A theoretical framework for evaluating mental workload resources in human systems design for manufacturing operations

Abstract

As the nature of manufacturing work is changing, requiring more cognitive demands, there is a need to develop system models for measuring and predicting human performance in repetitive task operations. This paper presents a theoretical framework, which provides a systematic approach for measuring mental workload using a combination of analytical and empirical techniques: human performance modeling with a computer simulation and mathematical modeling, along with physiological, subjective and performance measures. For this study, the Air Force MATB, which is a re-development of the NASA simulation tool, was used to model multitasking in a controlled environment to validate the theoretical framework. The independent variable of task complexity was measured, in the modeling of resource demands for a cleaning-inspection process and a final inspection process, using three dependent variables (subjective, physiological and performance measures) with a total of four responses (NASA-TLX, Workload Profile, fixation duration and human error probability). The results indicate no significant difference among the response variables for each task complexity level, indicating the model accurately represents the operator's workload. Additional analysis shows accurate predication from the model in analyzing workload peaks. Relevance to industryThis theoretical framework is designed to evaluate operator mental workload utilization in the manufacturing domain. This is important because manufacturing work is changing by requiring more multitasking roles, which increases cognitive demand. Therefore, the industry needs models that can predict operator performance and mental workload for improved productivity.