Evolution of Performance Measurement Methods in Conditions of Uncertainty: The Implementation of Fuzzy Sets in Performance Measurement
One of the basic issues of development management is connected with performance measurement as a prerequisite for identifying the achievement of development objectives. The aim of our research is to develop an improved model of assessing a company’s development results. The model should take into account the cyclical nature of development and the high degree of uncertainty in dealing with numerous management tasks. Our hypotheses may be formulated as follows: Hypothesis 1. The cycle of a company’s development may be studied from the standpoint of a project cycle. To do that, methods and tools of project analysis are to be used. Hypothesis 2. The problem of the uncertainty when justifying managerial decisions within the framework of a company’s development cycle can be solved through the use of the mathematical apparatus of fuzzy logic. The reasoned justification of the validity of the hypotheses made is given in the suggested article. The fuzzy logic toolkit applies to the case of technology shift within an enterprise. It is proven that some restrictions in performance measurement that are incurred to conventional methods could be eliminated by implementation of the fuzzy logic apparatus in performance measurement models.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1127346Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 651
 Behrens W., Hawranek P.M. Manual for the preparation of industrial feasibility studies. Newly revised and expanded edition. UNlDO United Nations Industrial Development Organization Vienna 1991
 Kruchkov V.N., 2009 Nonlinearity of time in management or than strategic planning differs from the long-term? Omsk, OMGU
 Popov D. Jevoljucija pokazatelej strategii razvitija predprijatija.// "Upravlenie kompaniej", 2003.- №2
 Kaplan R. S., Norton D. P. 1992. The balanced scorecard — Measures that drive performance. Harvard Business Review 70 (1): 71–79.
 Stewart, G. B. (1994), EVA™: Fast and Fantasy. Journal of Applied Corporate Finance, 7: 71–84. doi: 10.1111/j.1745-6622. 1994.tb00406.x
 Stern, J. (2004), Corporate Governance, EVA, and Shareholder Value. Journal of Applied Corporate Finance, 16: 91–99. doi: 10.1111/j.1745-6622. 2004. tb00541.x
 Pritchard, R. D., Jones, S. D., Roth, P. L., Stuebing, K. K., & Ekeberg, S. E. (1989). The evaluation of an integrated approach to measuring organizational productivity. Personnel Psychology, 42, 69-115.
 Janssen, P., van Berkel, A., & Stolk, J. (1995). ProMES as part of a new management strategy. In R. D. Pritchard, (Ed.), Productivity measurement and improvement: Organizational case studies (pp. 43-61). New York: Praeger
 Hronec, S.M., Arthur Andersen & Co.: Vital signs: using quality, time, and cost performance measurements to chart your company’s future., 1993
 The Ernst & Young Guide to Performance Measurement for Financial Institutions: Methods for Managing Business Results Revised Edition (Bankline Publication) Hardcover – November 22, 1994
 Hendricks, J. A., Defreitas, D. G. and Walker, D. K. 1996. Changing performance measures at Caterpillar. Management Accounting (December): 18-22, 24.
 House, C. P. & Raymond L. (2009) HP Phenomenon: Innovation and Business Transformation. Stanford University 2009
 Zadeh L. A. Fuzzy Sets // Information and Control. 1965. Vol. 8, Issue 3. P. 338-353.
 Bellman R. E. and Zadeh L. A., Decision-Making in a Fuzzy Environment, Management Science, 17, pp. B-141-B-164, 1970
 Dubois D., Prade, H. and R.R. Yager, eds., Readings in Fuzzy Sets for Intelligent Systems, Morgan Kaufmann, San Francisco, Calif., 1993.
 Bellman R.E., Kalaba R. and Zadeh L. A. Abstraction and Pattern Classification. J. Math. Anal. and Appl. 2:581-586, 1966
 Buckley J.J. Theory of the Fuzzy Controller: An Introduction. Fuzzy Sets and Systems, 51:249-258, 1992.
 Cheeseman P. Probability versus Fuzzy Reasoning. In: L.N. Kanal und J.F. Lemmer, Hg., Uncertainty in Artificial Intelligence, 85-102. North-Holland, Amsterdam, 1986.
 Kosko B. Fuzzy Systems as Universal Approximators. Proc IEEE Int. Con/. on Fuzzy Systems, 1153-1162, San Diego, 1992.
 Mamdani E.H. und Gaines, B.R. Hg. Fuzzy Reasoning and its Applications. Academic Press, London, 1981.
 Sugeno M, Hg. Industrial Applications of Fuzzy Control. NorthHolland, Amsterdam, 1985.
 Sugeno M und Yasukawa T. A Fuzzy-Logic-Based Approach to Qualitative Modeling. IEEE Trans. on Fuzzy Systems, 1:7 H. 1993.
 Booker, J. M., Parkinson, W. J., Ross, T. J. Fuzzy Logic and Probability Application: Bridging the Gap, 2nd Edition. New York: John Wiley and Sons, 2004. 650 p.