Evaluating and Ranking of EFQM Projects by Using Robust DEA and a Case Study of Health and Care Services

Authors

  • Aref Shayan * Department of Industrial Engineering, Islamic Azad University, Saveh Science and Research Branch, Iran.
  • Mahnaz Ahadzadeh Namin Department of Mathematics, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran. https://orcid.org/0000-0001-6589-8489
  • Seyyed Esmaeel Najafi Department of Industrial Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran. https://orcid.org/0000-0002-8734-5436

https://doi.org/10.22105/ahse.v2i1.28

Abstract

The European Foundation for Quality Management (EFQM) Excellence Model is a practical, non-prescriptive framework to improve organizations. There are several methods for evaluating the performance, which can be placed in two categories: Parametric and nonparametric. Including the non-parametric techniques is key in terms of certainty and uncertainty (Robust Data Envelopment Analysis (RDEA)) to evaluate the relative efficiency of Decision Making Units (DMU). Given the importance of performance measurement and organizational performance, it is necessary to have a model that, in addition to the qualitative approach, is also a quantitative approach. So, by combining a qualitative model, EFQM, and a quantitative model, DEA, offers a model for evaluating and ranking EFQM projects, while both methods have advantages as well as disadvantages, they minimize. Given that in most optimization problems, the uncertainty in the data is not considered, it is likely that reasonable answers that largely decrease the loss of confidence can be justified. Answer the question. RDEA such techniques are that in view of the uncertainty in the data with high probability justify answer the question. In this paper, we used the RDEA for ranking and evaluating the performance of EFQM Projects, then compared the results. 

Keywords:

European foundation for quality management, Data envelopment analysis, Robust optimization, Robust data envelopment analysis

References

  1. [1] Zargar, S., Faghani, K., & Mahmudi, G. (2011). Assessing the role of the EFQM excellence model in organizational viability. World applied sciences journal, 14(7), 987–995. https://www.researchgate.net/publication/268362405
  2. [2] Vallejo, P., Saura, R. M., Sunol, R., Kazandjian, V., Ureña, V., & Mauri, J. (2006). A proposed adaptation of the EFQM fundamental concepts of excellence to health care based on the PATH framework. International journal for quality in health care, 18(5), 327–335. https://doi.org/10.1093/intqhc/mzl037
  3. [3] Downey‐Ennis, K., & Harrington, D. (2002). In search of excellence in Irish health care. International journal of health care quality assurance, 15(2), 65–73. https://doi.org/10.1108/09526860210421464
  4. [4] Nabitz, U., Klazinga, N., & Walburg, J. A. N. (2000). The EFQM excellence model: European and Dutch experiences with the EFQM approach in health care. International journal for quality in health care, 12(3), 191–202. https://doi.org/10.1093/intqhc/12.3.191
  5. [5] Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(6), 429–444. https://doi.org/10.1016/0377-2217(78)90138-8
  6. [6] Linh Pham, T. (2011). Efficiency and productivity of hospitals in Vietnam. Journal of health organization and management, 25(2), 195–213. https://doi.org/10.1108/14777261111134428
  7. [7] Sadjadi, S. J., & Omrani, H. (2008). Data envelopment analysis with uncertain data: An application for Iranian electricity distribution companies. Energy policy, 36(11), 4247–4254. https://doi.org/10.1016/j.enpol.2008.08.004
  8. [8] Ben-Tal, A., & Nemirovski, A. (1999). Robust solutions of uncertain linear programs. Operations research letters, 25(1), 1–13. https://doi.org/10.1016/S0167-6377(99)00016-4
  9. [9] Bertsimas, D., & Sim, M. (2003). Robust discrete optimization and network flows. Mathematical programming, 98(1), 49–71. https://doi.org/10.1007/s10107-003-0396-4
  10. [10] Khaki, A. R., Sadjadi, S. J., Gharakhani, M., & Rashidi, S. (2012). Data envelopment analysis under uncertainty: A case study from public healthcare. African journal of business management, 6(24), 7096. https://doi.org/10.5897/AJBM11.591
  11. [11] Gharakhani, M., Kazemi, I., & Alizadeh Haji, H. (2011). A robust DEA model for measuring the relative efficiency of Iranian high schools. Management science letters, 1(3), 389–404. http://dx.doi.org/10.5267/j.msl.2011.01.002
  12. [12] Roghanian, E., & Foroughi, A. (2010). An empirical study of Iranian regional airports using robust data envelopment analysis. International journal of industrial engineering computations, 1(1), 65–72. http://dx.doi.org/10.5267/j.ijiec.2010.01.006
  13. [13] Shahroudi, K. (2009). The application of data envelopment analysis methodology to improve the benchmarking process in the EFQM business model (Case study: Automotive industry of Iran). Iranian journal of optimization, 1(1), 201. (In Persian).
  14. [14] EFQM. https://efqm.org/
  15. [15] Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management science, 30(9), 1078–1092. https://doi.org/10.1287/mnsc.30.9.1078
  16. [16] Ben-Tal, A., & Nemirovski, A. (2000). Robust solutions of linear programming problems contaminated with uncertain data. Mathematical programming, 88(3), 411–424. https://doi.org/10.1007/PL00011380
  17. [17] Bertsimas, D., & Sim, M. (2004). The price of robustness. Operations research, 52(1), 35–53. https://doi.org/10.1287/opre.1030.0065
  18. [18] Bertsimas, D., & Sim, M. (2006). Tractable approximations to robust conic optimization problems. Mathematical programming, 107(1), 5–36. https://doi.org/10.1007/s10107-005-0677-1
  19. [19] Andersen, P., & Petersen, N. C. (1993). A procedure for ranking efficient units in data envelopment analysis. Management science, 39(10), 1261–1264. https://doi.org/10.1287/mnsc.39.10.1261
  20. [20] Maritz, J. S. (1995). Distribution-free statistical methods, second edition. Taylor & Francis. https://doi.org/10.1201/9781003059905

Published

2025-02-27

Issue

Vol. 2 No. 1 (2025): Annals of Healthcare Systems Engineering

Section

Articles

How to Cite

Shayan, A., Ahadzadeh Namin, M., & Najafi, S. E. . (2025). Evaluating and Ranking of EFQM Projects by Using Robust DEA and a Case Study of Health and Care Services. Annals of Healthcare Systems Engineering, 2(1), 28-37. https://doi.org/10.22105/ahse.v2i1.28