A taxonomy of factors influencing worker's performance in human–robot collaboration

The occurrence of human errors significantly affects the performance and economic results of production systems. In this context, Human Reliability Analysis (HRA) methods play a key role in assessing the reliability of a man–machine system. Several HRA methods use Performance-Shaping Factors (PSFs), that is, all the aspects of human behaviour and environment that can affect human performance, to evaluate the Human Error Probability (HEP). However, despite the greater emphasis given by researchers to define of PSFs in recent years, the changes caused by the new enabling technologies implemented in manufacturing systems and derived from the Industry 4.0 paradigm have not yet been fully explored. Focussing on Human–Robot Collaboration (HRC) in production systems, the authors aim to define a PSF taxonomy that is useful for HEP evaluations in collaborative environments. To the best of the authors' knowledge, HRA approaches have not been investigated yet for HRC applications. The proposed taxonomy, which results from the integration of the most significant factors impacting workers' performance in HRC into the PSFs provided by an HRA method, can represent an important contribution for researchers and practitioners towards improving HRA methods and their applications in the context of Industry 4.0.