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In the 25th Minitab tutorial, we are back in the final assembly department of Smartboard Company. Here, in the early late and night shifts, all the individual skateboard components are assembled into a finished skateboard, and subjected to a final automatic surface inspection before being shipped to the customer. Skateboards without surface damage are classified in the attributive category „good“, and can be sold. Skateboards with surface damage are classified in the attributive category „bad“, and either undergo cost-intensive reworking or in the worst case, have to be scrapped. The core topic in this training unit will be to learn how process stability can be investigated on the basis of this categorical data. To this end, we will first learn how the number of defective skateboards can be displayed chronologically in the form of a defect rate, by using a suitable quality control chart. We will then use the correct choice of quality control chart to assess whether the skateboard assembly process can be classified as a stable process from a qualitative perspective. In this context we will understand, that if our process data is only available in two attributive categories, as in this case in the attributive categories good part and bad part, quality control charts that take into account the laws of binomial distribution are always suitable. In this context, we will get to know the so-called quality control chart, P-chart. Before we can create the P-chart, we will first carry out a so-called P-chart diagnosis, to ensure that our data follows the laws of binomial distribution sufficiently well. Here we examine the important parameters, such as overdispersion and underdispersion, which provide us with information on how much the scattering behavior of our actual data landscape deviates from the scattering behavior of a theoretically ideal binomial distribution, and whether this deviation is still acceptable. With the knowledge we have gained up to this point, we will be able to decide in the context of the corresponding AIAG standard specifications, whether we should actually continue to work with the P-chart or whether we should use a modified P-chart, the so-called P-prime chart according to Laney due to an inadmissible over- or under-dispersion. In addition to the P- chart at the end of this training unit we will also get to know the so-called useful n-p- chart, which is also able to clearly and chronologically depict absolute proportions of defective units instead of relative proportions.


  • p- chart: Diagnosis
  • p- chart: Structure and principle
  • p- chart analysis
  • Working with identification variables
  • Manual derivation of the upper and lower control limits in the p- chart
  • np- chart: structure and principle
  • np- chart analysis
  • p`- chart according to Laney