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Properties of Indicators

  • Fiorenzo Franceschini
  • Maurizio Galetto
  • Domenico Maisano
Chapter
Part of the Management for Professionals book series (MANAGPROF)

Abstract

Indicators are helpful tools to represent (complex) processes, supporting evaluations and decisions. Unfortunately, selecting “good” indicators is not so trivial for at least two reasons: (i) there are not organic methods to support this activity and (ii) the success of this activity may depend on the complexity of the process of interest and the experience/intuition of users. The aim of the present chapter is to provide a taxonomy of some desirable properties of indicators, trying to answer several research questions, such as: “How many indicators should be used for representing a certain process?”, “Is there an optimal set of indicators?”, and “Can we aggregate/fuse multiple (sub)indicators into a single one?”. Description is accompanied by many pedagogical examples and an operational procedure to support the construction of indicators.

References

  1. Brown, M. G. (1996). Keeping score: Using the right metrics to drive world-class performance. New York: Quality Resources, CRC Press.Google Scholar
  2. Caplice, C., & Sheffi, Y. (1994). A review and evaluation of logistics metrics. The International Journal of Logistics Management, 5(2), 11–28.CrossRefGoogle Scholar
  3. Caplice, C., & Sheffi, Y. (1995). A review and evaluation of logistics performance measurement systems. The International Journal of Logistics Management, 6(1), 61–64.CrossRefGoogle Scholar
  4. Denton, D. K. (2005). Measuring relevant things. International Journal of Productivity and Performance Management, 54(4), 278–287.CrossRefGoogle Scholar
  5. Edwards, J. B. (1986). The use of performance measures. Montvale, NJ: National Association of Accountants.Google Scholar
  6. Finkelstein, L. (2003). Widely, strongly and weakly defined measurement. Measurement, 34(1), 39–48.CrossRefGoogle Scholar
  7. Flapper, S. D. P., Fortuin, L., & Stoop, P. P. M. (1996). Toward consistent performance measurement systems. International Journal of Operations and Production Management, 16(7), 27–37.CrossRefGoogle Scholar
  8. Franceschini, F. (2001). Dai Prodotti ai Servizi. Le nuove frontiere per la misura della qualità. Torino: UTET Libreria.Google Scholar
  9. Franceschini, F., & Galetto, M. (2001). A new approach for evaluation of risk priorities of failure modes in FMEA. International Journal of Production Research, 39(13), 2991–3002.CrossRefGoogle Scholar
  10. Franceschini, F., Galetto, M., Maisano, D., & Mastrogiacomo, L. (2008). Properties of performance indicators in operations management: A reference framework. International Journal of Productivity and Performance Management, 57(2), 137–155.CrossRefGoogle Scholar
  11. Franceschini, F., Galetto, M., Maisano, D., & Mastrogiacomo, L. (2012). The success-index: An alternative approach to the h-index for evaluating an individual’s research output. Scientometrics, 92(3), 621–641.CrossRefGoogle Scholar
  12. Franceschini, F., Galetto, M., Maisano, D., & Viticchiè, L. (2006a). The condition of uniqueness in manufacturing process representation by performance/quality indicators. Quality and Reliability Engineering International, 22(5), 567–580.CrossRefGoogle Scholar
  13. Franceschini, F., Galetto, M., & Maisano, D. (2006b). Classification of performance and quality indicators in manufacturing. International Journal of Services and Operations Management, 2(3), 294–311.CrossRefGoogle Scholar
  14. Franceschini, F., & Maisano, D. (2010). The citation triad: An overview of a scientist’s publication output based on Ferrers diagrams. Journal of Informetrics, 4(4), 503–511.CrossRefGoogle Scholar
  15. Franceschini, F., & Maisano, D. (2017). Critical remarks on the Italian research assessment exercise VQR 2011–2014. Journal of Informetrics, 11(2), 337–357.CrossRefGoogle Scholar
  16. Galbraith, L., & Greene, T. J. (1995). Manufacturing system performance sensitivity to selection of product design metrics. Journal of Manufacturing Systems, 14(2), 71–79.CrossRefGoogle Scholar
  17. Galbraith, L., Miller, W. A., & Greene, T. J. (1991). Pull system performance measures: A review of approaches for system design and control. Production Planning and Control, 2(1), 24–35.CrossRefGoogle Scholar
  18. Gunasekaran, A., & Kobu, B. (2007). Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995–2004) for research and applications. International Journal of Production Research, 45(12), 2819–2840.CrossRefGoogle Scholar
  19. Hauser, J., & Katz, G. (1998). Metrics: You are what you measure! European Management Journal, 16(5), 517–528.CrossRefGoogle Scholar
  20. Hazewinkel, M. (2013). Encyclopaedia of mathematics (Vol. 2, C). An updated and annotated translation of the soviet “mathematical encyclopaedia”. Berlin: Springer Science & Business Media.Google Scholar
  21. Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences, 102(46), 16569–16572.CrossRefGoogle Scholar
  22. Joint Research Centre-European Commission. (2008). Handbook on constructing composite indicators: Methodology and user guide. OECD Publishing. ISBN: 9789264043459.Google Scholar
  23. Juran, J. M. (1988). Juran on planning for quality. New York: The Free Press.Google Scholar
  24. Kaplan, R. S., & Norton, D. (1996). The balanced scorecard. Cambridge, MA: Harvard Business School Press.Google Scholar
  25. Kaydos, W. (1999). Operational performance measurement: Increasing total productivity. Boca Raton, FL: St. Lucie Press.Google Scholar
  26. Kearney, A. T. (1991). Measuring and improving productivity in the logistics process: Achieving customer satisfaction breakthroughs. Chicago: Council of Logistics Management.Google Scholar
  27. Keeney, R., & Raiffa, H. (1976). Decisions with multiple objectives: Preference and value tradeoffs. New York: Wiley.Google Scholar
  28. Lohman, C., Fortuin, L., & Wouters, M. (2004). Designing a performance measurement system: A case study. European Journal of Operational Research, 156, 267–286.CrossRefGoogle Scholar
  29. Maskell, B. H. (1991). Performance measurement for world class manufacturing. Cambridge, MA: Productivity Press.Google Scholar
  30. Melnyk, S. A., Stewart, D. M., & Swink, M. (2004). Metrics and performance measurement in operations management: Dealing with the metrics maze. Journal of Operations Management, 22, 209–217.CrossRefGoogle Scholar
  31. Mentzer, J. T., & Konrad, B. P. (1991). An efficiency/effectiveness approach to logistics performance analysis. Journal of Business Logistics, 12(1), 33.Google Scholar
  32. Mock, T. J., & Grove, H. D. (1979). Measurement, accounting and organizational information. New York: Wiley.Google Scholar
  33. Narens, L. (1996). A theory of ratio magnitude estimation. Journal of Mathematical Psychology, 40(2), 109–129.CrossRefGoogle Scholar
  34. Narens, L. (2002). A meaningful justification for the representational theory of measurement. Journal of Mathematical Psychology, 46(6), 746–768.CrossRefGoogle Scholar
  35. Neely, A., Gregory, M., & Platts, K. (1995). Performance measurement system design. International Journal of Operations and Production Management, 4, 80–116.CrossRefGoogle Scholar
  36. Nevem Workgroup. (1989). Performance indicators in logistics. Bedford: IFS Ltd./Springer.Google Scholar
  37. New, C. C., & Szwejczewski, M. (1995). Performance measurement and the focused factory: Empirical evidence. International Journal of Operations and Production Management, 15(4), 63–79.CrossRefGoogle Scholar
  38. Perrin, B. (1998). Effective use and misuse of performance measurement. American Journal of Evaluation, 19(3), 367–379.CrossRefGoogle Scholar
  39. Roberts, F. S. (1979). Measurement theory. Reading, MA: Addison-Wesley.Google Scholar
  40. Roy, B., & Bouyssou, D. (1993). Aide Multicritère à la Décision: Méthodes et Cas. Paris: Economica.Google Scholar
  41. Smith, D. (2000). The measurement nightmare: How the theory of constraints can resolve conflicting strategies, policies, and measures. Boca Raton, FL: St. Lucie Press.Google Scholar
  42. Stevens, S. S. (1946). On the theory of scales of measurement. Science, 103(2684), 677–680.CrossRefGoogle Scholar
  43. Stevens, S. S. (1951). Mathematics, measurement and psychophysics. In S. S. Stevens (Ed.), Handbook of experimental psychology (pp. 1–49). New York: Wiley.Google Scholar
  44. Stockburger, D. W. (2016). Introductory statistics: Concepts, models and applications (3rd ed.). Retrieved September 2018, from http://psychstat3.missouristate.edu/Documents/IntroBook3/sbk.htm.
  45. U.S. Department of Energy – PBM SIG (Performance-Based Management Special Interest Group). (2012). Performance-based management handbook of techniques and tools–How to measure performance (revised). Retrieved September 2018, from http://www.orau.gov/pbm
  46. Winston, J. A. (1999). Performance indicators – Promises unmet: A response to Perrin. American Journal of Evaluation, 20(1), 95–99.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Fiorenzo Franceschini
    • 1
  • Maurizio Galetto
    • 1
  • Domenico Maisano
    • 1
  1. 1.Department of Management and Production Engineering (DIGEP)Politecnico di TorinoTurinItaly

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