Using task network modeling to predict human error
Pop, Vlad L.
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Human error taxonomies have been implemented in numerous safety critical industries. These taxonomies have provided invaluable insight into understanding the underlying causes of human error; however, their utility for actually predicting future errors remains in question. A need has been identified for another approach to supplement what we can extrapolate from taxonomies and better predict human error. Task network modeling is a promising approach to human error prediction that had yet to be empirically evaluated. This study tested a task network modeling approach to predicting human error in the context of automotive assembly. The task network modeling architecture was expanded to include a set of predictors from the human error literature, and used to model part of an operational automotive assembly plant. This manuscript contains three studies. Study 1 tested separate task network models for two different target areas of an active automotive assembly line. Study 2 tested the validity of predictions made by the models from Study 1, both within and across samples. Study 3 tested predictions across both models on a larger sample of vehicles. The expanded architecture accounted for 21.9% to 36.5% of the variance in human error and identified 12 explanatory variables that significantly predicted the occurrence of human error. Model outputs were used to compute prediction equations that were tested using binary logistic regression and then cross-validated twice using both split-half and cross-sample validation. The predictors of Time Pressure, Visual Workload, Auditory Workload, Cognitive Workload, Psychomotor Workload, Task Frequency, Information Flow, Teamwork, and Equipment Feedback were significant predictors of human error in all three models that were tested. The variables of Information Presentation and Task Dependency varied in significance across samples, but both were significant in two out of the three models. The variables of Shift and Hour into Shift were never significant in any of the three models. The variables that were greatly stable across studies were all related to the tasks being performed by each worker at each station. The variables related to the timing of errors, on the other hand, were never significant. The results indicate that an expanded task network architecture is a great tool for predicting the situations and circumstances in which human errors will occur, but not the timing of when they will occur. Nevertheless, task network modeling demonstrated to provide useful, valid, and accurate predictions of human error and should continue to be developed as an error prediction tool.