## 4 Discussion  
To effectively discuss the contributions of the current investigation, it is necessary to compare the present situation. However, existing risk assessments such as RULA, REBA, and similar evaluations are based on biomechanical models that are either not clearly defined or not publicly available. Additionally, these assessments do not take into account the anthropometric characteristics of workers. As a result, the risk values they produce are merely approximations. In contrast, the proposed model calculates biomechanical values based on the specific anthropometric characteristics of individual workers. This makes direct comparison between the models not applicable. On the other hand, the proposed fuzzy logic model performs group evaluations, unlike current assessments, which are carried out independently one by one. Generating all the evaluations at the same time allows a graph with the global behaviour pattern to be generated, which serves the specialist in decision making to improve tasks so that the risk for the majority of the workers is minimised.  
To achieve these results, we developed a decision support system that incorporates 54 membership equations for operations involving fuzzy sets. This system is important for evaluating how anthropometry and biomechanics affect ergonomic assessments and to what extent. The behavioural pattern of the group of 123 evaluations changes as risk value is added. For example, in Test 1, a low-risk task was analysed, so a biomechanical risk pattern inherent only to the worker's anthropometry was observed. In Test 2, a medium-risk task was analysed; in this case, the pattern of evaluations changed, and behaviour more affected by anthropometric and biomechanical characteristics was seen, although the trend was not yet very clear. Finally, in Test 3, the influence of anthropometry and biomechanics was clearly observed in the pattern of risk assessments, which followed the same shape as the anthropometric set.  
While the research objectives were achieved, the system requires improvement to enhance the accuracy of the evaluation. Currently, the DSS lacks some necessary information, so future efforts are suggested to refine the system and extend it applicability to other body parts. Artificial intelligence can be integrated initially as training software, followed by its implementation as an evaluation system.  
This model enhances the current theoretical framework by managing an infinite number of combinations without requiring the development of fuzzy rules, which can become numerous when combining various task elements. It incorporates the necessary components to compete effectively with risk assessments that analyse the same factors. This system is more accurate because it directly measures and utilises the worker's biomechanical and anthropometric values. It improves precision and offers a risk measurement system useful for users who may not be very familiar with such calculations.  
Finally, by utilising the fuzzy model, we can identify at least three types of suggestions for the improvement process and polices:  
1. Improving work tasks enables the establishment of minimum, average, and maximum parameters within which these tasks can be performed without risking musculoskeletal disorders.
2. Taking anthropometric and biomechanic risks into account allows for the redesign of work areas based on the characteristics of the users.
3. From a social responsibility standpoint, understanding body mass index (BMI) can help generate internal policies that promote healthy eating among employees.