Implementing Fuzzy DEA into the PEM Model for Performance Evaluation of Project-based Organizations: A case study
الموضوعات :
Owais Torabi
1
,
Mohammad Milad Ahmadi
2
,
Ruhollah Tavallaei
3
1 - Assistant Professor, Department of Evaluation Management, Faculty of Management, Imam Hossein University
2 - PhD Student in Systems Management, Imam Hossein University
3 - Associate Professor, Department of Science and Technology Policy, Faculty of Management, Imam Hossein University
الکلمات المفتاحية: efficiency analysis, Petrochemical Industry, Fuzzy Data Envelopment Analysis (FDEA), Project Excellence Model (PEM), Petro-Chem Supply Chain, Project Management,
ملخص المقالة :
Project-based organizations in upstream industries hold a large share of national resources and play an important role in the development of a country. Performance evaluation of project-based organizations can help managers to use inputs effectively and smooth their way to achieving goals. There are many qualitative and quantitative indices to performance evaluation of project-based organizations. Efficiency calculation through Data Envelopment Analysis (DEA) is a common index for performance assessment in such firms. In the traditional DEA model crisp data is needed while, in the real world, most of the data are imprecise and uncertain. A major cause of uncertainty related to the non-quantifiable, incomplete, and unachievable information that caused fuzzy logic and fuzzy sets merge in different models like DEA. The main idea of the present study is to combine quantitative and qualitative approaches in performance appraisal to take advantage of both and achieve more accurate results; therefore, in this paper, a hybrid model based on Fuzzy Data Envelopment Analysis (FDEA) and Project Excellence Model (PEM) is proposed for performance evaluation in project-based organizations. First, performance assessment by the PEM model of Fuzzy data is accomplished. Then, implementing Fuzzy DEA into the PEM model is performed in which the inputs and outputs of the FDEA model are the PEM model criteria. The proposed hybrid model is used to evaluate 30 petrochemical companies in Iran. The comparison of the results of both models indicates a correlation coefficient of almost 0.90 at the significance level of 0.01 that shows an appropriate correlation between the two models.
Andersen, P., and Petersen, N. (1993). A Procedure for Ranking Efficient Units in Data Envelopment Analysis. Management Science. 39(10): 1261-1264.
Babazadeh, R., Toloo, M., Shamsi, M., and Khalili, M. (2020). A fuzzy data envelopment analysis method for performance evaluation of renewable feedstock suppliers. International Journal of Renewable Energy Resources. 9(2): 17-27.
Banker, R. D., Charnes, A. and Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science. 30(9): 1078-1092.
Bushuyev, S., and Verenych, O. (2018). Organizational Maturity and Project: Program and Portfolio Success. In Silvius, G., and Karayaz, G. (Ed.), Developing Organizational Maturity for Effective Project Management (pp. 104-127). IGI Global. Hershey, Pennsylvania.
Charnes, A., Cooper, W. W. and Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research. 2(6): 429-444.
Chen, Z., Ming, X., Wang, R., and Bao, Y. (2020). Selection of design alternatives for smart product service system: A rough-fuzzy data envelopment analysis approach. Journal of Cleaner Production. 273: 122931.
Cooney, R. C. (2020). Project Success Criteria and Project Success Factors in Information Technology Projects. Academy of Management Proceedings. 2020(1): 20687.
Cooper, W.W., Park, K.S., and Yu, G. (1999). IDEA and AR-IDEA: Models for Dealing with Imprecise Data in DEA. Management Science. 45(4): 597-607
Cooper, W.W., Lawrence, M. S., and Kaoru, T. (2007). Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software. Boston, MA: Springer.
Díaz, R.F., and Sanchez-Robles, B. (2020). Non-Parametric Analysis of Efficiency: An Application to the Pharmaceutical Industry. Mathematics. 8 (1522): 1-27.
EFQM team (2013). EFQM Excellence Model. Retrieved from: https://www.efqm.org/
Emrouznejad, A., and Tavana, M. (2014). Performance Measurement with Fuzzy Data Envelopment Analysis, Studies in Fuzziness and Soft Computing 309. Heidelberg: Springer-Verlag.
GPM (2014). IPMA Organisational Competence Baseline – The standards for moving organisations forward (IPMA OCB). Version 1.0. (in Dutch). Retrieved from: https://www.gpm-ipma.de/
Grau, N. (2011) Standards and excellence in project management – two sides of the same coin? XV International symposium on project management, YUPMA 2011, Zlatibor 10-12. June 2011. pp. 11-18.
Guo, P., and Tanaka, H. (2001). Fuzzy DEA: a perceptual evaluation method. Fuzzy Sets and Systems. 119(1): 149-160.
Hatami-Marbini, A., Tavana, M., Emrouznejad, A., and Saati, S. (2011) Efficiency measurement in fuzzy additive data envelopment analysis. International Journal of Industrial and Systems Engineering. 10(1): 1-20.
Henriques, L.C., Sobreiro, V.A., Kimura, H., and Mariano, E.B. (2020). Two-stage DEA in banks: Terminological controversies and future directions. Expert Systems with Applications. 161: 1-31.
Hıdıroğlu, D. (2019). Self- assessment Performance Measurement in Construction Companies: An Application of the EFQM Excellence Model on Processes and Customer Stages. Procedia Computer Science. 158: 844-851.
IPMA (2016). Organisational Competence Baseline – for Developing Competence in Managing by Projects (IPMA OCB). Version 1.1. Retrieved from: https://www.ipma.world/
Jaafari, A. (2007). Project and program diagnostics: A systemic approach. International Journal of Project Management. 25(8): 781- 790.
Kwak, Y. H., Sadatsafavi, H., Walewski, J., and Williams, N. L. (2015). Evolution of project-based organization: A case study. International Journal of Project Management. 33(80: 1652- 1664.
Kao, C., and Liu, S.T. (2000). Fuzzy efficiency measures in data envelopment analysis. Fuzzy Sets and Systems. 113(3): 427-437.
Kao, C., and Liu, S.T. (2003). A mathematical programming approach to fuzzy efficiency ranking. International Journal of Production Economics. 86(2): 145-154.
Lertworasirikul, S., Fang, S.C., Joines, J.A., and Nuttle, H.L.W. (2003). Fuzzy data envelopment analysis (DEA): a possibility approach. Fuzzy Sets and Systems. 139(2): 379-394.
Lertworasirikul, S., Fang, S.C, Nuttle, H.L.W., and Joines, J.A. (2003). Fuzzy BCC Model for Data Envelopment Analysis. Fuzzy Optimization and Decision Making. 2: 337–358.
Li, D. (2016). Perspective for smart factory in petrochemical industry. Computers and Chemical Engineering. 91: 136-148.
Lotfi, F. H., Ebrahimnejad, A., Vaez-Ghasemi, M., & Moghaddas, Z. (2020). Data envelopment analysis with R. Berlin, Germany: Springer International Publishing.
Lotfizadeh, A. (1965). Fuzzy Sets. Information and Control. 8(3): 338-353.
Matin, R.K., and Azizi, R. (2015). A unified network-DEA model for performance measurement of production systems. Measurement. 60: 186-193.
Moghaddas, Z., Amirteimoori, A., & Kazemi Matin, R. (2022). Selective proportionality and integer-valued data in DEA: an application to performance evaluation of high schools. Operational Research, 1-25.
Moghaddas, Z., Tosarkani, B. M., & Yousefi, S. (2021). A Developed Data Envelopment Analysis Model for Efficient Sustainable Supply Chain Network Design. Sustainability, 14(1), 262.
Moghaddas, Z., Vaez-Ghasemi, M., Hosseinzadeh Lotfi, F., Farzipoor Saen, R. (2020). Stepwise pricing in evaluating revenue efficiency in Data Envelopment Analysis: A case study in power plants. Scientia Iranica, Available Online from 08 June 2020.
Obradović, V., Kostić, S. C., and Mitrović, Z. (2016). Rethinking Project Management– Did We Miss Marketing Management? Procedia - Social and Behavioral Sciences. 226: 390- 397.
Pambudi, G., and Nananukul, N. (2019). A hierarchical fuzzy data envelopment analysis for wind turbine site selection in Indonesia. Energy Reports. 5: 1041-1047.
Rezaee, M. J., Jozmaleki, M., and Valipour, M. (2018). Integrating dynamic fuzzy C-means, data envelopment analysis and artificial neural network to online prediction performance of companies in stock exchange. Physica A: Statistical Mechanics and its Applications. 489: 78-93.
Sengupta, J. K. (1992). A fuzzy systems approach in data envelopment analysis. Computers and Mathematics with Applications. 24(8-9): 259-266.
Steel, P. (2016). The Baldrige Business Model. Retrieved from: http://www.baldrige21.com/Baldrige%20Model.html
Sueyoshi, T., Qu, J., Li, A., and Xie, C. (2020). Understanding the efficiency evolution for the Chinese provincial power industry: A new approach for combining data envelopment analysis-discriminant analysis with an efficiency shift across periods. Journal of Cleaner Production. 277. 122371.
Szabó, L. (2016). Sustainability, creativity and innovation in project management–Model development for assessing organizational performance through projects. Vezetéstudomány-Budapest Management Review, 47(10): 3-18.
Westerveld, E. (2003). The Project Excellence Model®: linking success criteria and critical success factors. International Journal of Project Management. 21(6): 411-418.
Yu, Y. (2019). Implementation of Transnational Public Private Partnerships: Key Issues and Development of a Model to Achieve Project Excellence. Department of Building and Real Estate, The Hong Kong Polytechnic University. Hong Kong.
Zawawi, D. (2007). Quantitative versus qualitative methods in social sciences: Bridging the gap. Integration and Dissemination. 1: 3-4.
