On Optimizing Manufacturing Systems - A User Perspective
Recent developments and trends in manufacturing have resulted in tremendous efficiency increases. However, the full potential of advanced manufacturing systems has not yet been seen and efficiency problems remain. Traditional strategies for optimizing are directed mainly towards technical enhancements, but as humans play an important role in the manufacturing system their contribution to sustainable system efficiency should not be neglected. Manufacturing system optimizing requires careful considerations of both human and technical factors in a crossdisciplinary approach. This thesis aims to approach the efficiency problem from a user perspective. The system is seen as the means to fulfill tasks which the human operators are responsible for. Methods for optimizing system performance from this perspective are presently lacking in the manufacturing area.
A methodology which enables optimization of advanced manufacturing systems is presented. The methodology is called Task Evaluation and Analysis Methodology (TEAM) and provides a set of methods that combined will pinpoint problem areas in the system. The methodology evaluates the system holistically from four viewpoints; system work environment, system work tasks, system information flow, and statistical system performance. System users are involved in the evaluation to provide domain expert knowledge and to increase the possibilities of user acceptance of suggested improvements. Based on the deep understanding gained while performing TEAM, suggestions for optimization can be made.
The methodology has been applied in five case studies in different types of industrial manufacturing systems, including a large CIM cell, an FMS, and two parts of a flexible and automated welding line. Results from all studies show that TEAM identifies relevant problem areas and provides possibilities for high user acceptance of improvements. Efficiency increases have been reported, but since these cells are subject to constant changes, a separation of causes and effects has not always been possible.