Evaluation on risks of sustainable supply chain based on integrated rough DEMATEL in Tunisian dairy industry

Document Type : GOL20


1 Laboratory of Computer Engineering and Automation, University of Artois, Béthune, France

2 University of Sfax, Higher Institute of Industrial Management of Sfax, Sfax, Tunisia



Recently, sustainable supply chain management (SSCM) has grown considerably at agro-food supply chain (AFSC). Due to their complex nature, these supply chains are exposed to a variety of interrelated risks from natural disasters and man-made. Hence, one of the fundamental concerns in the AFSC is identifying and prioritizing risks to achieve sustainability. However, in analyzing sustainability concerns, most previous studies have paid less attention to interrelationship between sustainability and risk assessment. The objective of this work is to propose a methodology to supply chain sustainability risk assessment by evaluating environmental, economic, social and operational risks. The proposed approach is an integrated rough decision- making and trial evaluation laboratory (DEMATEL) method for solving this problem, which takes into account the interrelationship between different risks and the group preference variety. The proposed methodology integrates the strength of DEMATEL approach in exploring both internal strength and external influence of risks as well as the advantage of rough number in manipulating the vagueness of information. A real-world case study of a Tunisian dairy company is presented to test the applicability of the proposed framework. It can be observed from results that the most important risks are “Large number of intermediaries”, “Lack of proper storage facilities” and “Transport disruption”. The results and findings can help the dairy sector decision-makers in a variety of ways to successfully identify and prioritize supply chain risks in order to attain sustainability.


Abdel-Basset, M., Gunasekaran, M., Mohamed, M., and Chilamkurti, N. (2019). A framework for risk assessment, management and evaluation: Economic tool for quantifying risks in supply chain. Future Generation Computer Systems, Vol. 90, pp. 489-502.
Abdel-Basset, M., and Mohamed, R. (2020). A novel plithogenic TOPSIS-CRITIC model for sustainable supply chain risk management. Journal of Cleaner Production, Vol. 247, pp. 119586.
Ali, S. M., Moktadir, M. A., Kabir, G., Chakma, J., Rumi, M. J. U., and Islam, M. T. (2019). Framework for evaluating risks in food supply chain: Implications in food wastage reduction. Journal of Cleaner Production, Vol. 228, pp. 786-800
Allaoui, H., Guo, Y., Choudhary, A., and Bloemhof, J. (2018). Sustainable agro-food supply chain design using two-stage hybrid multi-objective decision-making approach. Computers & Operations Research, Vol. 89, pp. 369-384.
Baykasoğlu, A., and Gölcük, İ. (2017). Development of an interval type-2 fuzzy sets based hierarchical MADM model by combining DEMATEL and TOPSIS. Expert Systems with Applications, Vol. 70, pp. 37-51.
Behzadi, G., O’Sullivan, M. J., Olsen, T. L., and Zhang, A. (2018). Agribusiness supply chain risk management: A review of quantitative decision models. Omega, Vol. 79, pp. 21-42.
Chand, M., Raj, T., Shankar, R., and Agarwal, A. (2017). Select the best supply chain by risk analysis for Indian industries environment using MCDM approaches. Benchmarking: An International Journal. Vol. 24(5), pp. 1400-1413.
Chen, Z., Ming, X., Zhang, X., Yin, D., and Sun, Z. (2019). A rough-fuzzy DEMATEL-ANP method for evaluating sustainable value requirement of product service system. Journal of Cleaner Production, Vol. 228, pp. 485-508.
Chen, Z., Lu, M., Ming, X., Zhang, X., and Zhou, T. (2020). Explore and evaluate innovative value propositions for smart product service system: A novel graphics-based rough-fuzzy DEMATEL method. Journal of Cleaner Production, Vol. 243, pp. 118672.
Chen, Y., Ran, Y., Huang, G., Xiao, L., and Zhang, G. (2021). A new integrated MCDM approach for improving QFD based on DEMATEL and extended MULTIMOORA under uncertainty environment. Applied Soft Computing, Vol. 105, pp. 107222.
Cunha, L., Ceryno, P., and Leiras, A. (2019). Social supply chain risk management: A taxonomy, a framework and a research agenda. Journal of Cleaner Production, Vol. 220, pp. 1101-1110.
Da Silva, E. M., Ramos, M. O., Alexander, A., and Jabbour, C. J. C. (2020). A systematic review of empirical and normative decision analysis of sustainability-related supplier risk management. Journal of Cleaner Production, Vol. 244, pp. 118808.
De Oliveira, U. R., Marins, F. A. S., Rocha, H. M., and Salomon, V. A. P. (2017). The ISO 31000 standard in supply chain risk management. Journal of Cleaner Production, Vol. 151, pp. 616-633.
Deng, X., Yang, X., Zhang, Y., Li, Y., and Lu, Z. (2019). Risk propagation mechanisms and risk management strategies for a sustainable perishable products supply chain. Computers & Industrial Engineering, Vol. 135, pp. 1175-1187.
Diabat, A., Govindan, K., and Panicker, V. V. (2012). Supply chain risk management and its mitigation in a food industry. International Journal of Production Research, Vol. 50(11), pp. 3039-3050.
Du, Y. W., and Li, X. X. (2021). Hierarchical DEMATEL method for complex systems. Expert Systems with Applications, Vol. 167, pp. 13871.
El Baz, J., and Ruel, S. (2021). Can supply chain risk management practices mitigate the disruption impacts on supply chains’ resilience and robustness? Evidence from an empirical survey in a COVID-19 outbreak era. International Journal of Production Economics, Vol. 233, pp. 107972.
Fontela, E., and Gabus, A. (1976). Current perceptions of the world problematique. World Modeling: A Dialogue. North-Holland Publishing Company, Amsterdam/Oxford.
Gardas, B. B., Raut, R. D., and Narkhede, B. (2018). Evaluating critical causal factors for post-harvest losses (PHL) in the fruit and vegetables supply chain in India using the DEMATEL approach. Journal of cleaner production, Vol. 199, pp.  47-61
Ge, H., Nolan, J., Gray, R., Goetz, S., and Han, Y. (2016). Supply chain complexity and risk mitigation–A hybrid optimization–simulation model. International Journal of Production Economics, Vol. 179, pp. 228-238.
Giannakis, M., and Papadopoulos, T. (2016). Supply chain sustainability: A risk management approach. International Journal of Production Economics, Vol. 171, pp. 455-470.
Huang, K., Wu, K. F., and Ardiansyah, M. N. (2019). A stochastic dairy transportation problem considering collection and delivery phases. Transportation Research Part E: Logistics and Transportation Review, Vol. 129, pp. 325-338.
Ivanov, D. (2020). Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transportation Research Part E: Logistics and Transportation Review, Vol. 136, pp. 101922.
Jouzdani, J., and Govindan, K. (2021). On the sustainable perishable food supply chain network design: A dairy products case to achieve sustainable development goals. Journal of Cleaner Production, Vol. 278, pp. 123060.
Kara, M. E., Fırat, S. Ü. O., and Ghadge, A. (2020). A data mining-based framework for supply chain risk management. Computers & Industrial Engineering, Vol. 139, pp. 105570.
Kazancoglu, Y., Ozkan-Ozen, Y. D., and Ozbiltekin, M. (2018). Minimizing losses in milk supply chain with sustainability: An example from an emerging economy. Resources, Conservation and Recycling, Vol. 139, pp. 270-279.
Khalilzadeh, M., Shakeri, H., and Zohrehvandi, S. (2021). Risk identification and assessment with the fuzzy DEMATEL-ANP method in oil and gas projects under uncertainty. Procedia Computer Science, Vol. 181, pp. 277-284.
Khan, S. A. R., Yu, Z., Golpîra, H., Sharif, A., and Mardani, A. (2020). A state-of-the-art review and meta-analysis on sustainable supply chain management: Future research directions. Journal of Cleaner Production, Vol. 272, pp.123357.
Mangla, S. K., Kumar, P., and Barua, M. K. (2015). Flexible decision modeling for evaluating the risks in green supply chain using fuzzy AHP and IRP methodologies. Global Journal of Flexible Systems Management, Vol. 16(1), pp. 19-35.
Mangla, S. K., Luthra, S., Rich, N., Kumar, D., Rana, N. P., and Dwivedi, Y. K. (2018). Enablers to implement sustainable initiatives in agri-food supply chains. International Journal of Production Economics, Vol. 203, pp.379-393.
Mao, W., Wang, W., Luo, D., and Sun, H. (2019). Analyzing interactions between risk factors for ice disaster in Ning-Meng reach of Yellow River based on grey rough DEMATEL method. Natural Hazards, Vol. 97(3), pp. 1025-1049.
Mohammadfam, I., Aliabadi, M. M., Soltanian, A. R., Tabibzadeh, M., and Mahdinia, M. (2019). Investigating interactions among vital variables affecting situation awareness based on Fuzzy DEMATEL method. International Journal of Industrial Ergonomics, Vol. 74, pp. 102842.
Moktadir, M. A., Dwivedi, A., Khan, N. S., Paul, S. K., Khan, S. A., Ahmed, S., and Sultana, R. (2021). Analysis of risk factors in sustainable supply chain management in an emerging economy of leather industry. Journal of Cleaner Production, Vol. 283, pp. 124641.
Msaddak, M., BenNasr, J., Zaibet, L., and Fridhi, M. (2017). Social networks for the sustainability of the dairy sector: the role of cooperatives. Livestock Research for Rural Development, Vol. 29(2), pp. 22-26.
Nejad, M. C., Mansour, S., and Karamipour, A. (2021). An AHP-based multi-criteria model for assessment of the social sustainability of technology management process: A case study in banking industry. Technology in Society, Vol. 65, pp. 101602.
Li, X., Han, Z., Zhang, R., Zhang, Y., and Zhang, L. (2020). Risk assessment of hydrogen generation unit considering dependencies using integrated DEMATEL and TOPSIS approach. International Journal of Hydrogen Energy, Vol. 45(53), pp. 29630-29642.
Li, J., Xu, K., Ge, J., and Fan, B. (2021). Development of a quantitative risk assessment method for a biomass gasification unit by combining DEMATEL-ISM and CM-TOPSIS. Stochastic Environmental Research and Risk Assessment, pp. 1-17.
Liu, Z., and Ming, X. (2019). A framework with revised rough-DEMATEL to capture and evaluate requirements for smart industrial product-service system of systems. International Journal of Production Research, Vol. 57(22), pp. 7104-7122.
Ortiz‐Barrios, M., Miranda‐De la Hoz, C., López‐Meza, P., Petrillo, A., and De Felice, F. (2020). A case of food supply chain management with AHP, DEMATEL, and TOPSIS. Journal of Multi‐Criteria Decision Analysis, Vol. 27(1-2), pp. 104-128.
Ozturkoglu, Y., Kazancoglu, Y., and Ozkan-Ozen, Y. D. (2019). A sustainable and preventative risk management model for ship recycling industry. Journal of Cleaner Production, Vol. 238, pp. 117907.
Pamučar, D., Mihajlović, M., Obradović, R., and Atanasković, P. (2017). Novel approach to group multi-criteria decision making based on interval rough numbers: Hybrid DEMATEL-ANP-MAIRCA model. Expert Systems with Applications, Vol. 88, pp. 58-80
Paksoy, T., Çalik, A., Yildizbaşi, A., and Huber, S. (2019). Risk management in lean & green supply chain: A novel fuzzy linguistic risk assessment approach. In Lean and Green Supply Chain Management (pp. 75-100). Springer
Pawlak, Z. (1982). Rough sets. International journal of computer & information sciences, Vol. 11(5), pp. 341-356.
Pelissari, R., Oliveira, M. C., Abackerli, A. J., Ben‐Amor, S., and Assumpção, M. R. P. (2021). Techniques to model uncertain input data of multi‐criteria decision‐making problems: a literature review. International Transactions in Operational Research, Vol. 28(2), pp. 523-559
Rostamzadeh, R., Ghorabaee, M. K., Govindan, K., Esmaeili, A., and Nobar, H. B. K. (2018). Evaluation of sustainable supply chain risk management using an integrated fuzzy TOPSIS-CRITIC approach. Journal of Cleaner Production, Vol. 175, pp. 651-669.
Roy, J., Chatterjee, K., Bandyopadhyay, A., and Kar, S. (2018a). Evaluation and selection of medical tourism sites: A rough analytic hierarchy process based multi‐attributive border approximation area comparison approach. Expert Systems, Vol. 35(1), pp. 121-142.
Roy, J., Adhikary, K., Kar, S., and Pamucar, D. (2018b). A rough strength relational DEMATEL model for analysing the key success factors of hospital service quality. Decision Making: Applications in Management and Engineering, Vol. 1(1), pp. 121-142.
Samvedi, A., and Jain, V. (2013). A study on the interactions between supply chain risk management criteria using fuzzy DEMATEL method. International Journal of Operational Research, Vol. 18(3), pp. 255-271.
Sellitto, M. A., Vial, L. A. M., and Viegas, C. V. (2018). Critical success factors in Short Food Supply Chains: Case studies with milk and dairy producers from Italy and Brazil. Journal of Cleaner Production, Vol. 170, pp. 1361-1368.
Sharma, Y. K., Mangla, S. K., and Patil, P. P. (2021). Risks in sustainable food supply chain management. In Research Anthology on Food Waste Reduction and Alternative Diets for Food and Nutrition Security (pp. 265-280). IGI Global.
Soethoudt, H., Blom-Zandstra, G., and Axmann, H. (2018). Dairy value chain analysis in Tunisia: Business opportunities (No. WFBR-1829). Wageningen Food and Biobased Research, No. WFBR-1829.
Souissi, A., Mtimet, N., Thabet, C., Stambouli, T., and Chebil, A. (2019). Impact of food consumption on water footprint and food security in Tunisia. Food Security, Vol. 11(5), pp. 989-1008.
Song, W., Ming, X., and Liu, H. C. (2017). Identifying critical risk factors of sustainable supply chain management: A rough strength-relation analysis method. Journal of Cleaner Production, Vol. 143, pp. 100-115
Song, W., Zhu, Y., and Zhao, Q. (2020). Analyzing barriers for adopting sustainable online consumption: A rough hierarchical DEMATEL method. Computers & Industrial Engineering, Vol. 140, pp. 106279.
Tadić, S., Zečević, S., and Krstić, M. (2014). A novel hybrid MCDM model based on fuzzy DEMATEL, fuzzy ANP and fuzzy VIKOR for city logistics concept selection. Expert Systems with Applications, Vol. 41(18), pp.8112-8128.
Trivedi, A., Jakhar, S. K., and Sinha, D. (2021). Analyzing barriers to inland waterways as a sustainable transportation mode in India: A dematel-ISM based approach. Journal of Cleaner Production, Vol. 295, pp.126301.
Tse, Y. K., Zhang, M., Tan, K. H., Pawar, K., and Fernandes, K. (2019). Managing quality risk in supply chain to drive firm's performance: The roles of control mechanisms. Journal of Business Research, Vol. 97, pp. 291-303.
Valinejad, F., and Rahmani, D. (2018). Sustainability risk management in the supply chain of telecommunication companies: A case study. Journal of Cleaner Production, Vol. 203, pp. 53-67.
Yazdani, M., Gonzalez, E. D., and Chatterjee, P. (2019). A multi-criteria decision-making framework for agriculture supply chain risk management under a circular economy context. Management Decision. Vol. 143, pp. 106394.
Yazdani, M., Tavana, M., Pamučar, D., and Chatterjee, P. (2020). A rough based multi-criteria evaluation method for healthcare waste disposal location decisions. Computers & Industrial Engineering, Vol.143, pp. 106394
Wu, Y., Jia, W., Li, L., Song, Z., Xu, C., and Liu, F. (2019). Risk assessment of electric vehicle supply chain based on fuzzy synthetic evaluation. Energy, Vol. 182, pp. 397-411.
Wu, Y., and Zhou, J. (2019). Risk assessment of urban rooftop distributed PV in energy performance contracting (EPC) projects: an extended HFLTS-DEMATEL fuzzy synthetic evaluation analysis. Sustainable Cities and Society, Vol. 47, pp. 101524.
Xu, M., Cui, Y., Hu, M., Xu, X., Zhang, Z., Liang, S., and Qu, S. (2019). Supply chain sustainability risk and assessment. Journal of Cleaner Production, Vol. 225, pp. 857-867.
ZEF., FARA., INRAT. (2017). Innovation for sustainable agricultural growth in Tunisia.
Program of accompanying research for agricultural innovation. Country Dossier: Bonn,
Accra and Tunis: Center for development research, Forum for agricultural research in Africa
and Institut National de recherché Agronomique de Tunis
Zhang, X., and Su, J. (2019). A combined fuzzy DEMATEL and TOPSIS approach for estimating participants in knowledge-intensive crowdsourcing. Computers & Industrial Engineering, Vol. 137, pp. 106085.
Zhu, G. N., Hu, J., and Ren, H. (2020). A fuzzy rough number-based AHP-TOPSIS for design concept evaluation under uncertain environments. Applied Soft Computing, Vol. 91, pp. 106228.