A model to evaluate the Human Error Probability in inspection tasks of a production system

Investigating the dynamics of human-machine interaction and its impacts on production performance is a key issue in the context of Industry 4.0 because the concept of the “operator 4.0”, i.e., an operator integrated into a cyber-physical system, implies the need to manage complex human-machine systems. One of the most concerned fields on this topic is human reliability analysis, as the Human Error Probability (HEP) estimation by considering different work environment aspects. To this concern, the present work's purpose consists of assessing human error's impact on a manufacturing system by considering the different Performance Shaping Factors (PSFs) that affect the HEP. To this end, an analytical model has been developed to evaluate the human error in an inspection task to be accomplished in a full-real industrial case study. The HEP was estimated as a function of PSFs, including three different dimensions (i.e., task error proneness, operator's capabilities and characteristics of the work environment in the production system). It was found that the most impactful PSF affecting HEP depends on the working environment conditions in the production system. In this regard, the model shows that assuming an equal variation in the attributes related to all dimensions, the changes in working environment conditions from a physical and psychological point of view generate the most significant reduction in HEP. Consistently with these results, the average costs for reducing HEP by improving the working environment conditions are significantly higher than the average costs incurred to reduce the HEP considering the task error proneness and operator's capabilities.