IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i5p4433-d1085201.html
   My bibliography  Save this article

Benchmark Approach for Efficiency Improvement in Green Supply Chain Management with DEA Models

Author

Listed:
  • Farzad Zaare Tajabadi

    (Department of Industrial Engineering, Engineering Faculty, Eastern Mediterranean University, Famagusta 99628, North Cyprus Via Mersin 10, Turkey)

  • Sahand Daneshvar

    (Department of Industrial Engineering, Engineering Faculty, Eastern Mediterranean University, Famagusta 99628, North Cyprus Via Mersin 10, Turkey)

Abstract

Nowadays, concerns about environmental issues are increasing. Therefore, companies and producers are under pressure from government rules and regulations on one hand, and on the other hand, maintaining customer satisfaction concerning cares about the environment. Green supply chain management (GSCM) is a procedure to increase efficiency and decrease environmental effects for companies that collaborate with customers and suppliers. According to GSCM, there is some research about applying green aspects of purchasing, design, manufacture, distribution, packaging, marketing, and reverse logistics of supply chains to improve their company’s performance regarding environmental issues. Moreover, recently, DEA as a nonparametric model is used to evaluate the efficiency and performance of supply chains as decision-making units (DMUs). However, previous studies on efficiency improvement in GSCM did not investigate the effect of some economic and environmental factors together such as service level, emissions (CO 2 ), and size of the supply chains (arcs) on the efficiency of the whole supply system. These factors are essential as they can affect the manager’s ability to distinguish the true performance of a green supply chain. Thus, evaluating the efficiency of GSCM by DEA models and imposing the green principles to find out the efficient ones for increasing management performance is vital. Fulfilling the mentioned research gap, this paper developed a benchmark approach to verifying efficient DMUs and potential efficient DMUs which may improve costs and efforts to become efficient. In the case study, the benchmarks and potentially efficient DMUs are found by DEA standard models and slight adjustment is conducted for potentially efficient DMUs to change their status to efficient DMUs. Moreover, the effect of some green principles on the efficiency value of DMUs is verified using Tobit regression before and after the mentioned modification. A set of realistic results provided for the priority of potential DMUs modification confirmed the applicability of the proposed procedure.

Suggested Citation

  • Farzad Zaare Tajabadi & Sahand Daneshvar, 2023. "Benchmark Approach for Efficiency Improvement in Green Supply Chain Management with DEA Models," Sustainability, MDPI, vol. 15(5), pages 1-17, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:5:p:4433-:d:1085201
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/5/4433/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/5/4433/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Donthu, Naveen & Hershberger, Edmund K. & Osmonbekov, Talai, 2005. "Benchmarking marketing productivity using data envelopment analysis," Journal of Business Research, Elsevier, vol. 58(11), pages 1474-1482, November.
    2. Sabri, Ehap H. & Beamon, Benita M., 2000. "A multi-objective approach to simultaneous strategic and operational planning in supply chain design," Omega, Elsevier, vol. 28(5), pages 581-598, October.
    3. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    4. Felipe Fehlberg Herrmann & Ana Paula Barbosa-Povoa & Maria Angela Butturi & Simona Marinelli & Miguel Afonso Sellitto, 2021. "Green Supply Chain Management: Conceptual Framework and Models for Analysis," Sustainability, MDPI, vol. 13(15), pages 1-20, July.
    5. Sueyoshi, Toshiyuki & Goto, Mika, 2011. "Methodological comparison between two unified (operational and environmental) efficiency measurements for environmental assessment," European Journal of Operational Research, Elsevier, vol. 210(3), pages 684-693, May.
    6. Joseph Sarkis, 2006. "The adoption of environmental and risk management practices: Relationships to environmental performance," Annals of Operations Research, Springer, vol. 145(1), pages 367-381, July.
    7. Stewart, Theodor J., 2010. "Goal directed benchmarking for organizational efficiency," Omega, Elsevier, vol. 38(6), pages 534-539, December.
    8. Zhou, P. & Ang, B.W. & Poh, K.L., 2008. "Measuring environmental performance under different environmental DEA technologies," Energy Economics, Elsevier, vol. 30(1), pages 1-14, January.
    9. Sarkis, Joseph & Zhu, Qinghua & Lai, Kee-hung, 2011. "An organizational theoretic review of green supply chain management literature," International Journal of Production Economics, Elsevier, vol. 130(1), pages 1-15, March.
    10. Ezutah Udoncy Olugu & Kuan Yew Wong & Awaludin Mohamed Shaharoun, 2010. "A Comprehensive Approach in Assessing the Performance of an Automobile Closed-Loop Supply Chain," Sustainability, MDPI, vol. 2(4), pages 1-19, March.
    11. Zhu, Qinghua & Sarkis, Joseph & Lai, Kee-hung, 2008. "Confirmation of a measurement model for green supply chain management practices implementation," International Journal of Production Economics, Elsevier, vol. 111(2), pages 261-273, February.
    12. Zhou, P. & Ang, B.W. & Poh, K.L., 2008. "A survey of data envelopment analysis in energy and environmental studies," European Journal of Operational Research, Elsevier, vol. 189(1), pages 1-18, August.
    13. Ang, B.W. & Zhang, F.Q., 2000. "A survey of index decomposition analysis in energy and environmental studies," Energy, Elsevier, vol. 25(12), pages 1149-1176.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Denilson Ricardo de Lucena Nunes & Danyelle de Sousa Nascimento & Jennifer Rodrigues Matos & André Cristiano Silva Melo & Vitor William Batista Martins & Antônio Erlindo Braga, 2023. "Approaches to Performance Assessment in Reverse Supply Chains: A Systematic Literature Review," Logistics, MDPI, vol. 7(3), pages 1-15, June.
    2. Thi Kim Lien Nguyen & Thi Lan Huong Nguyen & Tri Long Ngo & Bang An Hoang & Hong Huyen Le & Thi Thanh Hong Tran, 2023. "An Integrated Approach of Fuzzy Analytic Hierarchy Process and Super Slack-Based Measure for the Logistics Industry in Vietnam," Sustainability, MDPI, vol. 15(16), pages 1-18, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jun-Der Leu & Wen-Hsien Tsai & Mei-Niang Fan & Sophia Chuang, 2020. "Benchmarking Sustainable Manufacturing: A DEA-Based Method and Application," Energies, MDPI, vol. 13(22), pages 1-21, November.
    2. Zhou, Haibo & Yang, Yi & Chen, Yao & Zhu, Joe, 2018. "Data envelopment analysis application in sustainability: The origins, development and future directions," European Journal of Operational Research, Elsevier, vol. 264(1), pages 1-16.
    3. Fang, Hong & Wu, Junjie & Zeng, Catherine, 2009. "Comparative study on efficiency performance of listed coal mining companies in China and the US," Energy Policy, Elsevier, vol. 37(12), pages 5140-5148, December.
    4. Wang, Ke & Lu, Bin & Wei, Yi-Ming, 2013. "China’s regional energy and environmental efficiency: A Range-Adjusted Measure based analysis," Applied Energy, Elsevier, vol. 112(C), pages 1403-1415.
    5. Zhou, P. & Ang, B.W., 2008. "Linear programming models for measuring economy-wide energy efficiency performance," Energy Policy, Elsevier, vol. 36(8), pages 2901-2906, August.
    6. Jeanneaux, Philippe & Latruffe, Laure, 2016. "Modelling pollution-generating technologies in performance benchmarking: Recent developments, limits and future prospects in the nonparametric frameworkAuthor-Name: Dakpo, K. Hervé," European Journal of Operational Research, Elsevier, vol. 250(2), pages 347-359.
    7. Zhou, P. & Ang, B.W. & Wang, H., 2012. "Energy and CO2 emission performance in electricity generation: A non-radial directional distance function approach," European Journal of Operational Research, Elsevier, vol. 221(3), pages 625-635.
    8. Ke Wang & Xueying Yu, 2017. "Industrial Energy and Environment Efficiency of Chinese Cities: An Analysis Based on Range-Adjusted Measure," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(04), pages 1023-1042, July.
    9. Wu, F. & Fan, L.W. & Zhou, P. & Zhou, D.Q., 2012. "Industrial energy efficiency with CO2 emissions in China: A nonparametric analysis," Energy Policy, Elsevier, vol. 49(C), pages 164-172.
    10. Alizadeh, Reza & Gharizadeh Beiragh, Ramin & Soltanisehat, Leili & Soltanzadeh, Elham & Lund, Peter D., 2020. "Performance evaluation of complex electricity generation systems: A dynamic network-based data envelopment analysis approach," Energy Economics, Elsevier, vol. 91(C).
    11. Fahimnia, Behnam & Sarkis, Joseph & Davarzani, Hoda, 2015. "Green supply chain management: A review and bibliometric analysis," International Journal of Production Economics, Elsevier, vol. 162(C), pages 101-114.
    12. Qingyou Yan & Xu Wang & Tomas Baležentis & Dalia Streimikiene, 2018. "Energy–economy–environmental (3E) performance of Chinese regions based on the data envelopment analysis model with mixed assumptions on disposability," Energy & Environment, , vol. 29(5), pages 664-684, August.
    13. Jie Wu & Xiang Lu & Dong Guo & Liang Liang, 2017. "Slacks-Based Efficiency Measurements with Undesirable Outputs in Data Envelopment Analysis," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(04), pages 1005-1021, July.
    14. Falavigna, G. & Ippoliti, R., 2020. "The socio-economic planning of a community nurses programme in mountain areas: A Directional Distance Function approach," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    15. Vlontzos, George & Niavis, Spyros & Manos, Basil, 2014. "A DEA approach for estimating the agricultural energy and environmental efficiency of EU countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 91-96.
    16. Blomberg, Jerry & Henriksson, Eva & Lundmark, Robert, 2012. "Energy efficiency and policy in Swedish pulp and paper mills: A data envelopment analysis approach," Energy Policy, Elsevier, vol. 42(C), pages 569-579.
    17. Dakpo, Hervé K & Jeanneaux, Philippe & Latruffe, Laure, 2014. "Inclusion of undesirable outputs in production technology modeling: The case of greenhouse gas emissions in French meat sheep farming," Working Papers 207806, Institut National de la recherche Agronomique (INRA), Departement Sciences Sociales, Agriculture et Alimentation, Espace et Environnement (SAE2).
    18. Chen, Chien-Ming, 2013. "A critique of non-parametric efficiency analysis in energy economics studies," Energy Economics, Elsevier, vol. 38(C), pages 146-152.
    19. Noor Ramli & Susila Munisamy & Behrouz Arabi, 2013. "Scale directional distance function and its application to the measurement of eco-efficiency in the manufacturing sector," Annals of Operations Research, Springer, vol. 211(1), pages 381-398, December.
    20. Kim, Kyunam & Kim, Yeonbae, 2012. "International comparison of industrial CO2 emission trends and the energy efficiency paradox utilizing production-based decomposition," Energy Economics, Elsevier, vol. 34(5), pages 1724-1741.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:5:p:4433-:d:1085201. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.