It is well established that there exist eight dimensions of quality:
Each dimension can be explicitly defined and is self-exclusive from the other dimensions of quality. A customer may rate your service or product high in conformance, but low in reliability. Or they may view two dimensions to work in conjunction with eachother, such as durability and reliability.
This article will discuss the dimension of conformance and how process variation should be interpretted. Process variation is important in the Six Sigma methodology, because the customer is always evaluating our services, products and processes to determine how well they are meeting their critical to qualitys (CTQs); in other words, how well they conform to the standards.
Conformance can simply be defined as the degree to which your service or product meets the CTQs and predefined standards. For the purpose of this article, it should be noted that your organization’s services and products are a funtion of your internal processes, as well as your supplier’s processes. (We know that everything in business is a process, right?)
Here are a few examples:
A simple way to teach the concept of how well your service or product conforms to the CTQs is with a picture of a target. A target, like those used in archery or shooting, has a series of concentric circles that alternate color. In the center of the target is the bullseye. When services or products are developed by your organization, the bullseye is defined by CTQs, the parts are defined by dimensional standards, and the materials are defined by purity requirements. As we see from the four examples above, the conformance CTQs usually involve a target dimension (the exact center of the target), as well as a permissible range of variation (center yellow area).
In Figure 1, three pictures help explain the variation in a process. The picture on the left displays a process that covers the entire target. While all the bullets appear to have hit the target, very few are in the bullseye. This is an example of a process that is centered around the target, but very seldomly meets the CTQs of the customer.
The middle picture in Figure 1 displays a process that is well grouped on the target (all the bullets hit the target in close proximity to eachother), but is well off target. In this picture – like in the first picture – almost every service or product produced fails to meet the customer CTQs.
The far right picture in Figure 1 displays a process that is well grouped on the target, and all the bullets are within the bullseye. This case displays a process that is centered and is within the tolerance of the customer CTQs. Because this definition of conformance defines “good quality” with all of the bullets landing within the bullseye tolerance band, there is little interest in whether the bullets are exactly centered. For the most part, variation (or dispersion) within the CTQ specification limits is not an issue for the customer.
In the real-world, we seldom view our processes as bullseyes (unless you work at a shooting range). So how can you determine if your process is scattered around the target, grouped well but off the bullseye, or grouped well on the bullseye? We can display our data in frequency distributions showing the number (percentage) of our process outputs having the indicated dimensions.
One can easily see the direct relationship of Figure 2 to Figure 1. In Figure 2, the far left picture displays wide variation that is centered on the target. The middle picture shows little variation, but off target. And the far right picture displays little variation centered on the target. Shaded areas falling between the specification limits indicate process output dimensions meeting specifications; shaded areas falling either to the left of the lower specification limit or to the right of the upper specification limit indicate items falling outside specification limits.
Most Black Belts have little time to completely understand the variation of their process before they move into the Improve phase of DMAIC (Define, Measure, Analyze, Improve, Control). For instance, do the critical X‘s of your process have a larger impact on variation (spread) or central tendency (centering)? Segmentation or subgrouping the data can help you find the correct critical X. Hypothesis testing will help you prove that it is so.
Improvements in meeting customer CTQs and specification limits are objective measures of quality that translate directly into quality gains, because transactional processing errors, late deliveries and product defects are regarded as undesirable by all customers.