Modelling performance management measures through statistics and system dynamics-based simulation

  1. Hanzel Grillo
  2. Francisco Campuzano-Bolarin
  3. Josefa Mula
Revista:
Dirección y organización: Revista de dirección, organización y administración de empresas

ISSN: 1132-175X

Año de publicación: 2018

Número: 65

Páginas: 20-35

Tipo: Artículo

DOI: 10.37610/DYO.V0I65.526 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Dirección y organización: Revista de dirección, organización y administración de empresas

Resumen

The objective of this paper is to establish a methodology that combines performance measurement, a statistical record of measures to identify any relations among them, and system dynamics-based simulation modeling with the aim of supporting operations decision systems. This methodology intends to provide the comprehensive analysis of performance in such a way that it also analyzes the sensitivity and optimization of certain metrics according to requirements in each case. In the literature, this appears as a poorly developed research area. Some relevant studies have been identified which have attempted this combination, but have not completely established it.

Referencias bibliográficas

  • 1. Akkermans HA, van Oorschot KE. 2004. Relevance assumed: a case study of balanced scorecard development using system dynamics. Journal of Operational Research Society 56: 931–941.
  • 2. Alfaro-Saiz J, Ortiz-Bas A, Rodríguez-Rodríguez R. 2007. Performance measurement system for enterprise networks. International Journal of Production and Performance Management, 56: 305–334.
  • 3. Angerhofer BJ, Angelides MC. 2006. A model and a performance measurement system for collaborative supply chains. Decision Support Systems 42: 283–301.
  • 4. Barnabè F. 2011. A “system dynamics based Balanced Scorecard” to support strategic decision making: Insights from a case study. International Journal of Production and Performance Management 60: 446–473.
  • 5. Bianchi C. 2012. Enhancing performance management and sustainable organizational growth through system-dynamics modelling. In Grösser S.N., Zeier R. (eds.) Systemic Management for intelligent organizations. Springer Berlin Heidelberg, 143-161.
  • 6. Boj JJ, Rodriguez-Rodriguez R, Alfaro-Saiz JJ. 2014. An ANP-multi-criteria-based methodology to link intangible assets and organizational performance in a Balanced Scorecard context. Decision Support Systems 68: 98–110.
  • 7. Burgess TF. 1998. Modelling the impact of reengineering with system dynamics. International Journal of Operations and Production Management 18: 950–963.
  • 8. Cai J, Liu X, Xiao Z, Liu J. 2009. Improving supply chain performance management: A systematic approach to analyzing iterative KPI accomplishment. Decision Support Systems 46: 512–521.
  • 9. Campuzano F, Mula J. 2011. Supply Chain Simulation: A System Dynamics Approach for Improving Performance. Springer, London.
  • 10. Chong IG, Jun CH. 2005. Performance of some variable selection methods when multicollinearity is presented. Chemometrics and Intelligent Laboratory Systems 78: 103–112.
  • 11. Cohen J, Cohen P, West SG, Aiken LS. 2013. Applied Multiple Regression/Correlation Analysis for Behavioral Sciences. Routledge, New Jersey.
  • 12. Cook RD. 1977. Detection of influential observation in linear regression. Technometrics 19: 15–18.
  • 13. Draper NR, Smith H. 1998. Applied Regression Analysis. 3rd edition. Wiley, New York.
  • 14. Durbin J, Watson GS. 1950. Testing for serial correlation in least squares regression: I. Biometrika 37: 409–428.
  • 15. Field A. 2013. Discovering Statistics Using IBM SPSS Statistics. Sage, London.
  • 16. Forrester JW. 1961. Industrial Dynamics. The MIT Press. Massachusetts Institute of Technology, Cambridge, Massachusetts.
  • 17. Gunasekaran A, Patel C, McGaughey RE. 2004. A framework for supply chain performance measurement. International Journal of Production Economics 87: 333– 347.
  • 18. Hazewinkel M. 2001. Encyclopaedia of Mathematics, Supplement III. Springer, Netherlands.
  • 19. Jusoh R, Nasir Ibrahim D, Zainuddin Y. 2008. The per- formance consequence of multiple performance measures usage: Evidence from the Malaysian manufacturers. International Journal of Production and Performance Management 57: 119-136.
  • 20. Kleijnen JPC, Smits MT. 2003. Performance metrics in supply chain management. Journal of the Operational Research Society 54: 507–514.
  • 21. Kutner MH, Nachtsheim C, Neter J. 2004. Applied Linear Regression Models. McGraw-Hill/Irwin.
  • 22. Mora-Monge CA, Subba-Rao S, Gonzalez ME, Sohal AS. 2006. Performance measurement of AMT: a cross regional study. Benchmarking International Journal 13: 135–146.
  • 23. Mula J, Campuzano-Bolarin F, Díaz-Madroñero M, Carpio KM. 2013. A system dynamics model for the supply chain procurement transport problem: comparing spreadsheets, fuzzy programming and simulation approaches. International Journal of Production Research 51: 4087–4104.
  • 24. Nudurupati SS, Bititci US, Kumar V, Chan FTS. 2011. State of the art literature review on performance measurement. Computers and Industrial Engineering 60: 279–290.
  • 25. Otto A, Kotzab H. 2003. Does supply chain management really pay? Six perspectives to measure the performance of managing a supply chain. European Journal of Operational Research 144: 306-320.
  • 26. Rodriguez RR, Saiz JJA, Bas A. 2009. Quantitative relationships between key performance indicators for supporting decision-making processes. Computers and Industry 60: 104–113.
  • 27. Rodriguez-Rodriguez R, Alfaro Saiz JJ, Ortiz Bas A, Carot JM, Jabaloyes JM. 2010. Building internal business scenarios based on real data from a performance measurement system. Technological Forecasting and Social Change 77: 50–62.
  • 28. Santos SP, Belton V, Howick S. 2002. Adding value to performance measurement by using system dynamics and multicriteria analysis. International Journal of Operations and Production Management 22: 1246–1272.
  • 29. Sousa CM. 2004. Export performance measurement: an evaluation of the empirical research in the literature. Journal of the Academic of Marketing Science 9: 1–23.
  • 30. Sterman JD. 2000. Business Dynamics: Systems Thinking and Modeling for a Complex World. Irwin/McGrawHill, Boston.
  • 31. Tung A, Baird K, Schoch HP. 2011. Factors influencing the effectiveness of performance measurement systems. International Journal of Operation and Production Management 31: 1287–1310.
  • 32. Verdecho MJ, Alfaro-Saiz JJ, Rodriguez-Rodriguez R, Ortiz-Bas A. 2012. A multi-criteria approach for managing inter-enterprise collaborative relationships. Omega 40: 249–263.
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