Investigating the effects of age, task load, task complexity, and input device on monitoring performance for smart manufacturing in the oil refining industry

Human beings play an important role in a smart manufacturing economy. In this study, we explored the effects of age, task load, task complexity, and input device on abnormal event detection performance in an oil refinery control room task. Thirty participants were recruited to complete a process plant monitoring task in which they were asked to continuously monitor the gauge states, and immediately detect and solve the abnormal events. Participants’ accuracy in detecting abnormal states was recorded and analysed during the task. We found that the complexity factor affected accuracy significantly, and younger adults had significantly higher accuracy than older adults in high task load trials. No significant effect was found for the input device factor. These findings suggest that age, task load, and task complexity should be taken into consideration when designing tools to improve older operators’ performance. Practitioner summary: The smart manufacturing economy elicits higher requirements for older operators in oil refinery monitoring tasks. Under high task load, older adults had lower accuracy in detecting abnormal conditions than younger adults. The task complexity affected participants’ accuracy in detecting abnormal states.