Task complexity, and operators’ capabilities as predictor of human error: Modeling framework and an example of application

This paper presents the initial framework adopted to assess human error in assembly tasks at a large manufacturing company in Ireland. The model to characterize and predict human error presented in this paper is linked conceptually to the model introduced by Rasch (1980), where the probability of a specified outcome is modelled as a logistic function of the difference between the person capacity and item difficulty. The model needs to be modified to take into account an outcome that is not dichotomous and feed into the interaction between two macro factors: (a) Task complexity: that sum-marises all factors contributing to physical and mental workload requirements for execution of a given operative task & (b) Human capability: that considered the skills, training and experience of the people facing the tasks, representing a synthesis of their physical and cognitive abilities to verify whether or not they are matching the task requirements. Task complexity can be evaluated as a mathematical construct considering the compound effects of Mental Workload Demands and Physical Workload Demands associated to an operator task. Similarly, operator capability can be estimated on the basis of the operators’ set of cognitive capabilities and physical conditions. A linear regression model was used to fit a dataset collected in R. The estimation of task complexity and operator skills was used to estimate human performance in a Poisson regression model. The preliminary results suggest that both elements are significant in predicting error occurrence.