The imported materials and components are usually include a small number that are defective, but it isn't known how many or how much it is costing. The gathered data is on the number of defective components that become defective at various points in the manufacturing process. On the surface, it appears that defective parts are not a major problem. Upwards of 99% of components are acceptable at each stage of the process. However, the combined effect of the defective parts leads to 15-20% waste in final products, which can translate into 200,000 defective units per million produced. For example, if materials cost $0.50 per unit, you will lose approximately $100,000 in waste before counting labor, machine time, and other expenses. Therefore, Six Sigma DMAIC is used to improve the existing products and/or processes.
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DMAIC (Define, Measure, Analyze, Improve, and Control) is used to improve existing products or processes. Suppose you are a costume jewelry manufacturer, coating inexpensive silver with thin layers of gold. You import materials and components from China. A small number of components are always defective, but you don't know how many or how much it costs. You have gathered the data (in the Data sheet) on the number of components that are defective or become defective at various points in the manufacturing process. On the surface, it appears that defective parts are not a major problem. Upwards of 99% of components are acceptable at each stage of the process. However, the combined effect of the defective parts leads to 15-20% waste in final products, which can translate into 200,000 defective units per million produced. For example, if materials cost $0.50 per unit, you will lose approximately $100,000 in waste, before counting labor, machine time, and other expenses. So, you need to reduce the number of defective units produced. However, the process is long and complicated, and you don't know which stage to begin with. Using @RISK, you can simulate many different outcomes and pinpoint the manufacturing stage that is the worst offender. You can also obtain key process capability metrics for each stage, as well as for the entire process, that will help you improve quality and reduce waste. In this way, @RISK is being used in the Measure and Analyze portions of the DMAIC method. @RISK is used to measure the existing state of the process, with capability metrics, and analyze how it might be improved with sensitivity analysis.
Using the historical data, @RISK's distribution fitting feature was used to define distribution functions describing the number of defective parts at each stage of the process: unpackaging/inspection, cutting, cleaning, and electroplating. The defective parts per million (DPPM) for each stage, and the process as a whole, are designated as @RISK outputs with Six Sigma specifications for the specification limits and target values. After the simulation is run, a variety of Six Sigma metrics are obtained for each stage, and the process as a whole.
Finally, sensitivity analysis and a tornado graph reveal that the cutting stage is the most to blame for overall product defects, even though another stage, cleaning, has a lower yield (more defects) on average. Even though the average yield of cutting is higher, the cutting process is less consistent and has more variation than the other processes.