Uncertainty Components in Performance Measures

  • Sérgio Dinis Teixeira de Sousa
  • Eusébio Manuel Pinto Nunes
  • Isabel da Silva Lopes
Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 229)

Abstract

Data quality is a multi-dimensional concept and this research will explore its impact in performance measurement systems (PMSs). Despite the large numbers of publications on the design of PMSs and the definition of critical success factors to develop Performance Measures (PMs), from the data user perspective there are possibilities of finding data quality problems, that may have a negative impact in decision making. This work identifies and classifies uncertainty components of PMSs, and proposes a qualitative method for PMs’ quality assessment. Fuzzy PMs are used to represent uncertainty that is present in any physical system. A method is also proposed to calculate an indicator of the compliance between a fuzzy PM and its target value, that can serve as a risk indicator for the decision-maker.

Keywords

Data quality Fuzzy sets Performance measurement  Performance measures Quality management Risk management Uncertainty 

Notes

Acknowledgments

This work was financed with FEDER Funds by Programa Operacional Fatores de Competitividade—COMPETE and by National Funds by FCT—Fundação para a Ciência e Tecnologia, Project: FCOMP-01-0124-FEDER.

References

  1. 1.
    Kaplan R, Norton D (1992) The balanced scorecard—measures that drive performance. Harvard Bus Rev 69: 71–79Google Scholar
  2. 2.
    Bourne M (2004) Handbook of performance measurement. Gee Publishing, LondonGoogle Scholar
  3. 3.
    Verweire K, Van den Berghe L (2003) Integrated performance management: adding a new dimension. Manag Decis 41(8):782–790Google Scholar
  4. 4.
    Pipino LL, Lee YW, Wang R (2002) Data quality assessment. J Commun ACM 45(4):211–218CrossRefGoogle Scholar
  5. 5.
    Lee YW, Strong DM (2002) AIMQ: a methodology for information quality assessment. Inf Manag 40(2):133–146CrossRefGoogle Scholar
  6. 6.
    Stvilia B, Gasser L (2007) A framework for information quality assessment. J Am Soc Inf Sci Technol 58(12):1720–1733CrossRefGoogle Scholar
  7. 7.
    Ge M, Helfert M, Abramowicz W, Fensel D (2008) Data and information quality assessment in information manufacturing systems. Springer, AustriaGoogle Scholar
  8. 8.
    Wand Y, Wang RY (1996) Anchoring data quality dimensions in ontological foundations. Commun ACM 39(11):86–95CrossRefGoogle Scholar
  9. 9.
    Dai Y, Su Y (2009) Assuring the information quality of production planning and control in Tobacco Industries. Fourth international conference on cooperation and promotion of information resources in science and technology, COINFO ’09Google Scholar
  10. 10.
    Madnick SE, Wang RY (2009) Overview and framework for data and information quality research. J Data Inf Qual 1(1):1–22Google Scholar
  11. 11.
    Yu C-S (2002) A GP-AHP method for solving group decision-making fuzzy AHP problems. Comput Oper Res 29(14):1969–2001MATHCrossRefGoogle Scholar
  12. 12.
    Durbach IN, Stewart TJ (2011) An experimental study of the effect of uncertainty representation on decision making. Eur J Oper Res 214(2):380–392CrossRefGoogle Scholar
  13. 13.
    Sousa SD, Nunes EP, Lopes IS (2012) Data quality assessment in performance measurement. Lecture notes in engineering and computer science: proceedings of the world congress on engineering, WCE 2012, London, UK, 4–6 July 2012, pp 1530–1535Google Scholar
  14. 14.
    Juran JM, Godfrey AB (1999) Juran’s quality handbook. McGraw-Hill, New YorkGoogle Scholar
  15. 15.
    Neely A, Gregory M, Platts K (1995) Performance measurement system design. Int J Oper Prod Manag 15(4):80–116CrossRefGoogle Scholar
  16. 16.
    Macpherson M (2001) Performance measurement in not-for-profit and public-sector organisations. Meas Bus Excell 5(2):13–17CrossRefGoogle Scholar
  17. 17.
    Seddon J (2002) Changing management thinking. Q2002—a world quality congress—46th EOQ congress, UKGoogle Scholar
  18. 18.
    Tenner A, DeToro I (1997) Process redesign. Addison-Wesley, HarlowGoogle Scholar
  19. 19.
    Kaplan RS, Norton DP (2001) The strategy-focused organization. Harvard Business School Press, Massachusetts, BostonGoogle Scholar
  20. 20.
    Wilcox M, Bourne M (2003) Predicting performance. Manag Decis 41(8):806–816CrossRefGoogle Scholar
  21. 21.
    Kanji G, Sá P (2002) Kanji’s business scorecard. Total Qual Manag 13(1):13–27CrossRefGoogle Scholar
  22. 22.
    Bititci U, Turner T, Begemann C (2000) Dynamics of performance measurement systems. Int J Oper Prod Manag 20(6):692–704CrossRefGoogle Scholar
  23. 23.
    Basu R (2001) New criteria of performance management. Meas Bus Excell 5(4):7–12CrossRefGoogle Scholar
  24. 24.
    Neely A, Adams C, Kennerley M (2002) The performance prism: the scorecard for measuring and managing business success. Financial Times Prentice Hall, LondonGoogle Scholar
  25. 25.
    Schalkwyk J (1998) Total quality management and the performance measurement barrier. TQM Mag 10(2):124–131CrossRefGoogle Scholar
  26. 26.
    Ghalayini A, Noble J, Crowe T (1997) An integrated dynamic performance measurement system for improving manufacturing competitiveness. Int J Prod Econ 48:207–225CrossRefGoogle Scholar
  27. 27.
    Globerson S (1985) Issues in developing a performance criteria system for an organisation. Int J Prod Res 23(4):639–646CrossRefGoogle Scholar
  28. 28.
    Franco M, Bourne M (2003) Factors that play a role in "managing through measures”. Manag Decis 41(8):698–710CrossRefGoogle Scholar
  29. 29.
    Batini C, Cappiello C (2009) Methodologies for data quality assessment and improvement. J ACM Comput Surv 41(3):1–52CrossRefGoogle Scholar
  30. 30.
    Galway LA, Hanks CH (2011) Classifying data quality problems. IAIDQ’s Inf Data Qual Newsl 7(4):1–3Google Scholar
  31. 31.
    JCGM/WG 1 (2008). JCGM 100:2008—GUM 1995 with minor corrections—evaluation of measurement data—guide to the expression of uncertainty in measurement, JCGMGoogle Scholar
  32. 32.
    Coolen FPA (2004) On the use of imprecise probabilities in reliability. Qual Reliab Eng Int 20(3):193–202CrossRefGoogle Scholar
  33. 33.
    Nunes E, Faria F, Matos M (2006) Using fuzzy sets to evaluate the performance of complex systems when parameters are uncertain. Proceedings of safety and reliability for managing risk, vol 3. Lisbon, pp 2351–2359Google Scholar
  34. 34.
    ISO-1012 (2003) ISO 10012 measurement management systems—requirements for measurement processes and measuring equipment, ISOGoogle Scholar
  35. 35.
    Wazed MA, Ahmed S (2009) Uncertainty factors in real manufacturing environment. Aust J Basic Appl Sci 3(2):342–351Google Scholar
  36. 36.
    Mula J, Poler R, García-Sabater J, Lario FC (2006) Models for production planning under uncertainty: a review. Int J Prod Econ 103(1):271–285CrossRefGoogle Scholar
  37. 37.
    Petrovic D, Roy R, Petrovic R (1999) Supply chain modelling using fuzzy sets. Int J Prod Econ 59(103):443–453CrossRefGoogle Scholar
  38. 38.
    Herroelen W, Leus R (2005) Project scheduling under uncertainty: survey and research potentials. Eur J Oper Res 165(2):289–306MathSciNetMATHCrossRefGoogle Scholar
  39. 39.
    Petrovic D (2001) Simulation of supply chain behaviour and performance in an uncertain environment. Int J Prod Econ 71(103):429–438CrossRefGoogle Scholar
  40. 40.
    Lam K-C, Lam MC-K, Wang D (2008) MBNQA-oriented self-assessment quality management system for contractors: fuzzy AHP approach. Constr Manag Econ 26(5):447–461CrossRefGoogle Scholar
  41. 41.
    Hu AH, Hsu C-W, Kuo T-C, Wu W-C (2009) Risk evaluation of green components to hazardous substance using FMEA and FAHP. Expert Syst Appl 36(3, Part 2): 7142–7147Google Scholar
  42. 42.
    Bashiri M, Hosseininezhad SJ (2009) A fuzzy group decision support system for multifacility location problems. Int J Adv Manuf Technol 42(5–6):533–543CrossRefGoogle Scholar
  43. 43.
    Ross T (1995) Fuzzy logic with engineering applications. McGraw-Hill, New YorkMATHGoogle Scholar
  44. 44.
    Klir GJ (1995) Fuzzy sets and fuzzi logic: theory and applications Upper Saddle River. Prentice Hall, N.JGoogle Scholar
  45. 45.
    Sousa SD, Nunes EP, Lopes I (2011) On the characterisation of uncertainty in performance measurement systems. In: Putnik GD, Ávila P (eds) Business sustainability 2.0. Guimarães: School of Engineering, University of Minho; Porto: ISEP, School of Engineering, Polytechnic of Porto, pp 82–88Google Scholar
  46. 46.
    Zadeh LA (1978) Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst 1(1):3–28MathSciNetMATHCrossRefGoogle Scholar
  47. 47.
    Nunes E, Sousa SD (2009) Fuzzy performance measures in a high uncertainty context proceedings of the IX Congreso Galego de Estatística e Investigación de Operacións, OurenseGoogle Scholar
  48. 48.
    El-Baroudy I, Simonovic P (2003) New fuzzy performance indices for reliability analysis of water supply systems. Water Resources Research Report, The University of Western Ontario, Department of Civil and Environmental EngineeringGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Sérgio Dinis Teixeira de Sousa
    • 1
  • Eusébio Manuel Pinto Nunes
    • 1
  • Isabel da Silva Lopes
    • 1
  1. 1.Centro AlgoritmiUniversity of Minho BragaPortugal

Personalised recommendations