Abstract

The Fine–Kinney method (Fine in J Saf Res 3:157–166, 1971; Kinney and Wiruth in Practical risk analysis for safety management. Naval Weapons Center, pp 1–20, 1976), which was first introduced as an occupational health and safety risk analysis tool in the 1970s, is a systematic methodology that provides a mathematical formula for calculating the risk that arises due to a specified hazard. In the traditional version of Fine–Kinney as suggested in its original version, a risk score (RS) is calculated as a result of mathematical multiplication of probability (P), exposure (E), and consequence (C) parameters. These calculated risk scores are used to establish priorities for the corrective efforts in order to eliminate risks or reduce their effects to a reasonable level. This simple and useful method is preferred and implemented by small and medium-sized enterprises. In the academic literature, it has been applied for many risk analysis problems, although it includes several drawbacks recently revealed. In this method, no weight assignment is made for each risk parameter. Also, it is hard to assess consequence, exposure, and probability, precisely. Multi-criteria decision making (MCDM) is a pool of methods used in occupational health and safety risk analysis both by international standard-setting organizations and scholars from the literature. In classical MCDM methods, performance values and weights of decision criteria are known precisely and are specified with crisp numbers. However, many real-world problems contain uncertainties, and the knowledge and judgment of experts cannot be expressed precisely. Fuzzy-based MCDM methods, which are developed to reflect types and degrees of uncertainties better, produce more accurate results compared to classical methods. In this chapter, we first present the basics of Fine–Kinney method, including its implementing procedure, basic terminology, and drawbacks. Then, we provide a state-of-the-art review of Fine–Kinney occupational risk assessment method and its extensions by fuzzy sets. Graphical results obtained from the review are demonstrated to show the current state. Future work suggestions are also included to the chapter to show the possible gaps and possible opportunities.

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