Possibilistic regression analysis of influential factors for occupational health and safety management systems

The code of occupational health and safety (OHS) is an influential regulation to improve the on-the-job safety of employees. A number of factors influence the planning and implementation of OHS management systems (OHSMS). The evaluation of OHSMS practice is the most important component when forming a health and safety environmental policy for employees. The objective of this research is to develop an intelligent data analysis (IDA) in which possibilistic regression being endowed with a convex hull approach is used to support the analysis of essential factors that influence OHSMS. Given such subjective terms, the obtained samples can be conveniently regarded as fuzzy input/output data represented by membership functions. The study offers this vehicle of intelligent data analysis as an alternative to evaluate the influential factors in a successful implementation of OHS policies and in this way decrease an overall computational effort. The obtained results show that several related OHSMS influential factors need to be carefully considered to facilitate a successful implementation of the OHSMS procedure.

► The realization of intelligent data analysis (IDA) with the use of possibilistic regression completed for OHSMS analysis purposes.
► Becomes an alternative solution especially for realizing possibilistic regression analysis carried out in the presence of fuzzy data in OHSMS environment.
► Perform a real-world OHSMS data analysis process; to identify the most influential factors contributing to the successful implementation of the OHSMS.
► The proposed approach for possibilistic regression could become an efficient of computer-aided ergonomics tool.
► The proposed approach becomes a potential vehicle for analyzing real-world data in real-time processing where the ambiguity or fuzziness cannot be avoided such as OHSMS performance analysis.