IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i3p2578-d1053112.html
   My bibliography  Save this article

Application of Fuzzy TRUST CRADIS Method for Selection of Sustainable Suppliers in Agribusiness

Author

Listed:
  • Adis Puška

    (Department of Public Safety, Government of Brčko District of Bosnia and Herzegovina, Bulevara Mira 1, 76100 Brčko, Bosnia and Herzegovina)

  • Miroslav Nedeljković

    (Institute of Agricultural Economics, Volgina 15, 11060 Belgrade, Serbia)

  • Ilija Stojanović

    (College of Business Administration, American University in the Emirates, Dubai International Academic City, Dubai P.O. Box 503000, United Arab Emirates)

  • Darko Božanić

    (Military Academy, University of Defence in Belgrade, Veljka Lukica Kurjaka 33, 11000 Belgrade, Serbia)

Abstract

This study deals with the selection of a sustainable supplier on the example of the agribusiness company Mamex from Bosnia and Herzegovina. The main problem of this research is the selection of a sustainable supplier as a part of the sustainable strategy of the Mamex company. One of the prerequisites is that suppliers must present sustainability principles in business by having an appropriate certificate. The results of the selection of sustainable suppliers are completed using a new hybrid fuzzy approach with the methods IMF SWARA (Improved Fuzzy Stepwise Weight Assessment Ratio Analysis) and fuzzy TRUST (multi-normalization multi-distance assessment) CRADIS (compromise ranking of alternatives from distance to ideal solution) methods. The innovative approach is reflected in the use of a combination of these methods, especially by combining the TRUST and CRADIS methods into one method. The IMF SWARA method shows that the most important main criterion is the economic criterion, while the least important is the social criterion. By applying the fuzzy TRUST CRADIS method, it is found that out of the observed six suppliers, the second supplier has the best indicators. These results are confirmed by other fuzzy methods: MABAC (multi-attributive border approximation area comparison), WASPAS (weighted aggregated sum product assessment), fuzzy SAW (simple additive weighting), MARCOS (measurement of alternatives and ranking according to compromise solution), ARAS (a new additive ratio assessment), and TOPSIS (technique for order preference by similarity to an ideal solution). This research shows that applying more normalization when ranking alternatives reduces the influence of individual normalizations, and this approach should be used in future research.

Suggested Citation

  • Adis Puška & Miroslav Nedeljković & Ilija Stojanović & Darko Božanić, 2023. "Application of Fuzzy TRUST CRADIS Method for Selection of Sustainable Suppliers in Agribusiness," Sustainability, MDPI, vol. 15(3), pages 1-19, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2578-:d:1053112
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/3/2578/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/3/2578/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Alptekin Ulutaş & Ayşe Topal & Dragan Pamučar & Željko Stević & Darjan Karabašević & Gabrijela Popović, 2022. "A New Integrated Multi-Criteria Decision-Making Model for Sustainable Supplier Selection Based on a Novel Grey WISP and Grey BWM Methods," Sustainability, MDPI, vol. 14(24), pages 1-20, December.
    2. Adis Puška & Željko Stević & Dragan Pamučar, 2022. "Evaluation and selection of healthcare waste incinerators using extended sustainability criteria and multi-criteria analysis methods," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(9), pages 11195-11225, September.
    3. Xiongyong Zhou & Zhiduan Xu, 2018. "An Integrated Sustainable Supplier Selection Approach Based on Hybrid Information Aggregation," Sustainability, MDPI, vol. 10(7), pages 1-49, July.
    4. Edward Elson Kosasih & Alexandra Brintrup, 2022. "A machine learning approach for predicting hidden links in supply chain with graph neural networks," International Journal of Production Research, Taylor & Francis Journals, vol. 60(17), pages 5380-5393, September.
    5. Adis Puška & Miroslav Nedeljković & Radivoj Prodanović & Radovan Vladisavljević & Radmila Suzić, 2022. "Market Assessment of Pear Varieties in Serbia Using Fuzzy CRADIS and CRITIC Methods," Agriculture, MDPI, vol. 12(2), pages 1-15, January.
    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. Nikita Osintsev & Aleksandr Rakhmangulov, 2025. "Supply Chain Sustainability Drivers: Identification and Multi-Criteria Assessment," Logistics, MDPI, vol. 9(1), pages 1-45, February.
    2. Xuemei Chen & Bin Zhou & Anđelka Štilić & Željko Stević & Adis Puška, 2023. "A Fuzzy–Rough MCDM Approach for Selecting Green Suppliers in the Furniture Manufacturing Industry: A Case Study of Eco-Friendly Material Production," Sustainability, MDPI, vol. 15(13), pages 1-21, July.

    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. Franciely Velozo Aragão & Daiane Maria de Genaro Chiroli & Fernanda Cavicchioli Zola & Emanuely Velozo Aragão & Luis Henrique Nogueira Marinho & Ana Lidia Cascales Correa & João Carlos Colmenero, 2023. "Smart Cities Maturity Model—A Multicriteria Approach," Sustainability, MDPI, vol. 15(8), pages 1-20, April.
    2. Bacilieri, Andrea & Astudillo-Estévez, Pablo, 2023. "Reconstructing firm-level input-output networks from partial information," INET Oxford Working Papers 2023-05, Institute for New Economic Thinking at the Oxford Martin School, University of Oxford, revised Jan 2025.
    3. Kazim Topuz & Akhilesh Bajaj & Kristof Coussement & Timothy L. Urban, 2025. "Interpretable machine learning and explainable artificial intelligence," Annals of Operations Research, Springer, vol. 347(2), pages 775-782, April.
    4. Jun Hu & Chengbin Chu & Regino Criado & Junhua Chen & Shuya Hao & Maoze Wang, 2025. "Visibility graph and graph convolution networks-based segmentation of carbon emission in China," Annals of Operations Research, Springer, vol. 348(1), pages 609-630, May.
    5. Duan, Xiaoyang & Zhao, Peixin & Li, Zhuyue & Han, Xue, 2024. "Quantifying the reciprocal impacts of capital and logistics networks in the supply chains: A cyber–physical system approach," Chaos, Solitons & Fractals, Elsevier, vol. 188(C).
    6. Mališa Žižović & Dragan Pamučar & Miloljub Albijanić & Prasenjit Chatterjee & Ivan Pribićević, 2020. "Eliminating Rank Reversal Problem Using a New Multi-Attribute Model—The RAFSI Method," Mathematics, MDPI, vol. 8(6), pages 1-16, June.
    7. R. Krishankumar & P. P. Amritha & K. S. Ravichandran, 2022. "RETRACTED ARTICLE: An integrated fuzzy decision model for prioritization of barriers affecting sustainability adoption within supply chains under unknown weight context," Operations Management Research, Springer, vol. 15(3), pages 1010-1027, December.
    8. Aytekin, Ahmet & Korucuk, Selçuk & Görçün, Ömer Faruk, 2024. "Determining the factors affecting transportation demand management and selecting the best strategy: A case study," Transport Policy, Elsevier, vol. 146(C), pages 150-166.
    9. Giuseppe Timpanaro, 2023. "Agricultural Food Marketing, Economics and Policies," Agriculture, MDPI, vol. 13(4), pages 1-9, March.
    10. Maciej Urbaniak & Blanka Tundys & Magdalena Ankiel, 2021. "Expectations of Production Companies Operating in Poland towards Suppliers with Regards to Implementation of the Sustainability Concept," Sustainability, MDPI, vol. 13(16), pages 1-15, August.
    11. Patchara Phochanikorn & Chunqiao Tan, 2019. "A New Extension to a Multi-Criteria Decision-Making Model for Sustainable Supplier Selection under an Intuitionistic Fuzzy Environment," Sustainability, MDPI, vol. 11(19), pages 1-24, September.
    12. Tarkan Tan & M. Hakan Akyüz & Bengisu Urlu & Santiago Ruiz, 2024. "Stop Auditing and Start to CARE: Paradigm Shift in Assessing and Improving Supplier Sustainability," Interfaces, INFORMS, vol. 54(3), pages 241-263, May.
    13. De Bock, Koen W. & Coussement, Kristof & Caigny, Arno De & Słowiński, Roman & Baesens, Bart & Boute, Robert N. & Choi, Tsan-Ming & Delen, Dursun & Kraus, Mathias & Lessmann, Stefan & Maldonado, Sebast, 2024. "Explainable AI for Operational Research: A defining framework, methods, applications, and a research agenda," European Journal of Operational Research, Elsevier, vol. 317(2), pages 249-272.
    14. Koen W. de Bock & Kristof Coussement & Arno De Caigny & Roman Slowiński & Bart Baesens & Robert N Boute & Tsan-Ming Choi & Dursun Delen & Mathias Kraus & Stefan Lessmann & Sebastián Maldonado & David , 2023. "Explainable AI for Operational Research: A Defining Framework, Methods, Applications, and a Research Agenda," Post-Print hal-04219546, HAL.
    15. Radojko LUKIC, 2024. "Comparative Analysis of Trade Performance between the European Union and Serbia using AHP-DNMA Method," Management and Economics Review, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 9(3), pages 453-469, October.
    16. Peigui Liu & Shuoya Cheng & Manting Shang & Yang Gao & Song Wei, 2023. "Effect of Weight of Water Resources Carrying Capacity Evaluation Index on Its Evaluation Results in Xinjiang, China," Sustainability, MDPI, vol. 15(3), pages 1-14, February.
    17. Ivanov, Dmitry, 2023. "Intelligent digital twin (iDT) for supply chain stress-testing, resilience, and viability," International Journal of Production Economics, Elsevier, vol. 263(C).
    18. Deveci, Muhammet & Gokasar, Ilgin & Chen, Yu & Wang, Weizhong & Karaismailoğlu, Ali Eren & Antucheviciene, Jurgita, 2025. "Analysis of green energy in sustainable transportation in developing nations through a decision support model," Renewable Energy, Elsevier, vol. 244(C).
    19. Haolun Wang, 2022. "Sustainable Circular Supplier Selection in the Power Battery Industry Using a Linguistic T-Spherical Fuzzy MAGDM Model Based on the Improved ARAS Method," Sustainability, MDPI, vol. 14(13), pages 1-26, June.
    20. Roya Ghamari & Mohammad Mahdavi-Mazdeh & Seyed Farid Ghannadpour, 2022. "Resilient and sustainable supplier selection via a new framework: a case study from the steel industry," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(8), pages 10403-10441, August.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:3:p:2578-:d:1053112. 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.