Simulation analysis for managing and improving productivity A case study of an automotive company

Purpose - The current economic crisis increased the demand on management to improve process efficiency. The purpose of this paper is to identify and resolve inefficiencies within the car assembly system utilizing discrete simulation modeling and analysis in order to improve productivity at one of the original equipment manufacturers (OEM) body shops in North America. Design/methodology/approach - This research was driven by a manager's recommendation from one of the Big Three (GM, Ford, Chrysler LLC) companies in order to improve operational performance. The data utilized in creating the simulation model was obtained from one of the assembly facilities that produce three different vehicles over a period of one year. All model simulation, inputs and outputs were discussed and agreed upon by facility management. Findings - The established base model was verified and validated to mimic the actual facility outputs indicating all process bottlenecks. Two model scenarios were considered: the first scenario focussed on the top bottleneck processes flexibility with a ROI of 497 percent, while the second considered changing the model mix percentage leading to a cost improvement of $1.6 million/annually. Research limitations/implications - The model only considered management decision on buffer sizes, batch size and the top bottleneck station alternatives to make improvements. Simulating improvements in labor efficiency, robots uptime, scrap root cause, and maintenance response to downtime where not considered. Practical implications - This paper indicated the importance of discrete simulation modeling in providing alternatives for improving process efficiency under certain financial limitations. Given the similarity of the automotive manufacturing processes among the various companies, the findings for this particular facility remain valid for other facilities. Originality/value - Investment cost and process improvement are currently the two biggest challenges facing operations managers in the manufacturing industry. This study allows managers to gain a broader perspective on discrete simulation ability to simulate complicated systems and present different process improvement alternatives.