محاسبه کارایی زنجیره تامین پایدار در صنعت سیمان (کاربرد مدل تحلیل پوششی داده‌های شبکه‌ای)
محورهای موضوعی : اقتصاد کار و جمعیت
محمد حسین درویش متولی
1
,
فرهاد حسین زاده لطفی
2
,
نقی شجاع
3
,
امیر غلام ابری
4
1 - عضو هیئت علمی دانشگاه آزاد اسلامی واحد تهران غرب
2 - استاد، گروه ریاضی (تحقیق در عملیات)، دانشگاه آزاد اسلامی، واحد علوم و تحقیقات، تهران، ایران.
3 - دانشیار گروه ریاضی دانشگاه آزاد اسلامی واحد فیروزکوه
4 - دانشیار گروه ریاضی دانشگاه آزاد اسلامی واحد فیروزکوه
کلید واژه: طبقه‌بندی JEL: D24, C60. واژگان کلیدی: تحلیل پوششی داده‌های شبکه‌ای (NDEA), شاخص بهره‌وری مالمکوئیست (MPI), زنجیره تامین پایدار (SSCM),
چکیده مقاله :
هدف این مقاله ارائه مدل مناسب مبتنی بر تحلیل پوششی داده‌ها با ساختار شبکه‌ای برای ارزیابی کارایی زنجیره تامین پایدار شرکت‌های سیمان حاضر در بورس ایران طی دوره زمانی 1393-95 و تعیین میزان بهره وری آن ها بر اساس شاخص مالمکوئیست می باشد. این مدل می‌کوشد با در نظر گرفتن محدودیت‌های کمّی و کیفی و نیز خروجی های نامطلوب، معیارهای پایداری در شبکه تامین معرفی نماید و از طریق سنجه آنها بر زنجیره تامین، نتایج سازگارتری با واقعیت حاصل نماید. به طور کلی، نتایج حاکی از کاهش بهره‌وری کل شرکت‌های یاد شده است؛ در این میان، تنها 7 شرکت کارایی خود را حفظ کرده و سایر شرکت‌ها نوسان عملکرد داشته‌اند.
The purpose of this paper is to present an appropriate model of data envelopment analysis with a network structure for evaluating the sustainability of the supply chain of cement companies present in the Iranian Stock Exchange during 2014-2015 and determine their productivity based on the Malmquist index. This model attempts to introduce sustainability criteria in the supply network with regard to quantitative and qualitative constraints as well as undesirable outputs and Through their measure of supply chain, results are more consistent with reality. Overall, the results indicate a decline in the total productivity of these companies; meanwhile, only 7 companies have maintained their performance and other companies have had a performance fluctuation.
منابع
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- Genovese, A., & Adolf, A., & Alejandro, F. (2017). Sustainable supply chain management and the transition towards a circular economy: Evidence and some applications. In O mega journal: 344- 357.
- Govindan, K., & Kadziński, M., & Sivakumar, R. (2016). Application of a novel PROMETHEE-based method for construction of a group compromise ranking to prioritization of green suppliers in food supply chain. In Omega journal, 58: 132–142.
- Green, K., & Morton, B. & New, S. (1996). Purchasing and environmental management: Interactions, policies and opportunities. Journal of Business Strategy and the Environment, 5: 188-197
- Hatami-Marbini, A., & Ebrahimnejad, A., & Lozano, S. (2017). Fuzzy efficiency measures in data envelopment analysis using lexicographic multiobjective approach. Journal of Computers & Industrial Engineering, 6 (105): 362–376.
- Hosseinzadeh-Lotfi, f., & Jahanshahloo, G.R., & Mohammadpour, M. (2013). An Extension of Cross Redundancy of Interval Scale Outputs and Inputs in DEA., Journal of Applied Mathematics Volume 2013, Article ID 658635.
- Hsu, SH., & Kuo, T. (2013). Using DEMATEL to develop a carbon management model of supplier selection in green supply chain management. Journal of Cleaner Production, 56: 164-172.
- Jahan Shohloo. R. & Alirezaee. M.R. (2000). Measuring the efficience of academic units at the Teacher Training University, institute of mathematics, Teacher training university Tehran Iran.
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- Kannan, D., & Sousa Jabbour, AB. (2014). Selecting green suppliers based on GSCM practices: Using fuzzy TOPSIS applied to a Brazilian electronics company. European Journal of Operational Research, 233: 432–447.
- Kao, CH. (2013). Dynamic data envelopment analysis: A relational analysis. European Journal of Operational Research, 227: 325–330.
- Kiani, Fatemeh, Ansari, Rahimi. (2014). Economic, social and environmental impacts of Hegmatan cement plant on Shangjarin Village. Quarterly Journal of Rural Space and Rural Development, 4 (2): 144-133.
- Liang, L., & Cook, W.D., & Zhu, J, (2008). DEA models for two Stage processes: Game approach and efficiency decomposition, Naval Research Logistics, 55: 643–653.
- Liou, J.J.H. (2013). New concepts and trends of MCDM for tomorrow–in honor of Professor. Journal Technological and Economic Development of Economy Volume, 19: 331-347.
- Mariadoss, B., & Ting Chi, H., & Tansuhaj, P., & Pomirleanu, N. Polyakovskiy, S., Varasi, M. )2016(. Sustainable supply chain network design: A case of the wine industry in Australia. In Omega journal, 66: 236–247.
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- Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130: 498–509.
- Shabanpour, H., & Yousefi, S., & Farzipoor Saen, R. (2017(. Forecasting efficiency of green suppliers by dynamic data envelopment analysis and artificial neural networks. Journal of Cleaner Production, 142, 1098-1107.
- Seied Hosseini, S. M.(2014). Advanced engineering economics and decision analysis. Science and Technology University Press.
- Seiford, L.M., & J. Zhu. (1999). Profitability and marketability of the top 55 US commercial banks. Management Science, 45: 1270–1288.
- Tavana, M., & Shabanpour, H. (2016). A hybrid goal programming and dynamic data envelopment analysis framework for sustainable supplier evaluation. Neural Comput & Applic, journal of Neural Comput & Applic, 28: 3683–3696.
- Tseg, G. H., & Chiang, C. H., & Li, C. W. (2007). Evaluating intertwined effects in e-learning programs: A novel hybrid MCDM model based on factor analysis and DEMATEL. Expert System with Applications, 32:1028-1044.
- Toke, L. K., & Gupta, R. C., & Dandekar M. (2012). An empirical study of green supply chain management in Indian perspective. International Journal of Applied Science and Engineering Research, 1(2): 372–83.
- Vafaie, F., & A. Babaie (2010). Designing the group fuzzy multiple criteria decision-making model for ranking the stocks on the Tehran stock exchange. Journal of Industrial Management, 14 (1): 89- 102.
- Wolf, C., & Seuring, S. (2010). Environmental impacts as buying criteria for third party logistical services. International Journal of Physical Distribution & Logistics Management, 40(1/2): 84–10.
- Yalcin, N., & Ali, B., & K. Cengiz (2012). Application of fuzzy multicriteria decision making methods for financial performance evaluation of Turkish manufacturing industries. Expert Systems with Applications, 39(1): 350-364.
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- Anderson, P., & Peterson, N.C. (1993). A procedure for ranking efficient units in data envelopment analysis. Management Science, 39, (10): 1261-1264.
- Azadi, M. & Jafarian, M. (2015). A new fuzzy DEA model for evaluation of efficiency and effectiveness of suppliers in sustainable supply chain management context. Computers & Operations Research, 54: 274 -285.
- Badiezadeh, T., & Farzipoor Saen, R., & Samavati, T. (2017). Assessing sustainability of supply chains by double frontier network DEA: A big data approach. Computers & Operations Research, 61(2): 1-7.
- Banker, R. D., Charnes, A. & Cooper, W.W. (1984). Models for the estimation of technical and scale efficiencies in data envelopment analysis, Management Science,
- Brandenburg, m. (2014). Quantitative models for sustainable supply chain management: Developments and directions. European Journal of Operational Research, 233(2): 299-312.
- Boudaghi, E., FarzipoorSaen, R. (2018). Developing a novel model of data envelopment analysis–discriminant analysis for predicting group membership of suppliers in sustainable supply chain. Computers and Operations Research 89,348–359.
- Charnes, A., & Cooper, W.W., & Rohdes, E. (1978). Measuring the Efficiency of Decision Making Units. Europen Journal of operational Research, 2: 249-444.
- Cook Wade D., & Zhu, Joe. Bi, Gongbing. Yang, Feng. Network DEA: Additive efficiency decomposition. European Journal of Operational Research, 207: 1122–1129.
- De Camargo Fiorini, P., & Charbel José, j. (2017). Information systems and sustainable supply chain management towards a more sustainable society. International Journal of Information Management, 37 (4): 241-249.
- Fare, R., & Grosskopf, S. (2000). Network DEA, Socio-Economic Planning Sciences, 34: 35-49.
- Farell, M.J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society, 120: 253-281.
- Gandhi, S., & Kumar Mangla, S., & Kumar, P. (2015). Evaluating factors in implementation of successful green supply chain management using DEMATEL: A case study. International Strategic Management Review, 3: 96–109.
- Genovese, A., & Adolf, A., & Alejandro, F. (2017). Sustainable supply chain management and the transition towards a circular economy: Evidence and some applications. In O mega journal: 344- 357.
- Govindan, K., & Kadziński, M., & Sivakumar, R. (2016). Application of a novel PROMETHEE-based method for construction of a group compromise ranking to prioritization of green suppliers in food supply chain. In Omega journal, 58: 132–142.
- Green, K., & Morton, B. & New, S. (1996). Purchasing and environmental management: Interactions, policies and opportunities. Journal of Business Strategy and the Environment, 5: 188-197
- Hatami-Marbini, A., & Ebrahimnejad, A., & Lozano, S. (2017). Fuzzy efficiency measures in data envelopment analysis using lexicographic multiobjective approach. Journal of Computers & Industrial Engineering, 6 (105): 362–376.
- Hosseinzadeh-Lotfi, f., & Jahanshahloo, G.R., & Mohammadpour, M. (2013). An Extension of Cross Redundancy of Interval Scale Outputs and Inputs in DEA., Journal of Applied Mathematics Volume 2013, Article ID 658635.
- Hsu, SH., & Kuo, T. (2013). Using DEMATEL to develop a carbon management model of supplier selection in green supply chain management. Journal of Cleaner Production, 56: 164-172.
- Jahan Shohloo. R. & Alirezaee. M.R. (2000). Measuring the efficience of academic units at the Teacher Training University, institute of mathematics, Teacher training university Tehran Iran.
- Jahani, A., & Soofi, F., & Mennati, R., & H. Rahimi Nezhad. (2013). Identifying the ranking of the companies listed on the Tehran stock exchange using studied variables and analytical hierarchy process, the national conference of accounting and management, september 5th, safashahr kharazmi international institute for educational research.
- Kannan, D., & Sousa Jabbour, AB. (2014). Selecting green suppliers based on GSCM practices: Using fuzzy TOPSIS applied to a Brazilian electronics company. European Journal of Operational Research, 233: 432–447.
- Kao, CH. (2013). Dynamic data envelopment analysis: A relational analysis. European Journal of Operational Research, 227: 325–330.
- Kiani, Fatemeh, Ansari, Rahimi. (2014). Economic, social and environmental impacts of Hegmatan cement plant on Shangjarin Village. Quarterly Journal of Rural Space and Rural Development, 4 (2): 144-133.
- Liang, L., & Cook, W.D., & Zhu, J, (2008). DEA models for two Stage processes: Game approach and efficiency decomposition, Naval Research Logistics, 55: 643–653.
- Liou, J.J.H. (2013). New concepts and trends of MCDM for tomorrow–in honor of Professor. Journal Technological and Economic Development of Economy Volume, 19: 331-347.
- Mariadoss, B., & Ting Chi, H., & Tansuhaj, P., & Pomirleanu, N. Polyakovskiy, S., Varasi, M. )2016(. Sustainable supply chain network design: A case of the wine industry in Australia. In Omega journal, 66: 236–247.
- Mathiyazhagan, K., & Govindan, K., & Noorul Haq, A. (2014(. Pressure analysis for green supply chain management implementation in Indian industries using analytic hierarchy process. International Journal of Production Research, 52: 1-16.
- Mollenkopf, D., & Stolze, H., & Tate, W.L., & Ueltschy, M. (2010) Green, lean, and global supply chains. International Journal of Physical Distribution and Logistics Management, 40(1/2): 14-41.
- Olfat, L., & Amiri, M., & Bamdad Soufi, J., & Pishdar, M. (2016). A dynamic network efficiency measurement of airports performance considering sustainable development concept: A fuzzy dynamic network-DEA approach. Journal of Air - Transport Management, 57: 272-290.
- Polyakovskiy, S., Varasi, M. (2017). Sustainable supply chain network design: A case of the wine industry in Australia. OmegaVol 66: 236–247.
- Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130: 498–509.
- Shabanpour, H., & Yousefi, S., & Farzipoor Saen, R. (2017(. Forecasting efficiency of green suppliers by dynamic data envelopment analysis and artificial neural networks. Journal of Cleaner Production, 142, 1098-1107.
- Seied Hosseini, S. M.(2014). Advanced engineering economics and decision analysis. Science and Technology University Press.
- Seiford, L.M., & J. Zhu. (1999). Profitability and marketability of the top 55 US commercial banks. Management Science, 45: 1270–1288.
- Tavana, M., & Shabanpour, H. (2016). A hybrid goal programming and dynamic data envelopment analysis framework for sustainable supplier evaluation. Neural Comput & Applic, journal of Neural Comput & Applic, 28: 3683–3696.
- Tseg, G. H., & Chiang, C. H., & Li, C. W. (2007). Evaluating intertwined effects in e-learning programs: A novel hybrid MCDM model based on factor analysis and DEMATEL. Expert System with Applications, 32:1028-1044.
- Toke, L. K., & Gupta, R. C., & Dandekar M. (2012). An empirical study of green supply chain management in Indian perspective. International Journal of Applied Science and Engineering Research, 1(2): 372–83.
- Vafaie, F., & A. Babaie (2010). Designing the group fuzzy multiple criteria decision-making model for ranking the stocks on the Tehran stock exchange. Journal of Industrial Management, 14 (1): 89- 102.
- Wolf, C., & Seuring, S. (2010). Environmental impacts as buying criteria for third party logistical services. International Journal of Physical Distribution & Logistics Management, 40(1/2): 84–10.
- Yalcin, N., & Ali, B., & K. Cengiz (2012). Application of fuzzy multicriteria decision making methods for financial performance evaluation of Turkish manufacturing industries. Expert Systems with Applications, 39(1): 350-364.