999. "IDEA and AR-IDEA: Models for Dealing with Imprecise Data in DEA," Management Science, INFORMS, vol. 45(4), pages 597-607, April.
    16. Ylvinger, Svante, 2000. "Industry performance and structural efficiency measures: Solutions to problems in firm models," European Journal of Operational Research, Elsevier, vol. 121(1), pages 164-174, February.
    17. Jean-Paul Chavas & Zohra Mechemache, 2006. "Efficiency measurements and the gains from trade under transaction costs," Journal of Productivity Analysis, Springer, vol. 26(1), pages 67-85, August.
    18. Ray, Pradip K. & Sahu, S., 1992. "Productivity measurement in multi-product manufacturing firms: Evaluation and control through sensitivity analysis," International Journal of Production Economics, Elsevier, vol. 28(1), pages 71-84, November.
    19. W. Cooper & L. Seiford & K. Tone & J. Zhu, 2007. "Some models and measures for evaluating performances with DEA: past accomplishments and future prospects," Journal of Productivity Analysis, Springer, vol. 28(3), pages 151-163, December.
    20. Kao, Chiang & Liu, Shiang-Tai, 2003. "A mathematical programming approach to fuzzy efficiency ranking," International Journal of Production Economics, Elsevier, vol. 86(2), pages 145-154, November.
    21. Chen, L. -H. & Kao, C. & Kuo, S. & Wang, T. -Y. & Jang, Y. -C., 1996. "Productivity diagnosis via fuzzy clustering and classification: An application to machinery industry," Omega, Elsevier, vol. 24(3), pages 309-319, June.
    22. W. Diewert & Alice Nakamura, 2003. "Index Number Concepts, Measures and Decompositions of Productivity Growth," Journal of Productivity Analysis, Springer, vol. 19(2), pages 127-159, April.
    23. W W Cooper & K S Park & G Yu, 2001. "IDEA (Imprecise Data Envelopment Analysis) with CMDs (Column Maximum Decision Making Units)," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(2), pages 176-181, February.
    24. Odeck, James, 2000. "Assessing the relative efficiency and productivity growth of vehicle inspection services: An application of DEA and Malmquist indices," European Journal of Operational Research, Elsevier, vol. 126(3), pages 501-514, November.
    25. Entani, Tomoe & Maeda, Yutaka & Tanaka, Hideo, 2002. "Dual models of interval DEA and its extension to interval data," European Journal of Operational Research, Elsevier, vol. 136(1), pages 32-45, January.
    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. Violeta Cvetkoska & Milanka Dimovska, 2021. "What Will Be The Productivity Of Employees With Shorter Work Hours?," International Journal of Business Research and Management (IJBRM), Computer Science Journals (CSC Journals), vol. 12(4), pages 139-162, August.

    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, 2006. "Interval efficiency measures in data envelopment analysis with imprecise data," European Journal of Operational Research, Elsevier, vol. 174(2), pages 1087-1099, October.
    2. Lampe, Hannes W. & Hilgers, Dennis, 2015. "Trajectories of efficiency measurement: A bibliometric analysis of DEA and SFA," European Journal of Operational Research, Elsevier, vol. 240(1), pages 1-21.
    3. Hatami-Marbini, Adel & Emrouznejad, Ali & Tavana, Madjid, 2011. "A taxonomy and review of the fuzzy data envelopment analysis literature: Two decades in the making," European Journal of Operational Research, Elsevier, vol. 214(3), pages 457-472, November.
    4. Shabani, Amir & Visani, Franco & Barbieri, Paolo & Dullaert, Wout & Vigo, Daniele, 2019. "Reliable estimation of suppliers’ total cost of ownership: An imprecise data envelopment analysis model with common weights," Omega, Elsevier, vol. 87(C), pages 57-70.
    5. Florica LUBAN, 2009. "Measuring efficiency of a hierarchical organization with fuzzy DEA method," Economia. Seria Management, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 12(1), pages 87-97, June.
    6. Park, K. Sam, 2010. "Duality, efficiency computations and interpretations in imprecise DEA," European Journal of Operational Research, Elsevier, vol. 200(1), pages 289-296, January.
    7. Toloo, Mehdi & Mensah, Emmanuel Kwasi & Salahi, Maziar, 2022. "Robust optimization and its duality in data envelopment analysis," Omega, Elsevier, vol. 108(C).
    8. Reza Farzipoor Saen, 2009. "A decision model for ranking suppliers in the presence of cardinal and ordinal data, weight restrictions, and nondiscretionary factors," Annals of Operations Research, Springer, vol. 172(1), pages 177-192, November.
    9. HATAMI-MARBINI, Adel & AGRELL, Per & AGHAYI, Nazila, 2013. "Imprecise data envelopment analysis for the two-stage process," LIDAM Discussion Papers CORE 2013004, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    10. Yu Yu & Weiwei Zhu & Qian Zhang, 2019. "DEA cross-efficiency evaluation and ranking method based on interval data," Annals of Operations Research, Springer, vol. 278(1), pages 159-175, July.
    11. Cui, Qiang & Lin, Jing-ling & Jin, Zi-yin, 2020. "Evaluating airline efficiency under “Carbon Neutral Growth from 2020” strategy through a Network Interval Slack-Based Measure," Energy, Elsevier, vol. 193(C).
    12. Liu, John S. & Lu, Louis Y.Y. & Lu, Wen-Min, 2016. "Research fronts in data envelopment analysis," Omega, Elsevier, vol. 58(C), pages 33-45.
    13. Adel Hatami-Marbini & Zahra Ghelej Beigi & Jens Leth Hougaard & Kobra Gholami, 2014. "Estimating Returns to Scale in Imprecise Data Envelopment Analysis," MSAP Working Paper Series 07_2014, University of Copenhagen, Department of Food and Resource Economics.
    14. K S Park, 2007. "Efficiency bounds and efficiency classifications in DEA with imprecise data," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(4), pages 533-540, April.
    15. repec:cor:louvrp:-2393 is not listed on IDEAS
    16. K S Park, 2004. "Simplification of the transformations and redundancy of assurance regions in IDEA (imprecise DEA)," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(12), pages 1363-1366, December.
    17. Joe Zhu, 2004. "Imprecise DEA via Standard Linear DEA Models with a Revisit to a Korean Mobile Telecommunication Company," Operations Research, INFORMS, vol. 52(2), pages 323-329, April.
    18. Jolly Puri & Shiv Prasad Yadav, 2017. "Improved DEA models in the presence of undesirable outputs and imprecise data: an application to banking industry in India," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 1608-1629, November.
    19. Yiwen Bian & Kangjuan Lv & Anyu Yu, 2017. "China’s regional energy and carbon dioxide emissions efficiency evaluation with the presence of recovery energy: an interval slacks-based measure approach," Annals of Operations Research, Springer, vol. 255(1), pages 301-321, August.
    20. Badunenko, Oleg & Fritsch, Michael & Stephan, Andreas, 2008. "Allocative efficiency measurement revisited--Do we really need input prices?," Economic Modelling, Elsevier, vol. 25(5), pages 1093-1109, September.
    21. Mehdi Toloo & Esmaeil Keshavarz & Adel Hatami-Marbini, 2021. "An interval efficiency analysis with dual-role factors," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 43(1), pages 255-287, March.

    More about this item

    Keywords

    Productivity; Fuzzy Set Theory; Efficiency; Performance Measure;
    All these keywords.

    JEL classification:

    • M0 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - 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:aml:intbrm:v:2:y:2011:i:1:p:19-32. 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: Nabeel Tahir (email available below). General contact details of provider: .

    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.