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Technical, environmental and eco-efficiency measurement for supplier selection: An extension and application of data envelopment analysis

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  • Mahdiloo, Mahdi
  • Saen, Reza Farzipoor
  • Lee, Ki-Hoon

Abstract

With increased global awareness of environmental sustainability, green supply chain management (GSCM) has received considerable attention in the literature over the decade. Green supplier selection and assessment in GSCM is one of the most significant and complex challenges for supply chain managers. This paper provides a new model and approach for green supplier selections by decomposing their efficiency indicators into technical, environmental and eco-efficiency scores. We show that the models in the literature are computationally intensive and are not able to measure eco-efficiency properly. Instead of running three different models, linear goal programming is used to integrate technical, environmental and eco-efficiency objectives into a multiple objective linear programming (MOLP) data envelopment analysis (DEA) model. Therefore, the model proposed in this paper is less computationally intensive than the models in the literature. The new model provides a more valid eco-efficiency indicator of decision-making units (DMUs) by utilizing a better combination of the technical and environmental efficiency objectives compared to the conventional models. Unlike the conventional models, the new model identifies DMUs as being eco-efficient if, and only if, they are both technically and environmentally efficient. We also discuss the non-dominated weights as the solutions of the MOLP model and use them to construct technical, environmental and eco cross-efficiency matrices of the DMUs. In order to illustrate the effectiveness and applicability of the proposed model, we present the real world business case of the Hyundai Steel Company and its suppliers.

Suggested Citation

  • Mahdiloo, Mahdi & Saen, Reza Farzipoor & Lee, Ki-Hoon, 2015. "Technical, environmental and eco-efficiency measurement for supplier selection: An extension and application of data envelopment analysis," International Journal of Production Economics, Elsevier, vol. 168(C), pages 279-289.
  • Handle: RePEc:eee:proeco:v:168:y:2015:i:c:p:279-289
    DOI: 10.1016/j.ijpe.2015.07.010
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    8. Pyoungsoo Lee & You-Jin Park, 2017. "Eco-Efficiency Evaluation Considering Environmental Stringency," Sustainability, MDPI, Open Access Journal, vol. 9(4), pages 1-18, April.
    9. Mohammad Izadikhah & Reza Farzipoor Saen, 2020. "Ranking sustainable suppliers by context-dependent data envelopment analysis," Annals of Operations Research, Springer, vol. 293(2), pages 607-637, October.
    10. Magambo, Isaiah & Dikgang, Johane & Gelo, Dambala & Tregenna, Fiona, 2021. "Environmental and Technical Efficiency in Large Gold Mines in Developing Countries," MPRA Paper 108068, University Library of Munich, Germany.
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    14. Tuni, Andrea & Rentizelas, Athanasios, 2019. "An innovative eco-intensity based method for assessing extended supply chain environmental sustainability," International Journal of Production Economics, Elsevier, vol. 217(C), pages 126-142.
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    18. Mohammad Izadikhah & Reza Farzipoor Saen & Kourosh Ahmadi, 2017. "How to Assess Sustainability of Suppliers in the Presence of Dual-Role Factor and Volume Discounts? A Data Envelopment Analysis Approach," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(03), pages 1-25, June.
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    21. Hosseini, Seyedmohsen & Barker, Kash, 2016. "A Bayesian network model for resilience-based supplier selection," International Journal of Production Economics, Elsevier, vol. 180(C), pages 68-87.
    22. A. Mohammed, 2020. "Towards a sustainable assessment of suppliers: an integrated fuzzy TOPSIS-possibilistic multi-objective approach," Annals of Operations Research, Springer, vol. 293(2), pages 639-668, October.
    23. Mahdiloo, Mahdi & Toloo, Mehdi & Duong, Thach-Thao & Farzipoor Saen, Reza & Tatham, Peter, 2018. "Integrated data envelopment analysis: Linear vs. nonlinear model," European Journal of Operational Research, Elsevier, vol. 268(1), pages 255-267.
    24. Fukuyama, Hirofumi & Liu, Hui-hui & Song, Yao-yao & Yang, Guo-liang, 2021. "Measuring the capacity utilization of the 48 largest iron and steel enterprises in China," European Journal of Operational Research, Elsevier, vol. 288(2), pages 648-665.

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