29 PROCESS CAPABILITY, BINOMIALLY DISTRIBUTED
In the 29th Minitab tutorial, we take a closer look at the assembly process in the final assembly department at Smartboard Company. As we already know all the individual skateboard components are assembled into a finished skateboard in this department, and then subjected to an automatic surface inspection before being shipped to the customer. Skateboards without surface damage are classified in the attributive category „good“, and can be sold to the customers. Skateboards with surface damage are classified in the attributive category „bad“, and must either be reworked at great expense or, in the worst case scrapped. The special feature of this process capability analysis is that we are no longer dealing with normally distributed data, as our quality attribute is present in the two categories good and bad, and therefore the statistical laws of the so-called binomial distribution must also be taken into account. In this Minitab tutorial, we will therefore use tools for our process capability analysis that take the laws of binomial distribution into account. We will learn how to carry out the necessary tests with our data set in advance of the actual process capability analysis of binomially distributed data, in order to check whether the laws of binomial distribution are actually observed by our data set. With these findings, we can then carry out the necessary process stability analysis as a preliminary stage to the actual process capability analysis, in order to ensure that the necessary process stability is actually guaranteed. For this purpose, we will use the corresponding control charts, such as the p-chart, and the np-chart, which take into account the laws of binomial distribution. Finally, we can also assess the process performance of binomially distributed process data. The parameters such as the so-called cumulative proportions of defective units, and the so-called rate of defective units, will play an important role here. And we will be able to use a graphical derivation, to understand the sigma level in our binomially distributed process data landscape.
MAIN TOPICS MINITAB TUTORIAL 29