26 PROCESS STABILITY ATTRIBUTIVE DATA: U-, C- CHART

26 PROCESS STABILITY ATTRIBUTIVE DATA: U-, C- CHART
In the 26th Minitab tutorial, we are in the final assembly department of Smartboard Company. Here, all individual skateboard components are currently assembled into a finished skateboard in the early late and night shifts, and then subjected to an automatic surface inspection before dispatch to the customer in order to check that no surface damage in the form of scratches, was caused during assembly. In the past, skateboards without surface scratches were classified in the „good“ quality category and could be released to customers. Accordingly, skateboards with surface damage were classified in the „poor“ quality category, and therefore either had to be reworked at great expense or, in the worst case scrapped. In order to be able to record the extent of surface damage in a more differentiated way in the future, the number of surface scratches has also been recorded by the automatic surface inspection system. In this Minitab tutorial, we will therefore learn to map the number of scratches detected on each product using a quality control chart, and to analyze whether the assembly process can be classified as a stable process in terms of the number of scratches.

In contrast to the training unit, in which we used the p and np chart, to set the number of bad parts in relation to the respective sample subgroup, and were able to carry out our stability analysis using the statistical laws of binomial distribution, this practical scenario now deals with the case, where the number of events on a product, in our case the number of scratches on a skateboard, is the focus of our stability analysis. We will learn that we are then dealing with Poisson distributed quality data. The central learning objective is to map the frequency of events, in our case the number of scratches on the skateboards by using a corresponding quality control chart and to analyze the assembly process with regard to its process stability. In this context, we will get to know the so-called u-chart, and c-chart, and learn how we can use a corresponding u-chart diagnosis, to check whether the scattering behavior of our data set also follows the laws of Poisson distribution sufficiently well. We will also understand how we can manually estimate the corresponding control limits according to the AIAG standard specifications. Based on our analysis results, we will then be able to derive appropriate improvement measures to improve process stability. We will then carry out another process stability analysis, based on the improved process, and compare the process stability of our improved process with the process stability of the original unimproved process. We will learn how to use the useful „Stages“ option, to divide an existing overall process into sub-processes, and thus also obtain the correct sub-process-related control limits. In the last part of this Minitab tutorial, we will also get to know the very useful so-called c- chart, on another data set consisting of constant subgroup sizes and understand, that the c- chart in contrast to the u- chart, is able to map the absolute event frequencies per subgroup.

MAIN TOPICS MINITAB TUTORIAL

• u- chart: Fundamentals
• u- chart diagnosis
• u- chart analysis
• Manual derivation of the control limits in the u- chart
• Division of the overall process into two sub-processes using the u- chart
• c- chart principle
• c- chart analysis
• Manual derivation of the control limits in the c- chart