A NEW ACCIDENT ANALYSIS MODEL PROPOSAL IN OCCUPATIONAL SAFETY RISK MANAGEMENT: "STAR DIAGRAM"


Creative Commons License

çınar u.

Yönetim Bilimleri Dergisi, cilt.21, sa.48, ss.205-219, 2023 (Hakemli Dergi) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 21 Sayı: 48
  • Basım Tarihi: 2023
  • Doi Numarası: 10.35408/comuybd.1231675
  • Dergi Adı: Yönetim Bilimleri Dergisi
  • Derginin Tarandığı İndeksler: TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.205-219
  • Çanakkale Onsekiz Mart Üniversitesi Adresli: Evet

Özet

Occupational accidents are a global problem in terms of their effects. Different techniques for the analysis of accidents are available in the literature. Accident analyzes, which experts often conduct with linguistic expressions, cause some problems in expressing the sizes and distributions of root and supporting factors. Existing approaches do not clearly reflect the distribution of factors that may cause an accident. In this direction, a new model has been proposed to the literature for the analysis of occupational accidents within the scope of this study. The proposed model was developed based on accident theories and complies with the principles of root cause analysis. However, it has brought many innovations to existing applications. The proposed method, the factors affecting the accident; It divides it into 5 main groups as “Personal”, “Environment”, “Management”, “Machine and Equipment” and “Organization”. Each of these identified main factors is characterized by schematic distribution to the 5 arms of a star. For this reason, the method is called "Star Diagram". The proposed model is a first in the literature in this field. The main parameters in risk analysis are probability and severity. Before accidents occur, the magnitude of the risk is expressed in line with these parameters. Inspired by this, the proposed model determines to what extent the impact categories contribute to the probability of the event and the severity of the damage after the accident occurs. In this direction, relative data can be expressed as a percentage of which category affects the formation of the accident. The proposed model stands out with its categorized root cause analysis and quantitative magnitude expressions, which are not available in other techniques. In this respect, it fills an important gap in the literature.