Manufacturing quality professionals love to hear the words “in control” when talking about processes. From a Statistical Process Control (SPC) point of view, saying that a process is in control means that it is stable or predictable. After putting in the work to get a process in control, how do you make sure it stays in control?
Every process has variation. While some sources of variation may be known and considered minor, critical variations must be detected and removed in order to maintain a stable process.
Understanding and Addressing Variation
Dr. Walter A. Shewhart identified the following two sources of process variation:
- Common Cause—Variation that is inherent as part of the process. Examples of common cause variation include natural wear and tear, changes in humidity, old machines, and other expected influences.
- Special Cause—Variation that is outside of the normal process. Examples of special cause variation include influences such as operator error, a broken part, or a power outage.
SPC provides statistical methods to observe the performance of a process in order to predict, identify, and remove sources of variation. To help maintain a stable process, you can use these practical tools:
- Real-Time Data—Data collection in real time provides early detection. Immediate corrective action can be taken to minimize making bad products.
- Control Charts—Control charts provide process performance relative to specified control limits and, therefore, can differentiate between common cause and special cause of variation.
- SPC Control Rules—When a process triggers a control rule, it is detecting an out-of-control or non-random condition. Depending where the data lies in the control chart, further investigation will be warranted.
- Corrective Actions—Methods for eliminating a source of variation may include proper training, well-defined process standards, and developing a robust process through process refinement.
Variation is present in all things. The challenge is to identify what is and is not natural variation and then create an action plan to eliminate the variation. The approaches above will help to maintain a historically established level of variation.
But Wait: It’s Not Time to Uncork the Champagne Just Yet
First, a little reminder. Don’t confuse control limits with specification limits, which represent the desired final product. Just being in control doesn’t always mean that the process is “good.” It’s possible to have a process that is in a state of statistical control but producing bad or out-of-specification parts.
What if you’ve got a process that is in control and producing products that are well within specification limits? Does continuous improvement mean that process should be improved at all costs?
This is where the economics of SPC come into play. If you are running a process in control with a high capability, it probably isn’t worth the time and cost necessary to improve that process. Moreover, you may even consider reducing your sampling frequency and focus your efforts on another process that is struggling.
An in-control process simply implies that a process has performed to a degree of stability in the past and that stability is expected to continue going forward. If the process is producing good products, this means you can expect the process to continue to do so.
The same is true if the process is producing bad products. Having a process that will allow for a predictable outcome of saleable goods and services is the first step. Making that process perform within desired specification is the ultimate goal.
So, identify process variation, use manufacturing Quality Intelligence to make strategic decisions, and put yourself in control.
For more information on how to get processes in control, we invite you to visit our website to learn more about the benefits of modernizing your SPC toolset.