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

Öz

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.

Anahtar Kelimeler

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

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