Presenting a productivity analysis model for Iran oil industries using Malmquist network analysis
Keywords:Performance evaluation, network data envelopment analysis, DEA, Malmquist productivity index
Organizational performance evaluation is a crucial factor in making strategic decisions for the future. To plan for economic growth, it is important to measure the efficiency and productivity of organizations. Efficiency is a key indicator for evaluating the optimal performance of economic units. Petrochemical companies are vital components of a country's economy and their operations contribute to the growth and progress of different sectors. In countries where the economy relies heavily on this industry, such as ours, petroleum is of utmost importance. Data Envelopment Analysis is a widely used method for measuring productivity. This study aims to analyze the performance evaluation relates to the supply chain of petrochemical companies using the network DEA and Malmquist index. Efficiency and performance indices are calculated for each stage of the process. The study determines the indices through literature review, expert consultation, analysis, and visits to petrochemical companies. The input- and output-oriented multiplier models are used to assess overall and stage efficiencies. Using the efficiency values, the Malmquist productivity index is determined. The study examines unit productivity for the years 1395 to 1398, and the results indicate that most of the units experienced productivity growth during this period.
Arbabi, M., Moghaddas, Z., Amirteimoori, A., & Khunsiavash, M. (2022). An innovative inverse model of network data envelopment analysis. Journal of Applied Research on Industrial Engineering. https://doi.org/10.22105/JARIE.2022.326607.1445
Bagherzadeh Valami, H., & Raeinojehdehi, R. (2016). Ranking units in Data Envelopment Analysis with fuzzy data. Journal of Intelligent and Fuzzy Systems, 30(5), 2505–2516. https://doi.org/10.3233/IFS-151756
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
Bosetti, V., Cassinelli, M., & Lanza, A. (2005). Using data envelopment analysis to evaluate environmentally conscious tourism management. The Economics of Tourism and Sustainable Development, 252–268. https://doi.org/10.4337/9781845426781.00013
Carboni, O. A., & Russu, P. (2015). Assessing Regional Wellbeing in Italy: An Application of Malmquist–DEA and Self-organizing Map Neural Clustering. Social Indicators Research, 122(3), 677–700. https://doi.org/10.1007/s11205-014-0722-7
Caves, D. W., Christensen, L. R., & Diewert, W. E. (1982). The Economic Theory of Index Numbers and the Measurement of Input, Output, and Productivity. Econometrica, 50(6), 1393. https://doi.org/10.2307/1913388
Charnes, A., Cooper, W. W., & Rhodes, E. L. (1978). Measuring the efficiency of farms. European Journal of Operational Research, 2, 429–444.
Charnes, A., Cooper, W. W., Wei, Q. L., & Huang, Z. M. (1989). Cone ratio data envelopment analysis and multi-objective programming. International Journal of Systems Science, 20(7), 1099–1118. https://doi.org/10.1080/00207728908910197
Chaubey, V., Sharanappa, D. S., Mohanta, K. K., Mishra, V. N., & Mishra, L. N. (2022). Efficiency and Productivity Analysis of the Indian Agriculture Sector Based on the Malmquist-DEA Technique. Universal Journal of Agricultural Research, 10(4), 331–343. https://doi.org/10.13189/ujar.2022.100402
Kao, C., & Hwang, S.-N. (2008). Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan. European Journal of Operational Research, 185(1), 418–429.
Coelli, T. (1996). A Guide to DEAP Version 2.1: A Data Envelopment Analysisi (Computer) Program. CEPA Working Papers, 8(96), 1–49.
Ebrahimnejad, A., Tavana, M., Lotfi, F. H., Shahverdi, R., & Yousefpour, M. (2014). A three-stage Data Envelopment Analysis model with application to banking industry. Measurement: Journal of the International Measurement Confederation, 49(1), 308–319. https://doi.org/10.1016/j.measurement.2013.11.043
Färe, R. (1991). Measuring Farrell efficiency for a firm with intermediate inputs. Academia Economic Papers, 19(19), 329–340.
Färe, R., & Groskopf, S. (2000). Network DEA. Socio-Economic Planning Sciences, 34, 35–49.
Färe, R., & Grosskopf, S. (2000). Network DEA. Socio-Economic Planning Sciences, 34(1), 35–49. https://doi.org/10.1016/S0038-0121(99)00012-9
Färe, R., & Whittaker, G. (1995). an Intermediate Input Model of Dairy Production Using Complex Survey Data. Journal of Agricultural Economics, 46(2), 201–213. https://doi.org/10.1111/j.1477-9552.1995.tb00766.x
Fukuyama, H., & Weber, W. L. (2010). A slacks-based inefficiency measure for a two-stage system with bad outputs. Omega, 38(5), 398–409. https://doi.org/10.1016/j.omega.2009.10.006
Golany, B., & Roll, Y. (1989). An application procedure for DEA. Omega, 17(3), 237–250. https://doi.org/10.1016/0305-0483(89)90029-7
Holod, D., & Lewis, H. F. (2011). Resolving the deposit dilemma: A new DEA bank efficiency model. Journal of Banking and Finance, 35(11), 2801–2810. https://doi.org/10.1016/j.jbankfin.2011.03.007
Hsieh, L. F., & Lin, L. H. (2010). A performance evaluation model for international tourist hotels in Taiwan-An application of the relational network DEA. International Journal of Hospitality Management, 29(1), 14–24. https://doi.org/10.1016/j.ijhm.2009.04.004
Huang, J., Chen, J., & Yin, Z. (2014). A network DEA model with super efficiency and undesirable outputs: An application to bank efficiency in China. Mathematical Problems in Engineering, 2014, 1-14. https://doi.org/10.1155/2014/793192
Kao, C., & Liu, S. T. (2014). Multi-period efficiency measurement in data envelopment analysis: The case of Taiwanese commercial banks. Omega (United Kingdom), 47, 90–98. https://doi.org/10.1016/j.omega.2013.09.001
Krishnasamy, G., Ridzwa, A. H., & Perumal, V. (2004). Malaysian post merger banks’ productivity: Application of malmquist productivity index. Managerial Finance, 30(4), 63–74. https://doi.org/10.1108/03074350410769038
Kumar, A., Shankar, R., & Debnath, R. M. (2015). Analyzing customer preference and measuring relative efficiency in telecom sector: A hybrid fuzzy AHP/DEA study. Telematics and Informatics, 32(3), 447–462. https://doi.org/10.1016/j.tele.2014.10.003
Kuo, R. J., & Lin, Y. J. (2012). Supplier selection using analytic network process and data envelopment analysis. International Journal of Production Research, 50(11), 2852–2863. https://doi.org/10.1080/00207543.2011.559487
Lee, B. L., & Worthington, A. C. (2016). A network DEA quantity and quality-orientated production model: An application to Australian university research services. Omega (United Kingdom), 60, 26–33. https://doi.org/10.1016/j.omega.2015.05.014
Li, Y., Chen, Y., Liang, L., & Xie, J. (2014a). DEA models for extended two-stage network structures. International Series in Operations Research and Management Science, 208(5), 261–284. https://doi.org/10.1007/978-1-4899-8068-7_12
Li, Y., Chen, Y., Liang, L., & Xie, J. (2014b). DEA models for extended two-stage network structures. International Series in Operations Research and Management Science, 208(50), 261–284. https://doi.org/10.1007/978-1-4899-8068-7_12
Mahmoudi, H., Bazrafshan, M., & Ahmadipour, M. (2021). Sustainable multi-objective optimization for the supply chain of petroleum products. Journal of Applied Research on Industrial Engineering, 8, 1–15. https://doi.org/10.22105/JARIE.2022.318695.1407
Malmquist, S. (1953). Index numbers and indifference surfaces. Trabajos de Estadistica, 4(2), 209–242. https://doi.org/10.1007/BF03006863
Muniz, R. D. F., Andriola, W. B., Muniz, S. M., & Thomaz, A. C. F. (2022). The use of data envelopment analysis to estimate the educational efficiency of Brazilian schools. Journal of Applied Research on Industrial Engineering, 9(4), 374–383. https://doi.org/10.22105/JARIE.2021.308815.1388
Murias, P., Martinez, F., & de Miguel, C. (2006). An economic wellbeing index for the Spanish provinces: A Data Envelopment Analysis approach. Social Indicators Research, 77(3), 395–417. https://doi.org/10.1007/s11205-005-2613-4
Shahbeyk, S., & Banihashemi, S. (2023). Loan Portfolio Performance Evaluation by Using Stochastic Recovery Rate. Journal of Applied Research on Industrial Engineering. https://doi.org/10.22105/JARIE.2023.346023.1478
Shermeh, H. E., Najafi, S. E., & Alavidoost, M. H. (2016). A novel fuzzy network SBM model for data envelopment analysis: A case study in Iran regional power companies. Energy, 112, 686–697. https://doi.org/10.1016/j.energy.2016.06.087
Zhou, J., Peng, R., Chang, Y., Liu, Z., Gao, S., Zhao, C., Li, Y., Feng, Q., & Qin, X. (2023). Analyzing the efficiency of Chinese primary healthcare institutions using the Malmquist-DEA approach: Evidence from urban and rural areas. Frontiers in Public Health, 11. https://doi.org/10.3389/fpubh.2023.1073552
How to Cite
Copyright (c) 2023 Decision Making: Applications in Management and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.