Efficiency of 4 stage supply chain in presence of non discretionary , undesirable and negative factors Using SBM model in DEA

Subject Areas : Labor and Demographic Economics

mehdi shoga ¹ , farhad hoseinzadeh lotfi ² , امیر غلام ابری ³ , Alireza Rashidi Komijan ⁴

1 - PhD student in Industrial Engineering, Faculty of Engineering, Islamic Azad University, Roodehen Branch
2 - Professor, Department of Applied Mathematics, Faculty of Basic Sciences, Islamic Azad University, Science and Research Branch, Tehran, Iran,
3 - عضو هیات دعلمی واحد فیروزکوه
4 - Department of Industrial Engineering, Firozkooh Branch, Iran

Received: 2020-05-03 Accepted : 2020-11-04 Published : 2020-09-22

Keywords: Cement industry, Network data envelopment analysis, Undesirable outputs, Keywords: Supply chain efficiency, Non-Discretionary Factors,

Abstract :

The purpose of this paper is to evaluate the performance of a four-stage supply chain in the presence of uncontrollable, undesirable and negative data in the cement industry. For this purpose, the Slack-Based Measure (SBM) model in network data envelopment analysis is presented to evaluate the performance of such chains. Then, 42 cement companies present in the stock exchange and securities, the corresponding chain of each of which has four stages of supplier, producer, distributor and customer, were evaluated during the period 1394-1394. Based on the implementation of the model, 5 companies have been efficient for three consecutive years and the efficiency score of the rest of them has been less than 1 in all years or some years.

References:

-          Azadeh, A., & Alem, S.M. (2010).‏ A flexible deterministic, stochastic and fuzzy Data Envelopment Analysis approach for supply chain risk and vendor selection problem: Simulation analysis. Expert Systems with Applications, 37(12): 7438-7448.

-          Badiezadeh, T., Farzipoor Saen, R., and Samavati, T. (2018). Assessing sustainability of supply chains by double frontier network DEA: A big data approach. Computers & Operations Research, 98: 284-290.

-          Boudaghi, E., and Farzipoor Saen, R. (2018). Developing a novel model of data envelopment analysis–discriminant analysis for predicting group membership of suppliers in sustainable supply chain. Computers & Operations Research, 89: 348-359.

-          Chen, C., and Yan, H. (2011). Network DEA model for supply chain performance evaluation. European journal of operational research, 213(1): 147-155.

-          Cook, W.D., Zhu, J., Bi, G., and Yang, F. (2010). Network DEA: Additive efficiency decomposition. European journal of operational research, 207(2): 1122-1129.

-          Cook, W.D., Liang, L., and Zhu, J. (2010). Measuring performance of two-stage network structures by DEA: a review and future perspective. Omega, 38(6): 423-430.

-          Izadikhah, M., and Farzipoor Saen, R. (2016). Evaluating sustainability of supply chains by two-stage range directional measure in the presence of negative data. Transportation Research Part D: Transport and Environment, 49: 110-126.

-          Izadikhah, M., Farzipoor Saen, R., and Ahmadi, K. (2017). How to assess sustainability of suppliers in volume discount context? A new data envelopment analysis approach. Transportation Research Part D: Transport and Environment, 51: 102-121.

-          Kalantary, M., and Farzipoor Saen, R. (2018). Assessing sustainability of supply chains: An inverse network dynamic DEA model. Computers & Industrial Engineering, 135: 1224-1238.

-          Khodakarami, M., Shabani, A., Farzipoor Saen, R., and Azadi, M. (2015). Developing distinctive two-stage data envelopment analysis models: An application in evaluating the sustainability of supply chain management. Measurement, 70: 62-74.

-          Mirhedayatian, S.M., Azadi, M., and Farzipoor Saen, R. (2014). A novel network data envelopment analysis model for evaluating green supply chain management. International Journal of Production Economics, 147: 544-554.

-          Saranga, H., and Moser, R. (2010). Performance evaluation of purchasing and supply management using value chain DEA approach. European journal of operational research, 207(1): 197-205. ‏

-          Shafiee, M., Lotfi, F.H., and Saleh, H. (2014). Supply chain performance evaluation with data envelopment analysis and balanced scorecard approach. Applied Mathematical Modelling, 38(21-22): 5092-5112.

-          Tajbakhsh, A., and Hassini, E. (2015). A data envelopment analysis approach to evaluate sustainability in supply chain networks. Journal of Cleaner Production, 105: 74-85

-          Tavana, M., Mirzagoltabar, H., Mirhedayatian, S.M., Farzipoor Saen, R., and Azadi, M. (2013). A new network epsilon-based DEA model for supply chain performance evaluation. Computers & Industrial Engineering, 66(2): 501-513.

-          Tavana, M., Kaviani, M. A., Di Caprio, D., and Rahpeyma, B. (2016). A two-stage data envelopment analysis model for measuring performance in three-level supply chains. Measurement, 78: 322-333.

-          Tavassoli, M., and Farzipoor Saen, R. (2019). Predicting group membership of sustainable suppliers via data envelopment analysis and discriminant analysis. Sustainable Production and Consumption, 18: 41-52.

-          Xu, J., Li, B., and Wu, D. (2009). Rough data envelopment analysis and its application to supply chain performance evaluation. International Journal of Production Economics, 122(2): 628-638.

-          Liu, W., & Sharp, J. (1999). DEA models via goal programming. In Data envelopment analysis in the service sector. Deutscher Universitäts-verlag, 79-101.