The current work used discrete event simulation techniques to model the economics of quality within an actual automotive stamping plant. Automotive stamping is a complex, capital intensive process requiring part-specific tooling and specialised machinery. Quality control and quality improvement is difficult in the stamping environment due to the general lack of process understanding and the large number to interacting variables. These factors have prevented the widespread use of statistical process control. In this work, a model of the quality control techniques used at the Ford Geelong Stamping plant is developed and indirectly validated against results from production. To date, most discrete event models are of systems where the quality control process is clearly defined by the rules of statistical process control. However, the quality control technique used within the stamping plant is for the operator to perform a 100% visual inspection while unloading the finished panels. In the developed model, control is enacted after a cumulative count of defective items is observed, thereby approximating the operator who allows a number of defective panels to accumulate before resetting the line. Analysis of this model found that the cost sensitivity to inspection error is dependent upon the level of control and that the level of control determines line utilisation. Additional analysis of this model demonstrated that additional inspection processes would lead to more stable cost structures but these structures many not necessarily be lower cost. The model was subsequently applied to investigate the economics of quality improvement. The quality problem of panel blemishes, induced by slivers (small metal fragments), was chosen as a case stuffy. Errors of 20-30% were observed during direct validation of the cost model and it was concluded that the use of discrete event simulation models for applications requiring high accuracy would not be possible unless the production system was of low complexity. However, the model could be used to evaluate the sensitivity of input factors and investigating the effects of a number of potential improvement opportunities. Therefore, the research concluded that it is possible to use discrete event simulation to determine the quality economics of an actual stamping plant. However, limitations imposed by inability of the model to consider a number of external factors, such as continuous improvement, operator working conditions or wear and the lack of reliable quality data, result in low cost accuracy. Despite this, it still can be demonstrated that discrete event simulation has significant benefits over the alternate modelling methods.
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