Strategic Decision-Making for AI-Based Predictive Safety in OHS: A Fuzzy FBWM–MARCOS Model

Umut Elbir

doi:10.35674/kent.1744339

TR EN

İş Sağlığı ve Güvenliğinde Yapay Zekâ Tabanlı Öngörücü Güvenlik İçin Stratejik Karar Verme: Bulanık FBWM–MARCOS Modeli

Öz

Bu çalışma, yapay zekâ tabanlı tahmine dayalı güvenlik sistemlerinin iş sağlığı ve güvenliği (İSG) yönetimine entegrasyonunda stratejik karar vermeyi incelemektedir. Amaç, belirsizlik koşullarında alternatif yapay zekâ çözümleri arasından seçim yapmaya yardımcı olacak, katı ve şeffaf bir çok ölçütlü karar çerçevesi geliştirmek ve uygulamaktır. Temel araştırma sorusu şudur: İSG yönetimi için güvenlik iyileştirmesi, kurumsal uygulanabilirlik ve stratejik uyum arasında en iyi dengeyi hangi yapay zekâ tabanlı tahmine dayalı güvenlik alternatifi sağlar? Entegre bulanık ÇÖKA yaklaşımı kapsamında ölçüt ağırlıklarının belirlenmesinde Bulanık En İyi–En Kötü Yöntemi (FBWM), alternatiflerin önceliklendirilmesinde ise MARCOS kullanılmış; teknik performans, insan faktörleri, hukuki/uyumsal uygunluk, maliyet ve uygulama olgunluğu gibi boyutlarda uzman görüşleri değerlendirilmiştir. Bulgular, nihai sıralamada güvenlik etkisi ve teknolojik hazırlık düzeyinin belirleyici olduğunu; maliyet ve hukuki uygunluğun düzenleyici rol oynadığını göstermektedir. Farklı ağırlıklandırma senaryolarında yapılan duyarlılık testlerinde kritik sıra değişimleri gözlenmemiş, modelin yönetsel olarak güvenilir olduğu teyit edilmiştir. Sonuçlar, yatırım ve İSG kurulları için uygulanabilir öneriler sunmakta; çalışmanın amacı ve merkezî araştırma sorusunu netleştirerek gelecekteki uygulamalara yol göstermektedir.

Anahtar Kelimeler

Decision-making under uncertainty , Multi-criteria decision analysis , Risk assessment frameworks , Safety technology adoption , Construction industry safety , Fuzzy Methods

Strategic Decision-Making for AI-Based Predictive Safety in OHS: A Fuzzy FBWM–MARCOS Model

Abstract

This study investigates strategic decision-making for integrating artificial intelligence–based predictive safety systems into occupational health and safety (OHS) management. The aim is to develop and apply a rigorous, transparent multi-criteria decision framework that helps organizations select among competing AI-driven safety solutions under uncertainty. The core research question is: Which AI-based predictive safety alternative offers the best balance of safety improvement, organizational feasibility, and strategic fit for OHS management? An integrated fuzzy MCDM approach combines Fuzzy Best–Worst Method (FBWM) to elicit criterion weights with MARCOS to prioritize alternatives evaluated by domain experts across technical performance, human factors, legal/regulatory fit, cost, and implementation readiness. The analysis highlights the dominant influence of safety impact and technological readiness on final rankings, while cost and legal compliance act as moderating considerations. Sensitivity tests across weighting schemes indicate stable priority orders without critical rank reversals, supporting managerial robustness. The findings provide actionable guidance for investment and OHS committees, demonstrate the practicality of a hybrid fuzzy model for high-risk settings, and clarify both the study’s aim and its central research question for future replications.

Keywords

Belirsizlik altında karar verme , Çok ölçütlü karar analizi , Risk değerlendirme çerçeveleri , Güvenlik teknolojilerinin benimsenmesi , İnşaat sektörü güvenliği

Kaynakça

Abdullah, A. G. (2024). Prioritizing Safety Aspects: Advanced Multi-Criteria Decision-Making for Nuclear Power Plant Site Selection in Indonesia. Journal of Sustainable Development of Energy, Water and Environment Systems, 12(4), 1-20.
Arena, S., Florian, E., Zennaro, I., Orrù, P. F., & Sgarbossa, F. (2022). A novel decision support system for managing predictive maintenance strategies based on machine learning approaches. Safety science, 146, 105529.
Badida, P., Janakiraman, S., & Jayaprakash, J. (2023). Occupational health and safety risk assessment using a fuzzy multi-criteria approach in a hospital in Chennai, India. International journal of occupational safety and ergonomics, 29(3), 1047-1056.
Bafail, O., & Alamoudi, M. (2025). Prioritizing Worker-Related Factors of Safety Climate Using Fuzzy DEMATEL Analysis. Systems, 13(5), 383.
Balfour Beatty. (2024). Balfour Beatty mandates human-recognition cameras across UK projects. Retrieved from https://www.balfourbeattyus.com
Berglund, L., Johansson, J., Johansson, M., Nygren, M., & Stenberg, M. (2023). Exploring safety culture research in the construction industry. Work, 76(2), 549-560.
Bhargava, A., Bester, M., & Bolton, L. (2021). Employees’ perceptions of the implementation of robotics, artificial intelligence, and automation (RAIA) on job satisfaction, job security, and employability. Journal of Technology in Behavioral Science, 6(1), 106-113.
Bianchi, G., Freddi, F., Giuliani, F., & La Placa, A. (2025). Implementation of an AI-based predictive structural health monitoring strategy for bonded insulated rail joints using digital twins under varied bolt conditions. Railway Engineering Science, 1-18.
Çağlarer, E. (2024). A current overview of multi-criteria decision-making methods used in occupational health and safety. Review of Management and Economic Engineering, 23(4), 320–337.
Cai, J., Hu, Y., Peng, Y., Guo, F., Xiong, J., & Zhang, R. (2024). A hybrid MCDM approach based on combined weighting method, cloud model and COPRAS for assessing road construction workers’ safety climate. Frontiers in Public Health, 12, 1452964.

Chatzoglou, P. D., Kotzakolios, A. E., & Marhavilas, P. K. (2025). Health and Safety Management System (HSMS) and Its Impact on Employee Satisfaction and Performance—A New HSMS Model. Safety, 11(2), 52.
Construction Dive. (2025, May 8). Balfour Beatty designates live traffic as fifth fatal risk in construction safety initiative.Retrieved from https://www.constructiondive.com
Construction Dive. (2025, October 23). Predictive AI construction group attracts more leading firms. Retrieved from https://www.constructiondive.com
Construction Enquirer. (2024, July 29). Balfour puts human-recognition cameras on plant. Retrieved from https://www.constructionenquirer.com
Construction Management. (2024, July 29). Balfour mandates human-recognition tech on its UK projects. Retrieved from https://constructionmanagement.co.uk
de Almeida Calixto, B. S., & Michaloski, A. O. (2025). Assessment of Occupational Risk Using Multi-Criteria Fuzzy AHP Methodology in a University Laboratory. Sustainability, 17(6), 2715.
Dormakaba. (2025). Predictive analytics in construction safety: Transforming high-risk industries with AI. Retrieved from https://blog.dormakaba.com
DroneDeploy. (2025). Safety AI platform launch: Automated OSHA compliance with drone analytics. Retrieved from https://www.dronedeploy.com
Ecer, F., Murat, T., Dinçer, H., & Yüksel, S. (2025). A fuzzy BWM and MARCOS integrated framework with Heronian function for evaluating cryptocurrency exchanges: a case study of Türkiye. In Blockchain, Crypto Assets, and Financial Innovation (pp. 399-433). Springer, Singapore.
Ergülen, A., & Çalık, A. (2024). F-BWM-MARCOS approach for performance evaluation of Türkiye's top 500 industrial enterprises in the pre-pandemic and pandemic era. Benchmarking: An International Journal.
Falco, A., Girardi, D., Dal Corso, L., Yıldırım, M., & Converso, D. (2021). The perceived risk of being infected at work: An application of the job demands–resources model to workplace safety during the COVID-19 outbreak. PloS one, 16(9), e0257197.
Hannover Messe. (2019, July 11). AI engine Vinnie to warn of accidents. Retrieved from https://www.hannovermesse.de
Hassija, V., Chamola, V., De, R., Das, S., Chakrabarti, A., Sangwoan, K. S., & Pandey, A. (2024). A Survey on Digital Twins: Enabling Technologies, Use Cases, Application, Open Issues and More. IEEE Journal of Selected Areas in Sensors.
He, Y., He, J., & Wen, N. (2023). The challenges of IoT-based applications in high-risk environments, health and safety industries in the Industry 4.0 era using decision-making approach. Journal of Innovation & Knowledge, 8(2), 100347.
Huber, J., Anzengruber-Tanase, B., Schobesberger, M., Haslgrübler, M., Fischer-Schwarz, R., & Ferscha, A. (2025). Evaluating User Safety Aspects of AI-Based Systems in Industrial Occupational Safety: A Critical Review of Research Literature. International Journal of Environmental Research and Public Health, 22(5), 705.
Jin, H., & Goodrum, P. M. (2021). Optimal fall protection system selection using a fuzzy multi-criteria decision-making approach for construction sites. Applied Sciences, 11(11), 5296.
Khalilzadeh, M., Ghasemi, P., Afrasiabi, A., & Shakeri, H. (2021). Hybrid fuzzy MCDM and FMEA integrating with linear programming approach for the health and safety executive risks: a case study. Journal of modelling in management, 16(4), 1025-1053.
Komatina, N., Djapan, M., Ristić, I., & Aleksić, A. (2021). Fulfilling external stakeholders’ demands—enhancement workplace safety using fuzzy mcdm. Sustainability, 13(5), 2892.
La Fata, C. M., Giallanza, A., Micale, R., & La Scalia, G. (2021). Ranking of occupational health and safety risks by a multi-criteria perspective: Inclusion of human factors and application of VIKOR. Safety science, 138, 105234.
Mendes, J. P., Marques, M., & Guedes Soares, C. (2024). Risk avoidance in strategic technology adoption. Journal of Modelling in Management, 19(5), 1485-1509.
Mikołajewska, E., Mikołajewski, D., Mikołajczyk, T., & Paczkowski, T. (2025). Generative AI in AI-Based Digital Twins for Fault Diagnosis for Predictive Maintenance in Industry 4.0/5.0. Applied Sciences, 15(6), 3166.
Nadeem, M. A., Surienty, L., & Haque, M. M. (2022). Impact of organization decision making styles and safety accountability on occupational health and safety implementation: The moderating role of mimetic motives. Frontiers in public health, 10, 1004767.
Navarro Claro, G. T., Bayona Soto, J. A., & Arévalo Ascanio, J. G. (2025). Impact of the occupational health and safety management system (OHSMS) on human talent management and organizational performance in the construction sector. Frontiers in Built Environment, 11, 1618356.
Park, J., & Kang, D. (2024). Artificial Intelligence and Smart Technologies in Safety Management: A Comprehensive Analysis Across Multiple Industries. Applied Sciences, 14(24), 11934.
Pro Builder. (2019, November 5). Can AI predict construction hazards and improve safety? Retrieved from https://www.probuilder.com
Rashid, A. B., & Kausik, A. K. (2024). AI revolutionizing industries worldwide: A comprehensive overview of its diverse applications. Hybrid Advances, 100277.
Rodríguez-Espíndola, O., Chowdhury, S., Dey, P. K., Albores, P., & Emrouznejad, A. (2022). Analysis of the adoption of emergent technologies for risk management in the era of digital manufacturing. Technological Forecasting and Social Change, 178, 121562.
Salehi, V., Moradi, G., Omidi, L., & Rahimi, E. (2023). An MCDM approach to assessing influential factors on healthcare providers’ safe performance during the COVID-19 pandemic: Probing into demographic variables. Journal of Safety Science and Resilience, 4(3), 274-283.
Sheraz, M., Chuah, T. C., Lee, Y. L., Alam, M. M., Al-Habashna, A. A., & Han, Z. (2024). A comprehensive survey on revolutionizing connectivity through artificial intelligence-enabled digital twin network in 6g. IEEE Access, 12, 49184-49215.
Singh, B., Chandra, S., Wongmahesak, K., & Shrivastava, D. (2025). Mobilizing Urban Planning and Resource Management: Emerging Trends in Digital Twin-Driven Healthcare. Accelerating Product Development Cycles With Digital Twins and IoT Integration, 155-174.
Sorensen, G., Dennerlein, J. T., Peters, S. E., Sabbath, E. L., Kelly, E. L., & Wagner, G. R. (2021). The future of research on work, safety, health and wellbeing: A guiding conceptual framework. Social science & medicine, 269, 113593.
Stoumpos, A. I., Kitsios, F., & Talias, M. A. (2023). Digital transformation in healthcare: technology acceptance and its applications. International journal of environmental research and public health, 20(4), 3407.
Taj, I., & Zaman, N. (2022). Towards industrial revolution 5.0 and explainable artificial intelligence: Challenges and opportunities. International Journal of Computing and Digital Systems, 12(1), 295-320.
Vural, S., Dalyan, O., Öztürk, Ö. F., & Pişkin, M. (2025). Sustainable OSH Training for Safety Culture in Construction Industry: The Case of Izmir Province. Disiplinlerarası Eğitim Araştırmaları Dergisi, 9(21), 367-381.
Xu, J., Li, J., & Chen, X. (2024). Assessment of technology adoption in construction safety management: Case study in China. Science Progress, 107(4), 00368504241304198.
Yiğitol, B. (2025). AI, Robotics, and Autonomous Systems: Assessing AI Applications in Aviation Industry. In Smart and Sustainable Operations Management in the Aviation Industry(pp. 115-141). CRC Press.

Ayrıntılar

Birincil Dil

İngilizce

Konular

Politika ve Yönetim (Diğer)

Bölüm

Araştırma Makalesi

Yazarlar

Umut Elbir ^*
0000-0003-1416-9731
Türkiye

Yayımlanma Tarihi

24 Şubat 2026

Gönderilme Tarihi

16 Temmuz 2025

Kabul Tarihi

5 Şubat 2026

Yayımlandığı Sayı

Yıl 2026 Cilt: 19 Sayı: 2

DOI

https://doi.org/10.35674/kent.1744339

IZ

https://izlik.org/JA26DB63XL

APA

Elbir, U. (2026). Strategic Decision-Making for AI-Based Predictive Safety in OHS: A Fuzzy FBWM–MARCOS Model. Kent Akademisi, 19(2), 1-22. https://doi.org/10.35674/kent.1744339

International Refereed and Indexed Journal of Urban Culture and Management | Kent Kültürü ve Yönetimi Uluslararası Hakemli İndeksli Dergi

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İş Sağlığı ve Güvenliğinde Yapay Zekâ Tabanlı Öngörücü Güvenlik İçin Stratejik Karar Verme: Bulanık FBWM–MARCOS Modeli

Öz

Anahtar Kelimeler

Strategic Decision-Making for AI-Based Predictive Safety in OHS: A Fuzzy FBWM–MARCOS Model

Abstract

Keywords

Kaynakça

Ayrıntılar

Birincil Dil

Konular

Bölüm

Yazarlar

Yayımlanma Tarihi

Gönderilme Tarihi

Kabul Tarihi

Yayımlandığı Sayı

DOI

IZ

Kaynak Göster