AGRICULTURAL SUPPLY CHAIN RISK ANALYSIS: A RANKING METHOD APPROACH

This paper proposes the Decision-Making and Trial Evaluation Laboratory (DEMATEL) method to assess the requirements of risk factors in the supply chain of agricultural products. It can be said that the supply chain of agricultural products is the most vulnerable to various risks. The risks may differ depending on the area (operational, economic, social, and environmental). Our objective in this paper is to determine the importance of each risk factor and their interrelationships to prioritize the most significant risks for further eliminate or mitigate them. To achieve this, we used the DEMATEL method on a specific dataset and compared our proposed method with fuzzyDEMATEL. The results underscore that the central risk factor requirements revolve around Enhanced customer service and Controlling carbon emissions and pollution. Furthermore, we categorized the risk factors into two groups: cause and effect. Consequently, we noted slight variations between the outcomes of the methods, indicating the effective identification of critical risk factors by both approaches.

1. Yang J. and Liu H. Research of vulnerability for fresh agricultural-food supply chain based on Bayesian network // Mathematical Problems in Engineering. – vol. 2018. – P. 1–17. https://doi.org/10.1155/2018/6874013.

2. Huang Y. Based on the supply chain of agricultural products logistics operational risk assessment and avoid: In 2015 Seventh International Conference on Measuring Technology and Mechatronics Automation, 2015. – P. 246–254. https://doi.org/10.1109/ICMTMA.2015.67.

3. Septiawan R., Komaruddin A., Sulistya B., Alfi N. and Shanmuganathan S. Prediction model for chilli productivity based on climate and productivity data: In 2012 Sixth UKSim/AMSS European Symposium on Computer Modeling and Simulation IEEE, 2012. – P. 54–59. https://doi.org/10.1109/EMS.2012.67.

4. Zhang H., Qiu B., and Zhang K. A new risk assessment model for agricultural products cold chain logistics // Industrial management & data systems. 2017. – Vol. 117. – No. 9. – P. 1800–1816. https://doi.org/10.1108/IMDS-03-2016-0098.

5. Salamai A., Hussain O.K., Saberi M., Chang E., and Hussain F.K. Highlighting the importance of considering the impacts of both external and internal risk factors on operational parameters to improve supply chain risk management // IEEE Access. – 2019. – Vol. 7. – P. 49297–49315. https://doi.org/10.1109/ACCESS.2019.2902191.

6. Yazdani M., Gonzalez E.D., and Chatterjee P. A multi-criteria decision-making framework for agriculture supply chain risk management under a circular economy context // Management Decision. – 2021. – Vol. 59. – No. 8. – P. 1801–1826. https://doi.org/10.1108/MD-10-2018-1088.

7. Ge H., Nolan J., Gray R., Goetz S. and Han Y. Supply chain complexity and risk mitigation – A hybrid optimization–simulation model // International Journal of Production Economics. – 2016. – Vol. 179. – P. 228–238. https://doi.org/10.1016/j.ijpe.2016.06.014.

8. Deng X., Yang X., Zhang Y., Li Y. and Lu Z. Risk propagation mechanisms and risk management strategies for a sustainable perishable products supply chain // Computers & Industrial Engineering. – 2019. – Vol. 135. – P. 1175–1187. https://doi.org/10.1016/j.cie.2019.01.014.

9. Benabdallah C., El-Amraoui A., Delmotte F. and Frikha A. An integrated rough-dematel method for sustainability risk assessment in agro-food supply chain. In 2020 5th International Conference on Logistics Operations Management (GOL). IEEE, 2020. – P. 1–9. https://doi.org/10.1109/GOL49479.2020.9314712.

10. Song W., Ming X. and Liu H.C. Identifying critical risk factors of sustainable supply chain management: A rough strength-relation analysis method // Journal of Cleaner Production. – 2017. – Vol. 143. – P. 100–115. https://doi.org/10.1016/j. jclepro.2016.12.145

11. Rostamzadeh R., Ghorabaee M. K., Govindan K., Esmaeili A. and Nobar H.B.K. Evaluation of sustainable supply chain risk management using an integrated fuzzy topsis-critic approach // Journal of Cleaner Production. – 2018. – Vol. 175. – P. 651–669. https://doi.org/10.1016/j.jclepro.2017.12.071.

12. Tomchek M. Sustainable technology impact on agricultural production // Decent Work and Economic Growth. – 2021. – P. 1024–1037.

13. De Oliveira U.R., Marins F.A.S., Rocha H.M. and Salomon V.A. P. The iso 31000 standard in supply chain risk management // Journal of Cleaner Production. – 2017. – Vol. 151. – P. 616–633. https://doi.org/10.1016/j.jclepro.2017.03.054.

14. Jaffee S., Siegel P. and Andrews C. Rapid agricultural supply chain risk assessment: A conceptual framework // Agriculture and rural development discussion paper. – 2010. – Vol. 47. – No. 1. – P. 1–64.

15. Behzadi G., O’Sullivan M.J., Olsen T.L. and Zhang A. Agribusiness supply chain risk management: A review of quantitative decision models // Omega. – 2018. – Vol. 79. – P. 21–42. https://doi.org/10.1016/j.omega.2017.07.005.

16. Si S.L., You X.Y., Liu H. C., and Zhang P. Dematel technique: A systematic review of the state-ofthe-art literature on methodologies and applications // Mathematical Problems in Engineering. – 2018. – Vol. 2018. – P. 1–33. https://doi.org/10.1155/2018/3696457.

17. Shieh J.I., Wu H.H. and Huang K.K. A DEMATEL method in identifying key success factors of hospital service quality // Knowledge-Based Systems. – 2010. – Vol. 23.3, P. 277–282. https://doi.org/10.1016/j.knosys.2010.01.013.

18. Yazdani M., Wang Z.X., Chan F. T. S. A decision support model based on the combined structure of DEMATEL, QFD and fuzzy values // Soft Computing. – 2020. – Vol. 24. – P. 12449–12468. https://doi.org/10.1007/s00500-020-04685-2.