Advertisement

Challenges of Performance Evaluation in Practice

  • Harald DyckhoffEmail author
  • Rainer Souren
Chapter
Part of the SpringerBriefs in Business book series (BRIEFSBUSINESS)

Abstract

The previous two main chapters of this brief book describe a fundamental theory and an important methodology of non-financial performance evaluation. Traditional production theories and data envelopment models are generalised in such a way that key performance indicators are represented by multiple, a priori incommensurable cost and benefit functions which are defined on inputs and outputs of the activities to be evaluated. This last chapter sketches selected further aspects and challenges that are essential for performance evaluation in practice. In particular, it is concerned with (1) balance and specialisation as performance categories in addition to effectiveness and efficiency; (2) the identification, selection and qualitative differentiation of the inputs and outputs that determine the considered costs and benefits; (3) the influence of the choice of compared activities and of exogenous weighting factors on the relative performance; (4) approaches to detect dependencies of the performance indicators from the inputs and outputs, even though they are often not quantifiable; and (5) more comprehensive concepts and systems of performance management and management accounting which may include the topics covered in this book.

Keywords

Balance versus specialisation Balanced scorecard Independence of irrelevant alternatives Key performance indicator Performance management Qualitative dominance 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Afsharian M, Ahn H, Neumann L (2016) Generalized DEA: An approach for supporting input/output factor determination in DEA. Benchmarking: An International Journal 23:1892–1909Google Scholar
  2. Ahn H (2001) Applying the Balanced Scorecard concept: An experience report. Long Range Planning 34:441–461CrossRefGoogle Scholar
  3. Ahn H, Le MH (2014) An insight into the specification of the input-output set for DEA-based bank efficiency measurement. Management Review Quarterly 64:3–37CrossRefGoogle Scholar
  4. Ahn H, Le MH (2016) Decision-oriented performance measurement framework. In: Ahn H, Clermont M, Souren R (ed) Nachhaltiges Entscheiden – Beiträge zum multiperspektivischen Performancemanagement von Wertschöpfungsprozessen. Springer Gabler, Wiesbaden, pp 369–383Google Scholar
  5. Belton V, Stewart TJ (1999) DEA and MCDA – Competing or complementary approaches? In: Meskens N, Roubens M (ed) Advances in Decision Analysis. Springer, Dordrecht, pp 87–103CrossRefGoogle Scholar
  6. Biazzo S, Garengo P (2012) Performance Measurement with the Balanced Scorecard: A Practical Approach to Implementation within SMEs. SpringerBriefs in Business 6, Berlin/HeidelbergGoogle Scholar
  7. Charnes A, Cooper WW, Rhodes E (1978) Measuring efficiency of decision making units. European Journal of Operational Research 2:429–444CrossRefGoogle Scholar
  8. Cook WD, Zhu J (2007) Classifying inputs and outputs in Data Envelopment Analysis. European Journal of Operational Research 180:692–699CrossRefGoogle Scholar
  9. Cook WD, Tone K, Zhu J (2014) Data envelopment analysis: Prior to choosing a model. Omega 44:1–4CrossRefGoogle Scholar
  10. Dyckhoff H, Ahn H (1998) Integrierte Alternativengenerierung und -bewertung. Die Betriebswirtschaft 58:49–63Google Scholar
  11. Dyckhoff H, Gutgesell S (2015) Properties of DEA-integrated balance and specialization measures. OR Spectrum 37:503‒527CrossRefGoogle Scholar
  12. Dyckhoff H, Souren R, Elyas A (2011) Reference data models for the strategic controlling of waste management firms: A new methodology for industry solution design. Business & Information Systems Engineering 3:65−75Google Scholar
  13. Dyckhoff H, Clermont M, Dirksen A, Mbock E (2013) Measuring balanced effectiveness and efficiency of German business schools’ research performance. In: Dilger A, Dyckhoff H, Fandel G (ed) Performance Management im Hochschulbereich. Springer Gabler, Wiesbaden, pp 39–60CrossRefGoogle Scholar
  14. Dyckhoff H, Mbock E, Gutgesell S (2015a) Distance-based measures of specialization and balance in multi-criteria: A DEA-integrated method. Journal of Multi-Criteria Decision Analysis 22:197–212CrossRefGoogle Scholar
  15. Dyckhoff H, Quandel A, Waletzke K (2015b) Rationality of eco-efficiency methods: Is the BASF analysis dependent on irrelevant alternatives? International Journal of Life Cycle Assessment 20:1557–1567CrossRefGoogle Scholar
  16. Dyson RG, Allen R, Camanho AS, Podinovski VV, Sarrico CC, Shale EA (2001) Pitfalls and protocols in DEA. European Journal of Operational Research 132:245–259CrossRefGoogle Scholar
  17. Eisenführ F, Weber M, Langer T (2010) Rational Decision Making. Springer, Berlin et al.CrossRefGoogle Scholar
  18. Emrouznejad A, De Witte K (2010) COOPER-framework: A unified process for non-parametric projects. European Journal of Operational Research 207:1573–1586CrossRefGoogle Scholar
  19. Hevner AR, March ST, Park J, Ram S (2004) Design science in information science research. MIS Quarterly 28:75–105CrossRefGoogle Scholar
  20. Kaplan RS, Norton DP (1996) The Balanced Scorecard: Translating Strategy into Action. Harvard, CambridgeGoogle Scholar
  21. Keeney RL (1992) Value-Focused Thinking: A Path to Creative Decisionmaking. Harvard, CambridgeGoogle Scholar
  22. Kleine A, Saling P, von Hauff M (2004) Ökoeffizienz-Analyse zu Entsorgungsoptionen Mineralölkohlenwasserstoff-kontaminierter Böden – Bodenbehandlung oder Deponierung? MainzGoogle Scholar
  23. Peffers K, Tuunanen T, Rothenberger MA, Chatterjee S (2008) A design science research methodology for information systems research. Journal of Management Information Systems 24:45–77CrossRefGoogle Scholar
  24. Saling P (2016) The BASF Eco-Efficiency Analysis: A 20-year Success Story. BASF SE, LudwigshafenGoogle Scholar
  25. Saling P, Kicherer A, Dittrich-Krämer B, Wittlinger R, Zombik W, Schmidt I, Schrott W, Schmidt S (2002) Eco-efficiency Analysis by BASF: The Method. International Journal of Life Cycle Assessment 7:203–218CrossRefGoogle Scholar
  26. Seiford LM, Zhu J (2002) Modeling undesirable factors in efficiency evaluation. European Journal of Operational Research 142:16–20CrossRefGoogle Scholar
  27. Souren R (1996a) Theorie betrieblicher Reduktion. Physica, HeidelbergCrossRefGoogle Scholar
  28. Souren R (1996b) Analyse, Planung und Steuerung stofflicher Reduktionsprozesse bei inhomogener Abfallqualität. Umweltwirtschaftsforum 4(4):13–19Google Scholar
  29. Uhlman BW, Saling P (2010) Measuring and Communicating Sustainability through Eco-Efficiency Analysis. In: CEP December 2010 (special expanded web-only version), American Institute of Chemical Engineers, CEP magazine article, December 2010:17–26dGoogle Scholar
  30. Uhlman BW, Saling P (2017) The BASF eco-efficiency toolbox: Holistic evaluation of sustainable solutions. In: Abraham M (ed) Encyclopedia of Sustainable Technologies, vol 1. Elsevier, Amsterdam, pp 131–144CrossRefGoogle Scholar
  31. Wagner JM, Shimshak DG (2007) Stepwise selection of variables in data envelopment analysis: Procedures and managerial perspectives. European Journal of Operational Research 180:57–67CrossRefGoogle Scholar
  32. Wenzel H (1998) Application dependency of LCA methodology: Key variable and their mode of influencing the method. International Journal of Life Cycle Assessment 3:281–288CrossRefGoogle Scholar
  33. Wojcik V (2018) Performanceanalyse mittels Verallgemeinerter Data Envelopment Analysis: Vorgehensmodell und Evaluation. Dr. Kovač, HamburgGoogle Scholar
  34. Wojcik V, Dyckhoff H, Gutgesell S (2017) The desirable input of undesirable factors in Data Envelopment Analysis. Annals of Operations Research 259:461–484CrossRefGoogle Scholar
  35. Wojcik V, Dyckhoff H, Clermont M (2019) Is data envelopment analysis a suitable tool for performance measurement and benchmarking in non-production contexts? Business Research 12(2): 559–595 (DOI  https://doi.org/10.1007/s40685-018-0077-z; open access)CrossRefGoogle Scholar
  36. Yin RK (2003) Case Study Research: Design and Methods. 3rd ed, Sage, Thousand OaksGoogle Scholar

Copyright information

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Business and EconomicsRWTH Aachen UniversityRheineGermany
  2. 2.Group of Sustainable Production and Logistics ManagementIlmenau University of TechnologyIlmenauGermany

Personalised recommendations