Carly Barry And Eston Martz 2014-02-26 05:33:32
Your process is producing parts that don’t all meet your customer’s specifications. You’re not happy. Your customer is not happy. Your boss is not happy. Fortunately, after some hard work, you finally find a way to improve the process. However, you want to perform the appropriate statistical analysis to verify your results and easily explain the process improvements to your boss. What should you do? The solution is before and after process capability analysis, performed with statistical software such as Minitab. A process capability analysis evaluates how well process outputs meet specifications. For example, a photocopier maker requires rubber rollers to be between 32.523 cm and 32.527 cm wide to avoid paper jams. Capability analysis reveals how well the manufacturing process meets these specifications, and provides insight into how to improve it. Before and after Process caPaBility analysis Assessing process capability before and after making process changes can be a valuable and easy way to prove improvements were made, while also ensuring your process is still meeting specification limits and producing “good” parts. But before assessing processing capability, you must first ensure your process is stable—you can’t predict the performance of an unstable process! But you can predict, and improve on, a stable process. The following example illustrates how one group of engineers used control charts and capability analysis to assess a process and then prove they improved it. Using Process caPaBility to assess a Process An extruded parts maker needed to verify their process was running between the lower spec limit of 17.5 hardness units and the upper limit of 22.5 hardness units, with the target value of 20. To collect data for their analysis, operators randomly selected three extruded The control charts revealed a stable process with all points falling within the control limits. However, the histogram showed that many measurements fell outside the specification limits, with 73,603 parts per million defective (overall). With more parts falling above the upper spec limit than below the lower limit, the engineers concluded that the process mean must be shifted. Also, the histogram shows that variation needed to be reduced in order to reduce the number of defective parts and improve the capability of the process. Using Process caPability to Verify imProVements The engineers adjusted the process to reduce the variation and obtain a process mean closer to 20. So once again, operators collected 30 parts using the same sampling method, and measured the hardness of the parts. The engineers were happy to see their adjustment to the process reduced the PPM from 73,603 to 2,681 – a 96% reduction in percent out of spec—and the process mean shifted from 20.820 to 20.037, along with reduced variation. Best of all, the engineers had statistical proof and graphs to easily explain the process improvements to their boss! If you need to perform capability analysis, check out the Assistant in Minitab 17 Statistical Software: The Assistant can also help you with basic graphical analysis, measurement systems analysis, hypothesis tests, regression, creating control charts, and even design of experiments. You can see all of what the Assistant has to offer by trying Minitab 17 free for 30 days—download the trial at www.minitab17.com. Minitab 17 gives you the statistical power to improve quality and the confidence to know you’ve done it right. Minitab inc. Visit www.minitab.com for software tutorials, including Quick start exercises that show you how to analyze data using the assistant, as well as blog posts, case studies, upcoming webinars, and more.
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