IDEAS home Printed from https://ideas.repec.org/a/eee/jomega/v24y1996i3p309-319.html
   My bibliography  Save this article

Productivity diagnosis via fuzzy clustering and classification: An application to machinery industry

Author

Listed:
  • Chen, L. -H.
  • Kao, C.
  • Kuo, S.
  • Wang, T. -Y.
  • Jang, Y. -C.

Abstract

Business units are always faced with intensifying pressure in a competitive economy. Increasing productivity is an effective solution for a firm to survive and prosper. The relative productivity in an industry has evolved into a significant determinant of the competitive position for a firm. This paper proposes a productivity diagnosis process for a firm on the basis of the productivity characters of an industry to gain an insight into the firm's relative productivity and to find the shortcomings in its management of resources. Firstly, productivity structure is determined. Pattern recognition technologies, namely fuzzy clustering and fuzzy classification, are then employed. After fuzzily clustering a training set according to three feature spaces, the productivity characters of the industry can be determined. A business unit can be diagnosed through fuzzily classifying its productivity features in a particular feature space and productivity indications can be furnished based on the associated productivity characters. As an illustration, data from 23 machinery firms in Taiwan are collected as a training set to analyze the productivity characters in each space, and two hypothetical firms are diagnosed.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:jomega:v:24:y:1996:i:3:p:309-319
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/0305-0483(96)00002-3
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    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. Roubens, Marc, 1982. "Fuzzy clustering algorithms and their cluster validity," European Journal of Operational Research, Elsevier, vol. 10(3), pages 294-301, July.
    2. Marvin B. Lieberman & Lawrence J. Lau & Mark D. Williams, 1990. "Firm-Level Productivity and Management Influence: A Comparison of U.S. and Japanese Automobile Producers," Management Science, INFORMS, vol. 36(10), pages 1193-1215, October.
    3. Eilon, S, 1993. "A methodology for analysis of corporate performance," Omega, Elsevier, vol. 21(5), pages 551-560, September.
    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. 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.
    2. Ozer, Muammer, 2001. "User segmentation of online music services using fuzzy clustering," Omega, Elsevier, vol. 29(2), pages 193-206, April.
    3. Ebrahimipour, V. & Suzuki, K., 2006. "A synergetic approach for assessing and improving equipment performance in offshore industry based on dependability," Reliability Engineering and System Safety, Elsevier, vol. 91(1), pages 10-19.

    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. Nick Zubanov & W.S. Siebert, 2009. "Management economics in a large UK retailer," CPB Discussion Paper 125, CPB Netherlands Bureau for Economic Policy Analysis.
    2. Kao, Chiang & Chen, Liang-Hsuan & Wang, Tai-Yue & Kuo, Shyanjaw & Horng, Shi-Dai, 1995. "Productivity improvement: Efficiency approach vs effectiveness approach," Omega, Elsevier, vol. 23(2), pages 197-204, April.
    3. Cusumano, Michael A., 1954-, 1992. "Japanese technology management--innovations, transferability, and the limitations of "lean" production," Working papers 3477-92., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    4. Sickles, Robin C. & Song, Wonho & Zelenyuk, Valentin, 2018. "Econometric Analysis of Productivity: Theory and Implementation in R," Working Papers 18-008, Rice University, Department of Economics.
    5. Chris Rowley & Saaidah Abdul-Rahman, 2007. "The Management of Human Resources in Malaysia: Locally-owned Companies and Multinational Companies," management revue - Socio-Economic Studies, Nomos Verlagsgesellschaft mbH & Co. KG, vol. 18(4), pages 427-453.
    6. Siebert, W. Stanley & Zubanov, Nick, 2008. "Management Economics in a Large Retail Organization," IZA Discussion Papers 3645, Institute of Labor Economics (IZA).
    7. Mingoti, Sueli A. & Lima, Joab O., 2006. "Comparing SOM neural network with Fuzzy c-means, K-means and traditional hierarchical clustering algorithms," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1742-1759, November.
    8. Chien-Ming Chen & Magali A. Delmas & Marvin B. Lieberman, 2015. "Production frontier methodologies and efficiency as a performance measure in strategic management research," Strategic Management Journal, Wiley Blackwell, vol. 36(1), pages 19-36, January.
    9. Baltrunaite, Audinga & Bovini, Giulia & Mocetti, Sauro, 2023. "Managerial talent and managerial practices: Are they complements?," Journal of Corporate Finance, Elsevier, vol. 79(C).
    10. Mehmet Nasih TAĞ & Duygu HIDIROĞLU, 2018. "Kaymakamların Algılanan Liderlik Tarzı Çalışanların İş Tatminini Etkiliyor mu? Ampirik Bir Çalışma," Istanbul Business Research, Istanbul University Business School, vol. 47(2), pages 233-258, November.
    11. Naoto Yamashita & Shin-ichi Mayekawa, 2015. "A new biplot procedure with joint classification of objects and variables by fuzzy c-means clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 9(3), pages 243-266, September.
    12. Mahvash S Qureshi & Mr. Charalambos G Tsangarides, 2006. "What is Fuzzy About Clustering in West Africa?," IMF Working Papers 2006/090, International Monetary Fund.
    13. Anne Marie Knott, 2001. "The Dynamic Value of Hierarchy," Management Science, INFORMS, vol. 47(3), pages 430-448, March.
    14. Leon Zolotoy & Don O’Sullivan & Geoffrey P. Martin, 2022. "Behavioural Agency and Firm Productivity: Revisiting the Incentive Alignment Qualities of Stock Options," Journal of Management Studies, Wiley Blackwell, vol. 59(7), pages 1756-1787, November.
    15. W. Stanley Siebert & Nikolay Zubanov, 2010. "Management Economics in a Large Retail Company," Management Science, INFORMS, vol. 56(8), pages 1398-1414, August.
    16. Sáenz-Royo, Carlos & Salas-Fumás, Vicente, 2014. "Long- and short-term efficiency in an automobile factory: An econometric case study," International Journal of Production Economics, Elsevier, vol. 156(C), pages 98-107.
    17. Stéphane Benveniste & Renaud Coulomb & Marc Sangnier, 2022. "The (Market) Value of State Honors," AMSE Working Papers 2201, Aix-Marseille School of Economics, France.
    18. Yuk Ying Chang & Sudipto Dasgupta & Gilles Hilary, 2010. "CEO Ability, Pay, and Firm Performance," Management Science, INFORMS, vol. 56(10), pages 1633-1652, October.
    19. Börsch-Supan, Axel, 1997. "Capital productivity and the nature of competition," Papers 97-21, Sonderforschungsbreich 504.
    20. Lieberman, Marvin B. & R. Johnson, Douglas, 1999. "Comparative productivity of Japanese and U.S. steel producers, 1958-1993," Japan and the World Economy, Elsevier, vol. 11(1), pages 1-27, January.

    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:eee:jomega:v:24:y:1996:i:3:p:309-319. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/375/description#description .

    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.