A comparative simulation study of work processes in autonomous production cells

An approach to human-centered design and assessment of work processes in flexible manufacturing systems with the help of dynamic task networks is presented. To model and simulate the task networks, the method of timed colored Petri Nets is used. Two task networks are developed. The first task network is a model of work processes in Autonomous Production Cells (APCs). The second task network represents work processes in conventional Computer Numerically Controlled (CNC)-based manufacturing systems. The material processing technology is associated with 5-axis milling. The values of attributes of task elements were acquired empirically on a fine-grained level with reference to a sample milling order. Comparative hypotheses regarding time-on-task, supervisory control functions, levels of cognitive control, human error (HE), and labor division were then formulated. To test these hypotheses, several simulation experiments were conducted. The results from inferential statistics show that single-operator APCs have a 30% higher efficiency in relation to total time-on-task. Moreover, the level of cognitive control is significantly shifted toward rule- and knowledge-based behavior. Surprisingly, the simulation of minor HE does not demonstrate a significantly worse performance from APCs. A simulated labor division among central process planner and production operator allows an additional efficiency improvement of approximately 15%. However, the labor division has two important drawbacks: first, a sequential incompleteness of operators' task spectrum occurs; second, the operator has to cope with hierarchical task incompleteness. Finally, a sensitivity analysis was carried out to investigate the effects of varying lot sizes and number of processed orders. © 2002 John Wiley & Sons, Inc.

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