Bulanık Fine–Kinney Yöntemiyle Operasyonel Tedarikçi Risklerinin Değerlendirilmesi: Fast-Food Zincir Restoranında Bir Vaka Çalışması

Abstract

Fast-food işletmeleri, zamanlama, süreç güvenilirliği ve tedarikçi koordinasyonunun hizmet sürekliliği ile müşteri memnuniyetini belirlediği dinamik ve rekabetçi ortamlarda faaliyet göstermektedir. Bu işletmelerin standartlaştırılmış, sık tekrarlanan ve zaman baskısı altında gerçekleşen teslimat süreçlerine olan bağımlılığı, onları operasyonel aksaklıklara karşı oldukça kırılgan hale getirmektedir. Bu nedenle tedarikçi kaynaklı risklerin erken tespiti, izlenmesi ve önceliklendirilmesi fast-food işletmeleri açısından son derece kritik bir gereklilik olarak öne çıkmaktadır. Bu çalışmada Manisa’da faaliyet gösteren bir fast-food zincir restoranında tedarikçiyle ilişkili operasyonel riskler bulanık Fine–Kinney yöntemiyle değerlendirilerek tedarik zinciri güvenilirliğini artırmaya yönelik kritik risk alanları saha uygulaması üzerinden ortaya konulmuştur. Çalışmada benimsenen yaklaşımla, klasik Fine–Kinney yönteminin sistematik yapısı, uzman görüşlerindeki belirsizlikleri temsil etmeye olanak tanıyan bulanık mantığın esnekliğiyle birleştirilmiş ve dilsel yargıların nicel ifadelere dönüştürülerek ele alınması sağlanmıştır. Tedarik operasyonlarında doğrudan görev alan çalışanlardan elde edilen değerlendirmeler sonucunda teslimat zaman uyuşmazlıkları, soğuk zincir ihlalleri, paketleme ve taşıma hasarları, iletişim yetersizlikleri ve ekipman arızaları en kritik risk faktörleri olarak belirlenmiştir. Bulgular, fast-food tedarik sistemlerinin zamanlama doğruluğu, koordinasyon kalitesi ve süreç tutarlılığına yüksek düzeyde duyarlı olduğunu göstermektedir. Çalışma, bulanık Fine–Kinney yönteminin belirsizlik koşullarında risklerin sistematik biçimde analiz edilmesi ve önceliklendirilmesinde güçlü ve uyarlanabilir bir araç olduğunu ortaya koymakta, ayrıca fast-food sektöründe operasyonel sürekliliğin güçlendirilmesi, tedarikçi performansının izlenmesi ve risk temelli karar süreçlerinin geliştirilmesine yönelik pratik katkılar sunmaktadır.

Keywords

• Fast-food tedarik zincirleri
• Tedarik zinciri risk yönetimi
• Tedarikçi riskleri
• Operasyonel risk değerlendirmesi
• Bulanık Fine-Kinney yöntemi

Assessment of Supplier-Related Operational Risks Using the Fuzzy Fine–Kinney Method: A Fast-Food Chain Case Study

Abstract

Fast-food restaurants operate in dynamic and competitive environments where timing accuracy, process reliability, and supplier coordination determine service continuity and customer satisfaction. Their dependence on standardized, frequently repeated, and time-pressured delivery processes makes them highly vulnerable to operational disruptions. Therefore, the early detection, monitoring, and prioritization of supplier-related risks stand out as a critical necessity for fast-food enterprises. In this study, supplier-related operational risks in a fast-food chain restaurant operating in Manisa, Türkiye, were evaluated using the fuzzy Fine–Kinney method, and critical risk areas aimed at improving supply chain reliability were revealed through a field application. In the adopted approach, the systematic structure of the classical Fine–Kinney method was combined with the flexibility of fuzzy logic, which allows representing uncertainties in expert opinions and enables linguistic judgments to be transformed into quantitative expressions. Based on the evaluations obtained from employees directly involved in supply operations, delivery time mismatches, cold-chain violations, packaging and handling damages, communication deficiencies, and equipment malfunctions were identified as the most critical risk factors. The findings show that fast-food supply systems are highly sensitive to timing accuracy, coordination quality, and process consistency. The study reveals that the fuzzy Fine–Kinney method is a strong and adaptable tool for systematically analyzing and prioritizing risks under uncertainty, and additionally provides practical contributions for strengthening operational continuity, monitoring supplier performance, and improving risk-based decision-making processes in the fast-food sector.

Keywords

• Fast-food supply chains
• Supply chain risk management
• Supplier-related risks
• Operational risk assessment
• Fuzzy Fine-Kinney method

References

1. Aslan, S. (2022). Risk assessment of construction works in city square using Fine Kinney method. Çukurova Üniversitesi Mühendislik Fakültesi Dergisi, 37(2), 329-340. https://doi.org/10.21605/cukurovaumfd.1146062
2. Ayvaz, B., Tatar, V., Sağır, Z., & Pamucar, D. (2024). An integrated Fine-Kinney risk assessment model utilizing Fermatean fuzzy AHP-WASPAS for occupational hazards in the aquaculture sector. Process Safety and Environmental Protection, 186, 232-251. https://doi.org/10.1016/j.psep.2024.04.025
3. Birgören, B. (2017). Calculation challenges and solution suggestions for risk factors in the risk analysis method in the Fine Kinney risk analysis method. International Journal of Engineering Research and Development, 9(1), 19-25. https://doi.org/10.29137/umagd.346168
4. Ceylan, B. O. (2025). Control theory-based fuzzy Fine-Kinney risk assessment for boiler automation system from the maritime autonomous surface ships (MASS) perspective. Ocean Engineering, 322, 120444. https://doi.org/10.1016/j.oceaneng.2025.120444
5. Çınar, F., Solmaz, M. S., & Çakmak, E. (2021). Evaluation of ship manoeuvres in port by using fuzzy Fine Kinney method. International Journal of Environment and Geoinformatics, 8(4), 537-548. https://doi.org/10.30897/ijegeo.938973
6. Cui, Y., & Basnet, C. (2015). An exploratory study of supply chain risk management in the New Zealand fast food industry. International Journal of Logistics Systems and Management, 20(2), 199-215. https://doi.org/10.1504/IJLSM.2015.067256
7. Dogan, B., Oturakci, M., & Dagsuyu, C. (2022). Action selection in risk assessment with fuzzy Fine–Kinney-based AHP-TOPSIS approach: a case study in gas plant. Environmental Science and Pollution Research, 29(44), 66222-66234. https://doi.org/10.1007/s11356-022-20498-2
8. Fang, C., Chen, Y., Wang, Y., Wang, W., & Yu, Q. (2023). A Fermatean fuzzy GLDS approach for ranking potential risk in the Fine-Kinney framework. Journal of Intelligent & Fuzzy Systems, 45(2), 3149-3163. https://doi.org/10.3233/JIFS-230423

9. Gökler, S. H., Yılmaz, D., Ürük, Z. F., & Boran, S. (2022). A new hybrid risk assessment method based on Fine-Kinney and ANFIS methods for evaluation spatial risks in nursing homes. Heliyon, 8(10), e11028. https://doi.org/10.1016/j.heliyon.2022.e11028
10. Gul, M., & Celik, E. (2018). Fuzzy rule-based Fine-Kinney risk assessment approach for rail transportation systems. Human and Ecological Risk Assessment: An International Journal, 24(7), 1786-1812. https://doi.org/10.1080/10807039.2017.1422975
11. Gul, M., Yucesan, M., & Ak, M. F. (2022). Control measure prioritization in Fine− Kinney-based risk assessment: a Bayesian BWM-Fuzzy VIKOR combined approach in an oil station. Environmental Science and Pollution Research, 29(39), 59385-59402. https://doi.org/10.1007/s11356-022-19454-x
12. Gunasinghe, M. D. S., & Cooray, N. H. K. (2020). Impact of supply chain uncertainty and risk on perceived organizational performance in fast food industry with special reference to Anuradhapura District. Sri Lanka Journal of Management Studies, 2(1), 17-43. https://doi.org/10.4038/sljms.v2i1.26
13. Ilbahar, E., Karaşan, A., Cebi, S., & Kahraman, C. (2018). A novel approach to risk assessment for occupational health and safety using Pythagorean fuzzy AHP & fuzzy inference system. Safety science, 103, 124-136. https://doi.org/10.1016/j.ssci.2017.10.025
14. Karahan, V., & Aydoğmuş, E. (2023). Risk Analysis and risk assessment of laboratory work by Fine Kinney method. International Journal of Advanced Natural Sciences and Engineering Researches, 7(4), 442-446. https://doi.org/10.59287/ijanser.788
15. Kartal, D., & Soyluk, A. (2023). Revision of Fuzzified Fine-Kinney Method, an Adaptive Method for Natural Disaster Risk Management. Geoconservation Research, 6(2), 409-426. https://doi.org/10.57647/j.gcr.2023.0602.25
16. Kinney, G. F., & Wiruth, A. D. (1976). Practical risk analysis for safety management. China Lake, CA: Naval Weapons Center.
17. Manuj, I., & Mentzer, J. (2008). Global supply chain risk management. Journal of Business Logistics, 29(1), 133-155. https://doi.org/10.1002/j.2158-1592.2008.tb00072.x
18. Milli, A., Salman, S., & Sancak, E. (2021). A case of risk assessment by using Fine-Kinney method in sub-leather processing. Usak University Journal of Engineering Sciences, 4(1), 42-57. https://doi.org/10.47137/uujes.907595
19. Mukucha, P., & Chari, F. (2022). Supply chain resilience: the role of supplier development in the form of contract farming in fast-food outlets in Zimbabwe. Continuity & Resilience Review 4(3), 280–299. https://doi.org/10.1108/CRR-03-2022-0006
20. Netro, Z. G., Romero, E. D., & Flores, M. J. (2018). Adaptation of the Fine-Kinney method in supply chain risk assessment. In C. A. Brebbia, F. Garzia, & M. Lombardi, Safety and Security Engineering VII (Wit Transactions on the Built Environment) (pp. 43-55). Wit Press.
21. Oturakçı, M., & Dağsuyu, C. (2017). Risk değerlendirmesinde bulanık fine‐kinney yöntemi ve Uygulaması. Karaelmas İş Sağlığı ve Güvenliği Dergisi, 1(1), 17-25. https://doi.org/10.33720/kisgd.327548
22. Över Özçelik, T., Yilmaz Yalciner, A., Cetinkaya, M., & Aker, A. (2025). Risk assessment with the fuzzy Fine-Kinney method in a business operating in the metal industry. International Journal of Occupational Safety and Ergonomics, 31(1), 308-317. https://doi.org/10.1080/10803548.2024.2438562
23. Özbakır, O. (2023). Hazard and risk assessment in a dairy products factory in Iğdır province using the Fine Kinney Risk Method: recommendations for mitigation. International Journal of Agriculture Environment and Food Sciences, 7(3), 563-572. https://doi.org/10.31015/jaefs.2023.3.10
24. Pajić, V., & Andrejić, M. (2023). Risk analysis in internal transport: An evaluation of occupational health and safety using the Fine-Kinney method. Journal of Operational and Strategic Analytics, 1(4), 147-159. https://doi.org/10.56578/josa010401
25. Satici, S., & Mete, S. (2023). Fine-Kinney-Based Occupational Risk Assessment using Pythagorean Fuzzy AHP-COPRAS for the Lifting Equipment in the Energy Distribution and Investment Sector. Gazi University Journal of Science. https://doi.org/10.35378/gujs.1227756.
26. Sianturi, G., Fiatno, A., Henny, M., & Hakim, M. D. A. (2025). Hazard risk assessment in post weld heat treatment process using the fine-kinney method. Indonesian Journal of Industrial Engineering & Management, 6(1), 61-72. https://doi.org/10.22441/ijiem.v6i1.26055
27. Srivastava, M., & Rogers, H. (2021). Managing global supply chain risks: effects of the industry sector. International Journal of Logistics Research and Applications, 25(7), 1091–1114. https://doi.org/10.1080/13675567.2021.1873925
28. Varzandeh, J., Farahbod, K., & Jake Zhu, J. (2016). Global logistics and supply chain risk management. Journal of Business & Behavioral Sciences, 28(1), 124- 130.
29. Wang, W., Han, X., Ding, W., Wu, Q., Chen, X., & Deveci, M. (2023). A Fermatean fuzzy Fine-Kinney for occupational risk evaluation using extensible MARCOS with prospect theory. Engineering Applications of Artificial Intelligence, 117, 105518. https://doi.org/10.1016/j.engappai.2022.105518
30. Wang, W., Jiang, W., Han, X., & Liu, S. (2022). An extended gained and lost dominance score method based risk prioritization for Fine-Kinney model with interval type-2 fuzzy information. Human and Ecological Risk Assessment: An International Journal, 28(1), 154–183. https://doi.org/10.1080/10807039.2021.2023807