Today, the risk management of budget challenges throughout the budget process is greater than ever. The approach can also be suggested and adapted for production and service science environments where their occupational health & safety are highly required. The outcomes of this study are beneficial for OHS decision-makers by highlighting the most prioritized hazards causing serious occupational accidents in flights schools as part of aviation industry. In addition, by an innovative sensitivity analysis, the effect of major changes in the weight of each risk parameter on the final priority score and ranking of the hazards was evaluated. The results of existing studies were tested, and we considered both Pythagorean and Fermatean fuzzy aggregation operators. Within the scope of the study, the hazards pertaining to the flight and ground training, training management, administrative and facilities in a flight school were assessed and prioritized. In this study, an occupational risk assessment approach based on 3,4-quasirung fuzzy MCDM is presented. This makes this type of fuzzy set applicable in addressing many problems in multi-criteria decision making (MCDM). Since this new approach has a wider space, it can express uncertain information in a more flexible and exhaustive way. In this approach, the sum of the cube of the degree of membership and the fourth power of the degree of non-membership must be less than or equal to 1. The 3,4-quasirung fuzzy set (3,4-QFS) is a new type of fuzzy set theory emerged as an extension of the Pythagorean fuzzy sets and Fermatean fuzzy sets. Although fuzzy logic-related decision models related to the assessment of these risks have been developed and applied a lot in the literature, there is an opportunity to develop novel occupational risk assessment models depending on the development of new fuzzy logic extensions. Elimination or reduction to an acceptable level of analyzed risks, which is a systematic and proactive process, is then put into action. The concept of occupational risk assessment is related to the analysis and prioritization of the hazards arising in a production or service facility and the risks associated with these hazards risk assessment considers occupational health and safety (OHS). The paper shows that fault prioritization lacks research about available learning methods and underlines that expert opinions are needed. This paper reviews literature that presents methods for several steps of fault management and provides an overview of requirements for fault handling and methods for fault detection, fault classification, and fault prioritization, as well as their prerequisites. Machine Learning methods exploit this data to support fault management. The increasing usage of sensors to monitor machine health status in production lines leads to large amounts of data and high complexity. Data-driven methods can support fault management. The prioritization of faults accelerates the fault amendment process but depends on preceding fault detection and classification. For that, efficient fault management and quick amendment of faults in production lines are needed.
The increase of productivity and decrease of production loss is an important goal for modern industry to stay economically competitive.