Robotic Manufacturing Cell: An Analysis of Variables Affecting Performance

Analyzing the performance of manufacturing cells is a well established concept. The justification for conducting thorough analyses of manufacturing cells comes from the known advantages it can provide, including performance improvement and production planning improvement, both current and future. This study focuses on assessing the variables affecting performance of a robotic manufacturing cell through the measure of throughput. Initially, simulation modeling is utilized to model an existing robotic cell and compare the output to actual production output from the same cell. Additionally, general regression modeling is employed to analyze the following variables and their effect on throughput: machine downtime, off-plan time, setup time, weekly schedule requirements, scrap rate and preceding operation output. Results of the analysis show that off-plan time and setup time are the only significant predictors of performance throughput. Furthermore, general regression modeling based on real data, rather than simulation modeling, is more accurate in predicting throughput. Discussion and results are presented in this thesis, as well as the practical implications. Finally, an integrated methodology is proposed for analyzing the output performance of robotic manufacturing cells