Research on World Agricultural Economy

Leveraging Fuzzy Logic for Resilient Agricultural Supply Chains: Risk Mitigation and Decision-Making in Jordan

Suleiman Shelash

Electronic Marketing and Social Media Department, Economic and Administrative Sciences, Zarqa University, Zarqa 13110, Jordan Faculty of Business and Communications, INTI International University, Persiaran Perdana BBN, Putra Nilai, Nilai, Negeri Sembilan 71800, Malaysia

Asokan Vasudevan

Faculty of Business and Communications, INTI International University, Persiaran Perdana BBN, Putra Nilai, Nilai, Negeri Sembilan 71800, Malaysia

Menahi Mosallam Alqahtani

Management Department, Management Science, Community College of Qatar, Doha 23241, Qatar

Xiaoqian Sun

Faculty of Liberal Arts, Shinawatra University, 99 Moo 10, Bangtoey, Samkhok, Pathum Thani 12160, Thailand

Imad Ali

GNIOT Institute of Management Studies, Plot No. 7, Chanakya Block, Knowledge Park-II, Greater Noida, Uttar Pradesh 201306, India

DOI: https://doi.org/10.36956/rwae.v6i3.1602

Received: 18 December 2024 | Revised: 8 January 2025 | Accepted: 5 February 2025 | Published Online: 9 July 2025

---

Abstract

This study explored the use of fuzzy logic as a decision-making framework to address uncertainties and prioritize risks in the agricultural supply chain. Fuzzy logic's ability to handle imprecise and incomplete data was leveraged to develop a Risk Severity Index, assess risks across supply chain stages, and simulate various disruption scenarios. The research utilized a descriptive-analytical design, combining fuzzy logic modeling with risk mapping, correlation analysis, and scenario simulation. Primary data were collected through stakeholder inputs, and secondary data included weather patterns, market trends, and logistical disruptions. The fuzzy inference system converted qualitative data into linguistic risk classifications (Low, Moderate, High), providing a robust method for ranking risks. Scenario simulations tested the framework's adaptability to changing conditions, such as extreme weather events. The findings revealed significant regional disparities in risk levels, with the South region identified as the most vulnerable due to high rainfall variability, pest outbreaks, and logistical challenges. The fuzzy logic framework proved effective in identifying and prioritizing risks, enhancing decision-making and resilience across the supply chain. This study validates fuzzy logic as a practical tool for risk assessment and mitigation in agricultural supply chains. By integrating fuzzy logic with risk mapping and scenario analysis, the study provides actionable insights for stakeholders, enabling localized and adaptive interventions. Policymakers are encouraged to invest in climate-resilient infrastructure, market stabilization strategies, and capacity-building initiatives to strengthen the sustainability and efficiency of Jordan’s agricultural supply chains.

Keywords: Fuzzy logic; Decision-Making Framework; Risk Assessment; Supply Chain Resilience; Jordan

---

References

[1] Jabarin, A.S., 2021. Evolution of the role of the agricultural sector in the food security of Jordan: A SWOT analysis after a century of establishment. Jordan J Agric Sci. 17(3), 243. DOI: https://doi.org/10.35516/jjas.v17i3.82

[2] Hajar, H.A.A., Hajer, M.A.A., 2023. A multi-dimension sustainability assessment of the economic growth in Jordan using the Sustainability Window Analysis. J Knowl Econ. 15(2), 7171. DOI: https://doi.org/10.1007/s13132-023-01140-z

[3] Alrabei, A.M., 2023. Green electronic auditing and accounting information reliability in the Jordanian Social Security Corporation: The mediating role of cloud computing. Int J Financ Stud. 11(3), 114. DOI: https://doi.org/10.3390/ijfs11030114

[4] Mohammad, A.A.S., Al-Daoud, K.I., Al-Daoud, S.I.S., et al., 2024. Content marketing optimization: A/B testing and conjoint analysis for engagement strategies in Jordan. J Ecohumanism. 3(7), 3086–3099. DOI: https://doi.org/10.62754/joe.v3i8.5066

[5] Alrabei, A.M., Jawabreh, O., Saleh, M.M.A., 2023. Accounting information and role it on financial reports quality in Jordanian hotels, and social performance as a mediating effect. Int J Sustain Dev Plan. 18(7), 2271–2279. DOI: https://doi.org/10.18280/ijsdp.180732

[6] Sowmiyaa, S., Lavanya, S.M., Mahendran, K., et al., 2021. An insight into fuzzy logic computation technology and its applications in agriculture and meteorology. Orient J Comput Sci Technol. 13(203), 97. DOI: https://doi.org/10.13005/ojcst13.0203.06

[7] Alrabei, A.M., Al-Othman, L.N., Abutaber, T.A., et al., 2023. Nexus between intellectual capital and financial performance sustainability: Evidence from listed Jordanian firms. Appl Math. 17(5), 881–888. DOI: http://dx.doi.org/10.18576/amis/170514

[8] Somi, S., Seresht, N.G., Fayek, A.R., 2020. Framework for risk identification of renewable energy projects using fuzzy case-based reasoning. Sustainability. 12(13), 5231. DOI: https://doi.org/10.3390/su12135231

[9] Alrabei, A.M., Al-Othman, L.N., Al-Dalabih, F.A., et al., 2022. The impact of mobile payment on the financial inclusion rates. Inf Sci Lett. 11(4), 1033–1044. DOI: http://dx.doi.org/10.18576/isl/110404

[10] Mohammad, A.A.S., Al-Daoud, K.I., Mohammad, S.I.S., et al., 2024. Analysing the effectiveness of omnichannel marketing strategies on customer experience in Jordan. J Ecohumanism. 3(7), 3074–3085. DOI: https://doi.org/10.62754/joe.v3i7.5063

[11] Almomani, T., Almomani, M., Obeidat, M., et al., 2023. Audit committee characteristics and firm performance in Jordan: The moderating effect of board of directors’ ownership. Uncertain Supply Chain Manag. 11(4), 1897–1904. DOI: http://dx.doi.org/10.5267/j.uscm.2023.6.002

[12] Alrabei, A.M.A., 2017. Perception of Jordanian banks employees on the relationship between accounting information quality (AIQ) and documentary credits. Int J Appl Bus Econ Res. 15(19), 409–419.

[13] Adger, W.N., Brown, I., Surminski, S., 2018. Advances in risk assessment for climate change adaptation policy. Philos Trans R Soc A Math Phys Eng Sci. 376(2121). DOI: https://doi.org/10.1098/rsta.2018.0106

[14] World Bank, 2022. Climate is central to Jordan’s development model. 9 November 2022. https://www.worldbank.org/en/news/press-release/2022/11/09/climate-is-central-to-jordan-s-development-model

[15] Qi, X., Zhang, J., Zhao, S., et al., 2017. Tackling complex emergency response solutions evaluation problems in sustainable development by fuzzy group decision making approaches with considering decision hesitancy and prioritization among assessing criteria. Int J Environ Res Public Health. 14(10), 1165. DOI: https://doi.org/10.3390/ijerph14101165

[16] Faur, M., Bungău, C., 2021. Proactive risk assessment via fuzzy approach in a decisional process of consignment stock program adoption. MATEC Web Conf. 343, 7012. DOI: https://doi.org/10.1051/matecconf/202134307012

[17] Al-Sawaie, K.M., Abbas, N.A., AlSmeiran, M., et al., 2025. Estimating potential output using a production function approach. Appl Math Inform Sci. 19(2), 271–278. DOI: http://dx.doi.org/10.18576/amis/190204

[18] Choudhary, V., 2015. Agricultural risk management in the face of climate change. Available from: http://documents.vsemirnyjbank.org/curated/ru/787511468170682886/Agricultural-risk-management-in-the-face-of-climate-change

[19] Alrabei, A.M., 2023. The mediating effect of COVID-19 pandemic on the nexus between accounting information systems reliability and e-commerce: From the perception of documentary credit employees. Inf Sci Lett. 12(8), 2867–2876. DOI: http://dx.doi.org/10.18576/isl/120835

[20] Mohammad, A.A., Shelash, S.I., Saber, T.I., et al., 2025. Internal audit governance factors and their effect on the risk-based auditing adoption of commercial banks in Jordan. Data Metadata. 4, 464. DOI: https://doi.org/10.56294/dm2025464

[21] Derbas, F.B., Al-Qudah, A.M., 2018. The impact of technology on Jordanian agricultural sector. Int J Acad Res Econ Manag Sci. 7(1). DOI: https://doi.org/10.6007/ijarems/v7-i1/4013

[22] Mohammad, A.A.S., Khanfar, I.A., Al-Daoud, K.I., et al., 2024. Impact of perceived brand dimensions on consumers' purchase choices. J Ecohumanism. 3(7d), 2341–2350. DOI: https://doi.org/10.62754/joe.v3i7.4382

[23] Gabr, M.E., 2023. Impact of climatic changes on future irrigation water requirement in the Middle East and North Africa’s region: A case study of upper Egypt. Appl Water Sci. 13(7). DOI: https://doi.org/10.1007/s13201-023-01961-y

[24] Sultana, A., 2020. Climate variability and wheat crop yield in Pakistan: Analyzing food security prospects in selected agro-climatic zones. Pak Soc Sci Rev. 4(4), 16. DOI: https://doi.org/10.35484/pssr.2020(4-iv)02

[25] Nimer, A., Sana, K.H.A., van den Berg, C., 2016. The cost of irrigation water in the Jordan Valley. World Bank: Washington, DC, USA. DOI: https://doi.org/10.1596/k8697

[26] Morales, L.E., 2017. The effects of international price volatility on farmer prices and marketing margins in cattle markets. Int Food Agribus Manag Rev. 21(3), 335. DOI: https://doi.org/10.22434/ifamr2017.0020

[27] Baffes, J., Kshirsagar, V., Mitchell, D., 2017. What drives local food prices? Evidence from the Tanzanian maize market. World Bank Econ Rev. 33(1), 160. DOI: https://doi.org/10.1093/wber/lhx008

[28] Mohammad A.A.S., Alolayyan M.N., Al-Daoud K.I., 2024. Association between social demographic factors and health literacy in Jordan. J Ecohumanism. 3(7), 2351-2365. DOI: 10.62754/joe.v3i7.4384

[29] Akayleh F.A., 2013. Agricultural development in the age of trade liberalisation: What did we really get? A case study of Jordan. Int J Econ Bus Res. 5(3), 286. DOI: https://doi.org/10.1504/ijebr.2013.052484

[30] Yaseen T., 2019. Invasive pests that threaten strategic agricultural crops in the Arab and MENA region. New Medit. 18(4). DOI: https://doi.org/10.30682/nm1904i

[31] I. K. Abu Yaman., 2023. Insect pests of the Jordan River Valley (Al-Ghor). DOI: https://onlinelibrary.wiley.com/doi/10.1111/j.1439-0418.1967.tb02059.x

[32] Khader B.F.Y., Yigezu Y.A., Duwayri M.A., et al., 2019. Where in the value chain are we losing the most food? The case of wheat in Jordan. Food Sec. 11(5), 1009. DOI: https://doi.org/10.1007/s12571-019-00962-7

[33] Agrawal P., Narain R., Ullah I., 2019. Analysis of barriers in implementation of digital transformation of supply chain using interpretive structural modelling approach. J Model Manag. 15(1), 297. DOI: https://doi.org/10.1108/jm2-03-2019-0066

[34] Feng P., Zhou X., Zhang D., et al., 2022. The impact of trade policy on global supply chain network equilibrium: A new perspective of product-market chain competition. Omega. 109. DOI: https://doi.org/10.1016/j.omega.2022.102612

[35] Vorley B., Cotula L., Chan M.K., 2012. Tipping the balance: Policies to shape agricultural investments and markets in favour of small-scale farmers. 6 December 2012. Oxfam International: Oxford, UK. DOI: https://doi.org/10.21201/2012.2288

[36] Gao Y., Feng Z., Zhang S., 2021. Managing supply chain resilience in the era of VUCA. Front Eng Manag. 8(3), 465. DOI: https://doi.org/10.1007/s42524-021-0164-2

[37] Trang H.T.T., Hong P.T.T., 2021. Measuring the impact of infrastructure quality on firm performance: A review of literature, metrics, and evidence. Proceedings of the International Conference on Emerging Challenges: Business Transformation and Circular Economy (ICECH 2021); 5-6 November 2021; Hanoi, Vietnam. Advances in Economics, Business and Management Research. Atlantis Press: Paris, France. DOI: https://doi.org/10.2991/aebmr.k.211119.022

[38] Zandi P., Rahmani A.M., Khanian M., et al., 2020. Agricultural risk management using fuzzy TOPSIS analytical hierarchy process (AHP) and failure mode and effects analysis (FMEA). Agriculture. 10(11), 504. DOI: https://doi.org/10.3390/agriculture10110504

[39] Nay J.J., Abkowitz M., Chu E., et al., 2014. A review of decision-support models for adaptation to climate change in the context of development. Clim Dev. 6(4), 357. DOI: https://doi.org/10.1080/17565529.2014.912196

[40] Afifa Y.N., Santoso I., 2022. Proactive risk mitigation strategies and building strategic resilience in the food supply chain: A review. Food Res. 6(2), 9. DOI: https://doi.org/10.26656/fr.2017.6(2).257

[41] Verner D., Lee D., Ashwill M., et al., 2013. Increasing resilience to climate change in the agricultural sector of the Middle East. The World Bank: Washington, D.C., USA. DOI: https://doi.org/10.1596/978-0-8213-9844-9

[42] Shaar I.M.A., Khattab S.A., Alkaied R., et al., 2022. Supply chain integration and green innovation, the role of environmental uncertainty: Evidence from Jordan. Uncertain Supply Chain Manag. 10(3), 657. DOI: https://doi.org/10.5267/j.uscm.2022.5.009

[43] Briske D.D., Joyce L.A., Polley H.W., et al., 2015. Climate-change adaptation on rangelands: Linking regional exposure with diverse adaptive capacity. Front Ecol Environ. 13(5), 249. DOI: https://doi.org/10.1890/140266

[44] Dhanda K.K., Sarkis J., Dhavale D.G., 2021. Institutional and stakeholder effects on carbon mitigation strategies. Bus Strat Environ. 31(3), 782. DOI: https://doi.org/10.1002/bse.2917

[45] Ettahiri, F.E., Elmaallam, M., 2019. The impact of fuzzy logic on knowledge management in the context of supply chain. Proceedings of the ACM International Conference on Computing and Communication Technologies; 27-29 March 2019; Rabat, Morocco. 82(1). DOI: https://doi.org/10.1145/3320326.3320395

[46] Hazell, P., 2010. The role of markets for managing agricultural risks in developing countries. In: Otsuka, K., Kalirajan, K. (eds.). Agricultural Development and Rural Poverty Reduction, 1st ed. Palgrave Macmillan London: London, UK. 291. DOI: https://doi.org/10.1057/9780230295018_17

[47] Tchonkouang, R.D., Onyeaka, H., Nkoutchou, H., 2024. Assessing the vulnerability of food supply chains to climate change-induced disruptions. Sci Total Environ. 920. DOI: https://doi.org/10.1016/j.scitotenv.2024.171047

[48] Mushtaq, S., Kath, J., Stone, R., et al., 2020. Creating positive synergies between risk management and transfer to accelerate food system climate resilience. Clim Change. 161(3), 465. DOI: https://doi.org/10.1007/s10584-020-02679-5

[49] Nakandala, D., Lau, H.C.W., Zhao, L., 2016. Development of a hybrid fresh food supply chain risk assessment model. Int J Prod Res. 55(14), 4180. DOI: https://doi.org/10.1080/00207543.2016.1267413

[50] Li, Z.P., Yee, Q.M.G., Tan, P.S., et al., 2013. An extended risk matrix approach for supply chain risk assessment. Proceedings of the IEEE International Conference on Industrial Engineering and Engineering Management; 10-13 December 2013; Bangkok, Thailand. DOI: https://doi.org/10.1109/ieem.2013.6962700

[51] Miroudot, S., 2020. Reshaping the policy debate on the implications of COVID-19 for global supply chains. J Int Bus Policy. 3(4), 430. DOI: https://doi.org/10.1057/s42214-020-00074-6

[52] McDonald-Madden, E., Baxter, P.W.J., Passingham, H.P. 2008. Making robust decisions for conservation with restricted money and knowledge. J Appl Ecol. 45(6), 1630. DOI: https://doi.org/10.1111/j.1365-2664.2008.01553.x

[53] Bertsimas, D., Thiele, A., 2006. Robust and data-driven optimization: Modern decision making under uncertainty. DOI: https://doi.org/10.1287/educ.1063.0022

[54] Kruse, C.S., Karem, P., Shifflett, K., 2016. Evaluating barriers to adopting telemedicine worldwide: A systematic review. J Telemed Telecare. 24(1), 4. DOI: https://doi.org/10.1177/1357633x16674087

[55] Flood, I., Issa, R.R.A., O'Brien, W.J., 2003. Barriers to the development, adoption, and implementation of information technologies: Case studies from construction. Towards a Vision for Information Technology in Civil Engineering; 15-16 November 2003; Nashville, Tennessee, USA. 1(4). DOI: https://doi.org/10.1061/40704(2003)30