Modeling the Future Efficiency of the Green Supply Chain of the Poultry Farming Industry Using Multistage DEA and Artificial Neural Networks
Subject Areas : تخصصی
Tahere Torkashvand
1
,
Fatemeh saghafi
2
,
Mohammad Hossein Darvish Motevalli
3
*
1 -
2 - Associate Prof. of University of Tehran
3 - Department of Industrial Management, West Tehran Branch, Islamic Azad University, Tehran, Iran.
Keywords: Artificial neural network, multi-stage data envelopment analysis, green supply chain of poultry farming industry,
Abstract :
Traditional DEA and multi-stage DEA models cannot predict future performance. In other words, all DEA and multi-stage DEA models evaluate units based on past performance. This paper seeks to move from previous monitoring models to a future planning approach that includes new contributions. In this study, artificial neural networks (ANN) are combined with multi-stage DEA to predict the future performance of the green supply chain of the poultry industry. For this purpose, first, after identifying the indicators affecting the green supply chain by combining the fuzzy Delphi method and the BWM method, 8 input indicators and 8 output indicators were identified, and after collecting the data of the studied chains using the artificial neural network, the data was predicted for 5 years. Then, the predicted data from the artificial neural network was used in multi-stage DEA. Multi-stage DEA evaluates the performance of the green poultry supply chain in the past, present, and future periods simultaneously. These results are useful for policy making and efficiency performance trend analysis.
(2015). "Theories in sustainable supply chain management: a structured literature review." International Journal of Physical Distribution & Logistics Management.
(2018). "Prediction of poultry yield using data mining techniques." Int J Innov Eng Sci Res: 16-32.
Akanmode, E., et al. (2018). "Prediction of poultry yield using data mining techniques." Int J Innov Eng Sci Res 2: 16-32.
Alinezhad, A. and A. Taherinezhad (2021). "Performance Evaluation of Production Chain using Two-Stage DEA Method (Case Study: Iranian Poultry Industry)." new economy and trad 16(3): 105-130.
Amini, A., et al. (2019). "Evaluation of green supply chain performance using network data envelopment analysis." International Journal of Green Economics 13(3-4): 187-201.
Amirbeygi, F., et al. (2022). "Evaluation of green supply chain performance using balanced scorecard and data envelopment analysis." Journal of Industrial Engineering and Management Studies 9(2): 64-85.
Anouze, A. L. M. and I. Bou-Hamad (2019). "Data envelopment analysis and data mining to efficiency estimation and evaluation." International Journal of Islamic and Middle Eastern Finance and Management 12(2): 169-190.
Chen, Y., Cook, W. D., Ning, L., & Zhu, J. (2009). "Additive efficiency decomposition in two-stage DEA." European journal of operational research,: 196(193), 1170-1176.
Krakovsky, R., Forgac, R., 2011. Neural network approach to multidimensional data classification via clustering. In: IEEE. 9th International Symposium on Intelli gent Systems and Informatics, pp. 169e174.
Depi (2013). "2013Structure of Victoria’s Chicken Meat Industry." Department of Environment and Primary Industries, Victoria, Australia. Accessed November.
Emrouznejad, A. and M. Tavana (2013). Performance measurement with fuzzy data envelopment analysis, Springer.
Ibiwoye, A., Ajibola, E., Sogunro, A.B., 2012. Artificial neural network model for predicting insurance insolvency. Int. J. Manag. Bus. Res. 2 (1), 59e68.
Färe, R., & Whittaker, G. (1995). "An intermediate input model of dairy production." Journal of Agricultural Economics,: 46, 201–213.
Geng, R., et al. (2017). "The relationship between green supply chain management and performance: A meta-analysis of empirical evidences in Asian emerging economies." International journal of production economics 183: 245-258.
Gupta, M. K. and P. Chandra (2022). "Effects of similarity/distance metrics on k-means algorithm with respect to its applications in iot and multimedia: a review." Multimedia Tools and Applications 81(26): 37007-37032.
Gwin, L., et al. (2013). Local meat and poultry processing: The importance of business commitments for long-term viability.
Melchiorre, C., Matteucci, M., Azzoni, A., Zanchi, A., 2008. Artificial neural networks and cluster analysis in landslide susceptibility zonation. Geomorpho 94 (3), 379e400.
Han, Y.M., Geng, Z.Q. and Zhu, Q.X. (2016), “Energy optimization and prediction of complex petrochemical industries using an improved artificial neural network approach integrating data envelopment analysis”, Energy Conversion and Management, Vol. 124, pp. 73-83.
Charnes, A., Cooper, W.W. and Rhodes, E. (1978), “Measuring the efficiency of decision making units”, European Journal of Operational Research, Vol. 2 No. 6, pp. 429-444.
Kwon, H.B., Lee, J. and Roh, J.J. (2016), “Best performance modeling using complementary DEA-ANN approach: application to Japanese electronics manufacturing firms”, Benchmarking: An International Journal, Vol. 23 No. 3, pp. 704-721.
Selim, S. and Bursalıoglu, S.A. (2015), “Efficiency of higher education in Turkey: a bootstrapped twostage DEA approach 1”, International Journal of Statistics and Applications, Vol. 5 No. 2,pp. 56-67.
Agasisti, T. and Ricca, L. (2016), “Comparing the efficiency of Italian public and private universities(2007-2011): an empirical analysis”, Italian Economic Journal, Vol. 2 No. 1, pp. 57-89.
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(181), 418-429.
Kao, C. (2014). "Efficiency decomposition for general multi-stage systems in data envelopment analysis." European Journal of Operational Research 232(1): 117-124.
Kao, C. (2014). "Network data envelopment analysis: A review." European Journal of Operational Research 239(1): 1-16.
Khashei, M. and M. Bijari (2010). "An artificial neural network (p, d, q) model for timeseries forecasting." Expert Systems with applications 37(1): 479-489.
Adler, N., Martini, G. and Volta, N. (2013), “Measuring the environmental efficiency of the global aviation fleet”, Transportation Research Part B: Methodological, Vol. 53, pp. 82-100.
Kumar, B. (2012). Theory of planned behaviour approach to understand the purchasing behaviour for environmentally sustainable products.
Kumar, S., & Putnam, V (2008). "Cradle to cradle: Reverse logistics strategies and opportunities across three industry sectors." International journal of production economics: 115(112), 305-315.
Liu, J.S., Lu, L.Y. and Lu, W.M. (2016), “Research fronts and prevailing applications in data envelopment analysis”, Data Envelopment Analysis, Springer, Boston, MA, pp. 543-574.
Alizadeh, A. and Omrani, H. (2019), “An integrated multi response Taguchi-neural network-robust data envelopment analysis model for CO2 laser cutting”, Measurement, Vol. 131, pp. 69-78.
Li, Y., Chen, Y., Liang, L., & Xie, J. (2012). "DEA models for extended two-stage network structures." Omega: 40(45), 611-618.
Ghasemi, N., Najafi, E., Lotfi, F.H. and Sobhani, F.M. (2020), “Assessing the performance of organizations with the hierarchical structure using data envelopment analysis: an efficiency analysis of Farhangian University”, Measurement, Vol. 156, 107609.
Misiunas, N., et al. (2016). "DEANN: A healthcare analytic methodology of data envelopment analysis and artificial neural networks for the prediction of organ recipient functional status." Omega 58: 46-54.
Manasakis, C., Apostolakis, A. and Datseris, G. (2013), “Using data envelopment analysis to measure hotel efficiency in Crete”, International Journal of Contemporary Hospitality Management, Vol. 25 No. 4, pp. 510-535.
Munongo, G. S. M. (2019). ENABLERS AND DE-ENABLERS OF GREEN SUPPLY CHAIN MANAGEMENT IN CAMEROON. Doctoral dissertation, PAN AFRICAN INSTITUTE FOR DEVELOPMENT-WEST AFRICA). (THE CASE OF SOSUCAM).
Nguyen, H.G., 2005. Using neutral work in predicting corporate failure. J. Soc. Sci. 1 (4), 199e202.
Nandy, A. and P. K. Singh (2020). "Farm efficiency estimation using a hybrid approach of machine-learning and data envelopment analysis: Evidence from rural eastern India." Journal of Cleaner Production 267: 122106.
Bhanot, N. and Singh, H. (2014), “Benchmarking the performance indicators of Indian Railway container business using data envelopment analysis”, Benchmarking: An International Journal, Vol. 21 No. 1, pp. 101-120."
Pratap, S., Jauhar, S.K., Paul, S.K. and Zhou, F. (2022), “Stochastic optimization approach for green routing and planning in perishable food production”, Journal of Cleaner Production, Vol. 333, 130063.
Carlucci, D., Renna, P. and Schiuma, G. (2013), “Evaluating service quality dimensions as antecedents to outpatient satisfaction using back propagation neural network”, Health Care Management Science, Vol. 16 No. 1, pp. 37-44.
Athanassopoulos, A.D. and Curram, S.P. (1996), “A comparison of data envelopment analysis and artificial neural networks as tools for assessing the efficiency of decision making units”, Journal of the Operational Research Society, Vol. 47 No. 8, pp. 1000-1016.
Rezaee, M. J., et al. (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.
Paradi, J.C. and Zhu, H. (2013), “A survey on bank branch efficiency and performance research with data envelopment analysis”, Omega, Vol. 41 No. 1, pp. 61-79.
Panapakidis, I.P. and Dagoumas, A.S. (2017), “Day-ahead natural gas demand forecasting based on the combination of wavelet transform and ANFIS/genetic algorithm/neural network model”, Energy, Vol. 118, pp. 231-245.
Shabanpour, H., et al. (2017). "Forecasting efficiency of green suppliers by dynamic data envelopment analysis and artificial neural networks." Journal of Cleaner Production 142: 1098-1107.
Shamsuddoha, M. (2014). "Integrated supply chain model for sustainable poultry production in Bangladesh: a system dynamics approach."
Tone, K., & Tsutsui, M. (2010). "Dynamic DEA: A slacks-based measure approach." Omega: 38(33-34), 145-156.
Kwon, H.B. (2017) Exploring the predictive potential of artificial neural networks in conjunction with DEA in railroad performance modeling. International Journal of Production Economics, 183(A), 159-170.
Zhang, G., et al. (1998). "Forecasting with artificial neural networks:: The state of the art." International journal of forecasting 14(1): 35-62.
Kuo, R.J., Wang, Y.C. and Tien, F.C. (2010), “Integration of artificial neural network and MADA methods for green supplier selection”, Journal of Cleaner Production, Vol. 18 No. 12, pp. 1161-1170.
Li, Y. and Liu, L. (2012), “Hybrid artificial neural network and statistical model for forecasting project total duration in earned value management”, International Journal of Networking and Virtual Organisations, Vol. 10 Nos 3-4, pp. 402-413.
Misiunas, N., Oztekin, A., Chen, Y. and Chandra, K. (2016), “DEANN: a healthcare analytic methodology of data envelopment analysis and artificial neural networks for the prediction of organ recipient functional status”, Omega, Vol. 58, pp. 46-54.
عرب حلوائی, ا., et al. (2023). "ارائه ی مدلی برای منبعیابی در زنجیرهی تأمین صنعت طیور." فصلنامه انجمن علوم مدیریت ایران 18(70): 58-89.
علیاکبر قره آغاجی, et al. (2022). "استفاده از الگوریتم شبکههای عصبی مصنوعی در پیش بینی خصوصیات کششی نخهای مغزی دار با مغزی نایلون و رویه پنبه." روشهای عددی در مهندسی 24(2): 241-251.
کشاورزی, و. ج. (1394). "شناخت، تجزیه و تحلیل صنعت طیور با هدف تعیین ساختاری کارا و اثربخش برای کسب و کار."
لطف الله زاده, س., et al. (1393). نقش مدیریت زنجیره تامین دریکپارچه سازی صنعت مرغداری مطالعه موردی شرکت پروتئین گسترسینا. اولین همایش ملی پژوهشهای مهندسی صنایع.
مستشاری, م. (1395). "اصول طراحی سالن مرغداری پرورش مرغ گوشتی متناسب با روش های نوین پرورش و کمترین هدررفت انرژی." تولید مرغ در ايران با سایر كشورها )شماره 19)." مجموعه نشریات تجارب دنیا در بخش کشاورزیو منابع طبیعی؛ مقایسه وضعیت تولید مرغ در ايران با سایر كشورها )شماره 19).
مصلحی, ح. (1399). "مقایسه وضعیت تولید مرغ در ایران با سایر کشور ها." مجموعه نشریات تجارب دنیا در بخش کشاورزیو منابع طبیعی.
نجفآبادی, م., et al. (2019). "ارزیابی توانایی مدلهای تحلیل پوششی دادههای فازی بازهای و استوار در تعیین کارایی واحدهای پرورش مرغ گوشتی استان خوزستان." Agricultural Economics 13(3): 29-56.
(1389). "مدلی احتمالی برای مدیریت زنجیره تامین چند دوره ای با در نظر گرفتن طول عمر تامین کنندگان،پایان نامه کارشناسی ارشد."
(1394). ارایه مدل ارزیابی عملکرد با استفاده از رویکرد تحلیل پوششی داده ها و منطق فازی- مورد مطالعه شرکت های سیمانی بورس, دولتی - وزارت علوم، تحقیقات، و فناوری - دانشگاه علامه طباطبایی - دانشکده مدیریت و حسابداری.