30 PROCESS CAPABILITY, POISSON DISTRIBUTED

30 PROCESS CAPABILITY, POISSON DISTRIBUTED
In the 30th Minitab tutorial, we are still in the final assembly department of Smartboard Company. Here, all individual skateboard components are assembled into a finished skateboard and finally subjected to an automatic surface inspection before shipping to the customer to ensure that no undesirable surface damage e.g. in the form of scratches, has occurred during final assembly, which could lead to unwanted customer complaints. In the past skateboards without surface scratches were classified in the attributive quality category „good part“, and could be delivered to customers. Skateboards with surface damage, on the other hand were classified in the attributive quality category „bad part“, and either reworked at great expense or even scrapped. In order to record the severity of the surface damage on the skateboards in even greater detail, the number of scratches per skateboard has also been recorded by the automatic surface inspection system. The focus of this training unit is now to analyze whether the assembly process can be classified as a capable process in terms of the number of scratches. In a previous Minitab tutorial, in which the surface inspection system had only classified the skateboards into two categories good and bad, we were able to carry out all the necessary analysis steps based on the binomial distribution in order to evaluate the process capability. In this practical scenario, the focus is no longer on the number of defects parts per subgroup, but on the number of defects per skateboard and subgroup. We will therefore learn that in this case the statistical laws of the Poisson distribution, rather than the binomial distribution, apply. The focus of this tutorial is therefore on process capability analysis, based on the laws of Poisson distribution.

We will learn that process stability is also an important prerequisite for a capability analysis based on Poisson distributed characteristics, in order to be able to correctly evaluate the process capability. In this context, we will learn how to perform the Poisson distribution test to ensure that our data actually follows the laws of Poisson distribution. We will use the so-called quality control chart u-chart, and the corresponding control tests to quickly work out whether Smartboard Company’s assembly process also has the required process stability as a preliminary stage to process capability, under these conditions. In addition to the important „Poisson plot“, we will also get to know the informative “ Cumulative DPU plot“ diagram, and interpret both. We will then move into the actual process capability analysis of our Poisson distributed data, to obtain the required capability metrics to assess process capability. In this context, we will get to know a number of important parameters, such as the lower and upper confidence interval limit, or the key figure DPU, in order to ultimately be able to assess the process performance in the necessary depth. As part of our analysis, we will also get to know a very efficient option for generating all the necessary analysis steps and information for assessing the quality of the Poisson distribution, process stability and process capability in one step, in the form of a process capability report. With all the necessary information from this process capability report, we can reliably derive whether the process capability of our Poisson distributed data can be classified as a capable, or non-capable process in relation to the customer’s target specification.

MAIN TOPICS MINITAB TUTORIAL 30

• Process stability tests in the run-up to capability analysis
• u- chart, principle
• Accumulated DPU
• Poisson plot for analyzing Poisson distribution
• Cumulative DPU plot
• Statistical analysis of the process capability of poisson distributed data
• Lower and upper CI-limit
• DPU: mean, min/max and target