A New Double Frontiers Data Envelopment Analysis Approach for Assessing the Sustainability of Suppliers
Abstract
One of the techniques for evaluating a supplier's sustainability is Data Envelopment Analysis (DEA). DEA is a non-parametric tool for measuring the relative efficiency of Decision-Making Units (DMUs). This paper develops a new double frontier DEA model based on the Slacks-Based Measure (SBM) for assessing suppliers' sustainability. Our proposed double frontier SBM model considers pessimistic and optimistic efficiencies. A case study is presented to demonstrate the applicability of the proposed model. The results show that the proposed model can completely rank the DMUs, and there is no tie between the overall efficiency scores.
Keywords:
Double frontier, Slacks-based measure, Sustainable supplier, Data envelopment analysis, Pessimistic efficiency, Optimistic efficiencyReferences
- [1] Srivastava, S. K. (2007). Green supply-chain management: A state-of-the-art literature review. International journal of management reviews, 9(1), 53–80. https://doi.org/10.1111/j.1468-2370.2007.00202.x
- [2] Carter, C. R., & Rogers, D. S. (2008). A framework of sustainable supply chain management: moving toward new theory. International journal of physical distribution & logistics management, 38(5), 360–387. https://doi.org/10.1108/09600030810882816
- [3] Daly, H. E., Cobb, J. B. (1994). For the common good: redirecting the economy toward community, the environment, and a sustainable future. Beacon Press. https://doi.org/10.1177/027046769101100137
- [4] Linton, J. D., Klassen, R., & Jayaraman, V. (2007). Sustainable supply chains: An introduction. Journal of operations management, 25(6), 1075–1082. https://doi.org/10.1016/j.jom.2007.01.012
- [5] Lu, L. Y. Y., Wu, C. H., & Kuo, T. C. (2007). Environmental principles applicable to green supplier evaluation by using multi-objective decision analysis. International journal of production research, 45(18–19), 4317–4331. https://doi.org/10.1080/00207540701472694
- [6] Wang, Y. M., & Lan, Y. X. (2011). Measuring malmquist productivity index: A new approach based on double frontiers data envelopment analysis. Mathematical and computer modelling, 54(11–12), 2760–2771. https://doi.org/10.1016/j.mcm.2011.06.064
- [7] Wang, Y. M., Chin, K. S., & Yang, J. B. (2007). Measuring the performances of decision-making units using geometric average efficiency. Journal of the operational research society, 58(7), 929–937. https://doi.org/10.1057/palgrave.jors.2602205
- [8] Wang, Y. M., & Chin, K. S. (2009). A new approach for the selection of advanced manufacturing technologies: DEA with double frontiers. International journal of production research, 47(23), 6663–6679. https://doi.org/10.1080/00207540802314845
- [9] Ho, W., Xu, X., & Dey, P. K. (2010). Multi-criteria decision making approaches for supplier evaluation and selection: A literature review. European journal of operational research, 202(1), 16–24. https://doi.org/10.1016/j.ejor.2009.05.009
- [10] Ghodsypour, S. H., & O’brien, C. (2001). The total cost of logistics in supplier selection, under conditions of multiple sourcing, multiple criteria and capacity constraint. International journal of production economics, 73(1), 15–27. https://doi.org/10.1016/S0925-5273(01)00093-7
- [11] Talluri, S., & Baker, R. (2002). A multi-phase mathematical programming approach for effective supply chain design. European journal of operational research, 141(3), 544–558. https://doi.org/10.1016/S0377-2217(01)00277-6
- [12] Kumar, M., Vrat, P., & Shankar, R. (2004). A fuzzy goal programming approach for vendor selection problem in a supply chain. Computers & industrial engineering, 46(1), 69–85. https://doi.org/10.1016/j.cie.2003.09.010
- [13] Saen, R. F. (2007). Suppliers selection in the presence of both cardinal and ordinal data. European journal of operational research, 183(2), 741–747. https://doi.org/10.1016/j.ejor.2006.10.022
- [14] Önüt, S., Gülsün, B., Tuzkaya, U. R., Tuzkaya, G., & others. (2008). A two-phase possibilistic linear programming methodology for multi-objective supplier evaluation and order allocation problems. Information sciences, 178(2), 485–500. https://doi.org/10.1016/j.ins.2007.08.002
- [15] Wu, D. D., Zhang, Y., Wu, D., & Olson, D. L. (2010). Fuzzy multi-objective programming for supplier selection and risk modeling: A possibility approach. European journal of operational research, 200(3), 774–787. https://doi.org/10.1016/j.ejor.2009.01.026
- [16] Saen, R. F. (2010). A new model for selecting third-party reverse logistics providers in the presence of multiple dual-role factors. The international journal of advanced manufacturing technology, 46, 405–410. https://doi.org/10.1007/s00170-009-2092-x
- [17] Saen, R. F. (2010). Restricting weights in supplier selection decisions in the presence of dual-role factors. Applied mathematical modelling, 34(10), 2820–2830. https://doi.org/10.1016/j.apm.2009.12.016
- [18] Noorizadeh, A., Mahdiloo, M., & Saen, R. F. (2011). Supplier selection in the presence of dual-role factors, non-discretionary inputs and weight restrictions. International journal of productivity and quality management, 8(2), 134–152. https://doi.org/10.1504/IJPQM.2011.041843
- [19] Zoroufchi, K. H., Azadi, M., & Saen, R. F. (2012). Developing a new cross-efficiency model with undesirable outputs for supplier selection. International journal of industrial and systems engineering, 12(4), 470–484. https://doi.org/10.1504/IJISE.2012.050124
- [20] Azadi, M., Saen, R. F., & Tavana, M. (2012). Supplier selection using chance-constrained data envelopment analysis with non-discretionary factors and stochastic data. International journal of industrial and systems engineering, 10(2), 167–196. https://doi.org/10.1504/IJISE.2012.045179
- [21] Junior, F. R. L., Osiro, L., & Carpinetti, L. C. R. (2014). A comparison between Fuzzy AHP and Fuzzy TOPSIS methods to supplier selection. Applied soft computing, 21, 194–209. DOI: 10.1016/j.asoc.2014.03.014
- [22] Karsak, E. E., & Dursun, M. (2014). An integrated supplier selection methodology incorporating QFD and DEA with imprecise data. Expert systems with applications, 41(16), 6995–7004. https://doi.org/10.1016/j.eswa.2014.06.020
- [23] Tavassoli, M., Faramarzi, G. R., & Farzipoor Saen, R. (2014). A joint measurement of efficiency and effectiveness for the best supplier selection using integrated data envelopment analysis approach. International journal of mathematics in operational research, 6(1), 70–83. https://doi.org/10.1504/IJMOR.2014.057861
- [24] Bai, C., & Sarkis, J. (2010). Integrating sustainability into supplier selection with grey system and rough set methodologies. International journal of production economics, 124(1), 252–264. https://doi.org/10.1016/j.ijpe.2009.11.023
- [25] Amindoust, A., Ahmed, S., Saghafinia, A., & Bahreininejad, A. (2012). Sustainable supplier selection: A ranking model based on fuzzy inference system. Applied soft computing, 12(6), 1668–1677. https://doi.org/10.1016/j.asoc.2012.01.023
- [26] Wen, L., Xu, L., & Wang, R. (2013). Sustainable supplier evaluation based on intuitionistic fuzzy sets group decision methods. Journal of information &computational science, 10(10), 3209–3220. DOI: 10.12733/jics20102169
- [27] Azadi, M., Jafarian, M., Saen, R. F., & Mirhedayatian, S. 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. https://doi.org/10.1016/j.cor.2014.03.002
- [28] Sarkis, J., & Dhavale, D. G. (2015). Supplier selection for sustainable operations: A triple-bottom-line approach using a Bayesian framework. International journal of production economics, 166, 177–191. DOI: 10.1016/j.ijpe.2014.11.007
- [29] Trapp, A. C., & Sarkis, J. (2016). Identifying Robust portfolios of suppliers: A sustainability selection and development perspective. Journal of cleaner production, 112, 2088–2100. DOI: 10.1016/j.jclepro.2014.09.062
- [30] Ahmady, N., Azadi, M., Sadeghi, S. A. H., & Saen, R. F. (2013). A novel fuzzy data envelopment analysis model with double frontiers for supplier selection. International journal of logistics research and applications, 16(2), 87–98. https://doi.org/10.1080/13675567.2013.772957
- [31] Wang, Y. M., & Lan, Y. X. (2013). Estimating most productive scale size with double frontiers data envelopment analysis. Economic modelling, 33, 182–186. https://doi.org/10.1016/j.econmod.2013.04.021
- [32] Azizi, H., Kordrostami, S., & Amirteimoori, A. (2015). Slacks-based measures of efficiency in imprecise data envelopment analysis: An approach based on data envelopment analysis with double frontiers. Computers & industrial engineering, 79, 42–51. https://doi.org/10.1016/j.cie.2014.10.019
- [33] Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European journal of operational research, 130(3), 498–509. https://doi.org/10.1016/S0377-2217(99)00407-5
- [34] Khodakarami, M., Shabani, A., & Farzipoor Saen, R. (2014). A new look at measuring sustainability of industrial parks: a two-stage data envelopment analysis approach. Clean technologies and environmental policy, 16, 1577–1596. https://doi.org/10.1007/s10098-014-0733-8%0A%0A
- [35] Ghodsypour, S. H., & Ghomi, S. M. T. F. (2010). Supply chain optimization policy for a supplier selection problem : a mathematical programming approach, 2(1), 17–31. (In Persian). https://www.sid.ir/en/vewssid/j_pdf/115720100102.pdf
- [36] Hashemi, S. H., Karimi, A., & Tavana, M. (2015). An integrated green supplier selection approach with analytic network process and improved Grey relational analysis. International journal of production economics, 159, 178–191. https://doi.org/10.1016/j.ijpe.2014.09.027