Multistage Data Envelopment Analysis Model for Malmquist Productivity Index Using Grey's System Theory to Evaluate Performance of Electric Power Supply Chain in Iran
Evaluation of organizational performance is among the most important measures that help organizations and entities continuously improve their efficiency. Organizations can use the existing data and results from the comparison of units under investigation to obtain an estimation of their performance. The Malmquist Productivity Index (MPI) is an important index in the evaluation of overall productivity, which considers technological developments and technical efficiency at the same time. This article proposed a model based on the multistage MPI, considering limited data (Grey’s theory). This model can evaluate the performance of units using limited and uncertain data in a multistage process. It was applied by the electricity market manager to Iran’s electric power supply chain (EPSC), which contains uncertain data, to evaluate the performance of its actors. Results from solving the model showed an improvement in the accuracy of future performance of the units under investigation, using the Grey’s system theory. This model can be used in all case studies, in which MPI is used and there are limited or uncertain data.
[1] J. Johnes, Data envelopment analysis and its application to the measurement of efficiency in higher education, Econ. Educ. Rev. 5 (3) (2006) 273–288.
[2] R.D. Banker, A. Charnes, W.W. Cooper, Some models for estimating technical and scale inefficiencies in data envelopment analysis, Manag. Sci. 30 (9) (1984)
[3] M.L. Song, S.H. Wang, DEA decomposition of China’s environmental efficiency based on search algorithm, Applied Mathematics and Computation, 247( 2014) 562-572
[4] M. Jim´enez, Carlos R. G. Alonso, L.S. Carulla, V.F. Rodr´ıguez, Evaluation of system efficiency using the Monte Carlo DEA: the case of Small Health Areas, European Journal of Operational Research
[5] H. Leleua, J. Moisesc, V.G. Valdmanisd, How do payer mix and technical inefficiency affect hospital profit? A weighted DEA approach, Operations Research for Health Care, 3(4) 2014 231-237
[6] G.Vlontzosa, S.Niavisa, B.Manosb, A DEA approach for estimating the agricultural energy and environmental efficiency of EU countries, Renewable and Sustainable Energy Reviews, 40 (2014) 91-96
[7] P.J. Sher, P.Y. Yang, The effects of innovative capabilities and R&D clustering on firm performance, the evidence of Taiwan's semiconductor industry, Technovation 25 (2005) 33–43.
[8] W.T. Lin, H.J. Yang, M.Y. Lin, The implementation of enterprise resource planning and product data management in semiconductor related industries, an empirical study in Taiwan, Int. J. Manage. 23 (1) (2006) 117–131.
[9] Mingzhi Mao, E.C. Chirwa, Application of grey model GM(1, 1) to vehicle fatality risk estimation, Technological Forecasting and Social Change, Volume 73, Issue 5, 2006, Pages 588-605.
[10] Yu-Shan Chen, Bi-Yu Chen, Applying DEA, MPI, and grey model to explore the operation performance of the Taiwanese wafer fabrication industry, Technological Forecasting and Social Change, Volume 78, Issue 3, 2011, Pages 536-546.
[11] H.V. Trivedi, J.K. Singh, Application of Grey System Theory in the Development of a Runoff Prediction Model, Biosystems Engineering, Volume 92, Issue 4, 2005, Pages 521-526.
[12] Wenbin Liu, Zhongbao Zhou, Chaoqun Ma, Debin Liu, Wanfang Shen, Two-stage DEA models with undesirable input-intermediate-outputs, Omega, Volume 56, 2015, Pages 74-87.
[13] Stephane Mussard, Nicolas Peypoch, On multi-decomposition of the aggregate Malmquist productivity index, Economics Letters, Volume 91, Issue 3, 2006,Pages 436-443.
[14] Filipa Da Silva Fernandes, Charalampos Stasinakis, Valeriya Bardarova, Two-stage DEA-Truncated Regression: Application in banking efficiency and financial development, Expert Systems with Applications, Volume 96, 2018, Pages 284-301.
[15] Reza Kiani Mavi, Reza Farzipoor Saen, Mark Goh, Joint analysis of eco-efficiency and eco-innovation with common weights in two-stage network DEA: A big data approach, Technological Forecasting and Social Change, 2018.
[16] Naser Amani, Hadi Bagherzadeh Valami, Ali Ebrahimnejad Application of Malmquist productivity index with carry-overs in power industry, Alexandria Engineering Journal, 2018.
[17] Visakh Sakthidharan, Sunitha Sivaraman, Impact of operating cost components on airline efficiency in India: A DEA approach, Asia Pacific Management Review, 2018.
[18] C.A. Ptak, E. Schragenheim, ERP Tools, Techniques, and Applications for Integrating the Supply Chain, St. Lucie Press, Florida, 2000.
[19] E. Thanassoulis, M.C.A.S. Portela, M. Graveney, Using DEA to estimate potential savings at GP units at medical specialty level, Socio-Economic Planning Sciences, 48(1) (2014) 38-48
[20] C.J. McDonald, New tools for yield improvement in integrated circuit manufacturing: can they be applied to reliability? Microelectron. Reliab. 39 (6–7) (1999) 731–739.
[21] R.M. Dabbas, H.N. Chen, Mining semiconductor manufacturing data for productivity improvement — an integrated relational database approach, Comput. Ind. 45 (1) (2001) 29–44.
[22] C.A. Bode, B.S. Ko, T.F. Edgar, Run-to-run control and performance monitoring of overlay in semiconductor manufacturing, Control Eng. Pract. 12 (2004) 893–900.
[23] Chiang Kao, Malmquist productivity index based on common-weights DEA: The case of Taiwan forests after reorganization, Omega, Volume 38, Issue 6, 2010, Pages 484-491.
[1] J. Johnes, Data envelopment analysis and its application to the measurement of efficiency in higher education, Econ. Educ. Rev. 5 (3) (2006) 273–288.
[2] R.D. Banker, A. Charnes, W.W. Cooper, Some models for estimating technical and scale inefficiencies in data envelopment analysis, Manag. Sci. 30 (9) (1984)
[3] M.L. Song, S.H. Wang, DEA decomposition of China’s environmental efficiency based on search algorithm, Applied Mathematics and Computation, 247( 2014) 562-572
[4] M. Jim´enez, Carlos R. G. Alonso, L.S. Carulla, V.F. Rodr´ıguez, Evaluation of system efficiency using the Monte Carlo DEA: the case of Small Health Areas, European Journal of Operational Research
[5] H. Leleua, J. Moisesc, V.G. Valdmanisd, How do payer mix and technical inefficiency affect hospital profit? A weighted DEA approach, Operations Research for Health Care, 3(4) 2014 231-237
[6] G.Vlontzosa, S.Niavisa, B.Manosb, A DEA approach for estimating the agricultural energy and environmental efficiency of EU countries, Renewable and Sustainable Energy Reviews, 40 (2014) 91-96
[7] P.J. Sher, P.Y. Yang, The effects of innovative capabilities and R&D clustering on firm performance, the evidence of Taiwan's semiconductor industry, Technovation 25 (2005) 33–43.
[8] W.T. Lin, H.J. Yang, M.Y. Lin, The implementation of enterprise resource planning and product data management in semiconductor related industries, an empirical study in Taiwan, Int. J. Manage. 23 (1) (2006) 117–131.
[9] Mingzhi Mao, E.C. Chirwa, Application of grey model GM(1, 1) to vehicle fatality risk estimation, Technological Forecasting and Social Change, Volume 73, Issue 5, 2006, Pages 588-605.
[10] Yu-Shan Chen, Bi-Yu Chen, Applying DEA, MPI, and grey model to explore the operation performance of the Taiwanese wafer fabrication industry, Technological Forecasting and Social Change, Volume 78, Issue 3, 2011, Pages 536-546.
[11] H.V. Trivedi, J.K. Singh, Application of Grey System Theory in the Development of a Runoff Prediction Model, Biosystems Engineering, Volume 92, Issue 4, 2005, Pages 521-526.
[12] Wenbin Liu, Zhongbao Zhou, Chaoqun Ma, Debin Liu, Wanfang Shen, Two-stage DEA models with undesirable input-intermediate-outputs, Omega, Volume 56, 2015, Pages 74-87.
[13] Stephane Mussard, Nicolas Peypoch, On multi-decomposition of the aggregate Malmquist productivity index, Economics Letters, Volume 91, Issue 3, 2006,Pages 436-443.
[14] Filipa Da Silva Fernandes, Charalampos Stasinakis, Valeriya Bardarova, Two-stage DEA-Truncated Regression: Application in banking efficiency and financial development, Expert Systems with Applications, Volume 96, 2018, Pages 284-301.
[15] Reza Kiani Mavi, Reza Farzipoor Saen, Mark Goh, Joint analysis of eco-efficiency and eco-innovation with common weights in two-stage network DEA: A big data approach, Technological Forecasting and Social Change, 2018.
[16] Naser Amani, Hadi Bagherzadeh Valami, Ali Ebrahimnejad Application of Malmquist productivity index with carry-overs in power industry, Alexandria Engineering Journal, 2018.
[17] Visakh Sakthidharan, Sunitha Sivaraman, Impact of operating cost components on airline efficiency in India: A DEA approach, Asia Pacific Management Review, 2018.
[18] C.A. Ptak, E. Schragenheim, ERP Tools, Techniques, and Applications for Integrating the Supply Chain, St. Lucie Press, Florida, 2000.
[19] E. Thanassoulis, M.C.A.S. Portela, M. Graveney, Using DEA to estimate potential savings at GP units at medical specialty level, Socio-Economic Planning Sciences, 48(1) (2014) 38-48
[20] C.J. McDonald, New tools for yield improvement in integrated circuit manufacturing: can they be applied to reliability? Microelectron. Reliab. 39 (6–7) (1999) 731–739.
[21] R.M. Dabbas, H.N. Chen, Mining semiconductor manufacturing data for productivity improvement — an integrated relational database approach, Comput. Ind. 45 (1) (2001) 29–44.
[22] C.A. Bode, B.S. Ko, T.F. Edgar, Run-to-run control and performance monitoring of overlay in semiconductor manufacturing, Control Eng. Pract. 12 (2004) 893–900.
[23] Chiang Kao, Malmquist productivity index based on common-weights DEA: The case of Taiwan forests after reorganization, Omega, Volume 38, Issue 6, 2010, Pages 484-491.
@article{"International Journal of Business, Human and Social Sciences:79117", author = "Mesbaholdin Salami and Farzad Movahedi Sobhani and Mohammad Sadegh Ghazizadeh", title = "Multistage Data Envelopment Analysis Model for Malmquist Productivity Index Using Grey's System Theory to Evaluate Performance of Electric Power Supply Chain in Iran", abstract = "Evaluation of organizational performance is among the most important measures that help organizations and entities continuously improve their efficiency. Organizations can use the existing data and results from the comparison of units under investigation to obtain an estimation of their performance. The Malmquist Productivity Index (MPI) is an important index in the evaluation of overall productivity, which considers technological developments and technical efficiency at the same time. This article proposed a model based on the multistage MPI, considering limited data (Grey’s theory). This model can evaluate the performance of units using limited and uncertain data in a multistage process. It was applied by the electricity market manager to Iran’s electric power supply chain (EPSC), which contains uncertain data, to evaluate the performance of its actors. Results from solving the model showed an improvement in the accuracy of future performance of the units under investigation, using the Grey’s system theory. This model can be used in all case studies, in which MPI is used and there are limited or uncertain data.
", keywords = "Malmquist Index, Grey's Theory, Charnes Cooper & Rhodes (CCR) Model, network data envelopment analysis, Iran electricity power chain. ", volume = "13", number = "9", pages = "1211-12", }
