When you are deciding on whether you should buy a certain product, what questions run through your mind? Does this product meet the conditions I have? Will it still be functional X months/years from now? Will I be able to buy a replacement that performs as well as it does now in the future? Certainly, as a customer, you want to be assured that your purchase will not be a waste of money and that you can potentially stay loyal to one brand or company. As a manufacturer, you want to ensure that you can satisfy your customers’ demand while maintaining a time- and cost-efficient process.

Well, you’re in luck! Today, we have statistical tools as our best friend. With their help, manufacturers and other business establishments can ensure that their product or service meets your expectations now and in the future. We can thank Walter A. Shewhart for his findings about variations in processes and for the creation of control charts, one of the most fundamental statistical tools in quality control, in the 1920s. These are used to assess the quality of a process and determine if it is capable. But, how is this done?

First, let’s start with the basics. The capability of a process refers to its ability to meet customer requirements or specifications. Capability statistics are calculated and analyzed to verify whether the process is capable. However, before a conclusion can be made, the following conditions must be satisfied:

1. The process must be stable.

   • If the process is unstable, that means there may be special causes of variation (i.e. unnatural variation) and the capability analysis results will, therefore, be invalid. Process stability can be verified with a control chart.

2. The data must fit the chosen distribution.

   • The standard capability analysis can only be used if the data is normally distributed, which can be verified with a probability plot or histogram. Non-normal capability analysis is performed on data with a non-normal distribution.

3. There must be a sufficient amount of data.

• • Like other research methods, a capability analysis requires a sample size that is representative of the observed process. Thus, it requires a large sample size (typically at least 100 observations) that is recorded over a period of time to reduce uncertainty of the data.

In a capability analysis, there are two main metrics to consider: short-term capability (Cp and Cpk) and overall capability (Pp and Ppk).

Short-term capability suggests how well a process can potentially meet the customer specifications, while overall capability informs how well the process performed over a period of time. The process is said to be stable and capable when the Cp and Pp values are almost equal, and 99.99% of all observations are within the upper and lower specification limits if these metrics are at least 1.33. Therefore, the desirable process must be able to meet both conditions.

It is easy to conclude that the process will be able to provide items or services of the same quality over time just by looking at these statistics. But how can we predict if it actually would maintain its quality in a longer period of time? Here’s where TRENDABLE™’s Key Trends feature comes in handy to generate long-term capability plots. Let’s look at two examples:

There are two key observations from the long-term capability plot: the Cpk of the process increases over time, and it increases above the Cpk target of 1.33. The manufacturer can then proceed with the existing stable and capable process. The next example will demonstrate the statistics that will be observed if the process is not capable.