Abstract
Nowadays, benchmarking is a widespread technique for evaluating an aspect—process, product, service, etc.—by comparing it against the best in class with the aim of improving this aspect or identifying the best alternative. There have been numerous attempts at defining a rigorous benchmarking process by specifying steps that should be taken to put benchmarking into practice. All these proposals use a method of calculation that treats the weights and ratings of each criterion as numerical variables, even if they are not. This means that the binary and linguistic variables have to be artificially translated to numerical variables, misleading us into thinking that the concepts we are dealing with are quantitative when they really are not. In this paper, we propose a new method of calculation based on fuzzy logic to rectify this key methodological error. Its definition is based on: (i) a new division operator for fuzzy numbers representing conjugated variables, as in the case outlined here; (ii) a new aggregation operator that can integrate binary, numerical and/or linguistic variables; and, finally, (iii) an operator that can translate the final fuzzy rating into the linguistic variable that best represents it. Therefore, the resulting method is: (i) closer to the user since it manages more human-understandable values and (ii) not dependent on the above artificial translation process, which could lead to sizeable variations in the benchmarking result.
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References
Anderson K, McAdam R (2004) A critique of benchmarking and performance measurement: lead or lag?. Benchmarking Int J 11(5): 465–483
Andrade J, Ares J, García R, Pazos J, Rodríguez S, Silva A (2008) Formal conceptualisation as a basis for a more procedural knowledge management. Decis Support Syst 45(1): 164–179
Bandermer H, Gottwald S (1995) Fuzzy sets, fuzzy logic, fuzzy methods with applications. Wiley, Chichester
Bar-Yossef Z, Guy I, Lempel R, Maarek YS, Soroka V (2008) Cluster ranking with an application to mining mailbox networks. Knowl Inf Syst 14(1): 101–139
Baldrige National Quality Program (BNQP), National Institute of Standards and Technology (2008) Criteria for performance excellence. http://www.quality.nist.gov/PDF_files/2009_2010/Business_Nonprofit_Criteria.pdf. Accessed 24 April 2009
Bonissone PP, Decaer KS (1986) Selecting uncertainty calculi and granularity: an experiment in trading-off precision and complexity. In: Kanal LH, Lemmer JF (eds) Uncertainty in artificial intelligence. North-Holland, Amsterdam, pp 217–247
Camp RC (1989) Benchmarking: the search for industry best practices that lead to superior performance. ASQC Quality Press, Milwaukee
Dattakumar R, Jagadeesh R (2003) A review of literature on benchmarking. Benchmarking Int J 10(3): 176–209
Dubois D, Prade H (1980) Fuzzy sets and systems: theory and applications. Academic Press, New York
Francis G, Holloway J (2007) What have we learned? Themes from the literature on best-practice benchmarking. Int J Manage Rev 9(3): 171–189
Gómez A, Juristo N, Montes C, Pazos J (1997) Ingeniería del conocimiento. Centro de Estudios Ramón Areces, Madrid
Herrera F, Herrera-Viedma E (1997) Aggregation operators for linguistic weighted information. IEEE Trans Syst Man Cybern 27(5): 646–656
Herrera F, Martínez LA (2000) 2-tuple fuzzy linguistic representation model for computing with words. IEEE Trans Fuzzy Syst 8(6): 746–752
Hodge VJ, Austin J (2005) A binary neural k-nearest neighbour technique. Knowl Inf Syst 8(3): 276–291
Holsapple CW, Joshi KD (2004) A formal knowledge management ontology: conduct, activities, resources, and influences. J Am Soc Inf Sci Technol 55(7): 593–612
Leekwijck W, Kerre EE (1999) Defuzzification: criteria and classification. Fuzzy Sets Syst 108(2): 159–178
Leung CW, Chan SC, Chung F (2006) A collaborative filtering framework based on fuzzy association rules and multiple-level similarity. Knowl Inf Syst 10(3): 357–381
Liebowitz J (1999) Knowledge management handbook. CRC Press, Florida
Massa S, Testa S (2004) Innovation or imitation? Benchmarking: a knowledge-management process to innovate services. Benchmarking Int J 11(6): 610–620
Pedrycz W, Gomide F (1998) An introduction to fuzzy sets: analysis and design. MIT Press, Cambridge
Pulat BM (1994) Process improvements through benchmarking. TQM Mag 6(2): 37–40
Schnaars S (1994) Managing imitation strategies. The Free Press, New York
Spendolini M (1992) The benchmarking book. Amacom Books, New York
Watson GH (2008) Benchmarking in project definition. In: Ruggeri F, Kenett R, Faltin FW (eds) Encyclopedia of statistics in quality and reliability. Wiley, Chichester, pp 207–217
Wiig K (1997) Knowledge management: where did it come from and where will it go?. Expert Syst Appl 13(1): 1–14
Wohlin C, Aurum A, Peterson H, Shull F, Ciolkowski M (2002) Software inspection benchmarking—a qualitative and quantitative comparative opportunity. In: Proceedings of 8th international software metrics symposium. IEEE Computer Society, Washington, pp 118–127
Xiong N, Litz L, Ressom H (2002) Learning premises of fuzzy rules for knowledge acquisition in classification problems. Knowl Inf Syst 4(1): 96–111
Yager RR (1995) An approach to ordinal decision making. Int J Approx Reason 12: 237–261
Yang J, Cheung WK, Chen X (2009) Learning element similarity matrix for semi-structured document analysis. Knowl Inf Syst 19(1): 53–78
Yasin MM (2002) The theory and practice of benchmarking: then and now. Benchmarking Int J 9(3): 217–243
Zadeh LA (1965) Fuzzy sets. Inf Control 8(3): 338–353
Zhang WG, Xiao WL (2009) On weighted lower and upper possibilistic means and variances of fuzzy numbers and its application in decision. Knowl Inf Syst 18(3): 311–330
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Andrade, J., Ares, J., Martínez, M.A. et al. A fuzzy approach for solving a critical benchmarking problem. Knowl Inf Syst 24, 59–75 (2010). https://doi.org/10.1007/s10115-009-0219-x
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DOI: https://doi.org/10.1007/s10115-009-0219-x