Manufacturing complexity has become more challenging with increased in demand fluctuation, product customisation and shorter lead time expectation. It is becoming more crucial to measure manufacturing complexity to better recognise and control the various manufacturing components to achieve optimum manufacturing performance. Cellular manufacturing or group technology is a method used to manage manufacturing complexity based on clustering of different types of equipment to process parts. The organizational structure of cellular manufacturing will always need to be flexible for reconfiguration to address rapid changes in customer requirement especially in managing its dual constraints; human and machine. Very often, the human component is overlooked or overestimated due to poor understanding of the effects of human constraints and lack of study is linked to the difficulty to model human’s behaviour. The purpose of this study is to develop a human system model to fill the gap in the study of human constraints on cellular manufacturing’s performance. As such, a new human system framework focusing on the aspects of human dynamics and attributes was designed to be integrated with the predetermined time standards system in an expert system, eMOST. The new human system model was evaluated for applicability at the actual manufacturing environment through five case studies where accurate labour utilisation and man-machine configuration information were conceived. Thus, the newly defined approach was able to efficiently improve data capture, analysis and model human constraints. The human information from the model was integrated with other manufacturing resources using WITNESS simulation modelling tool focusing on the bottleneck area to further evaluate the dynamic impact of these components on the manufacturing performance. Simulation modelling experiments use has also proven advantageous to change manufacturing configurations and run alternative scenarios to improve the efficiency of the system in terms of the throughput, cycle time, operator utilisation and man-machine configuration. The findings of this study enabled the management to make good decisions to efficiently manage the human resource and better predictions to reconfigure and competently manage resources allocation
