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About this lesson
Statistical process control uses real-time process data to determine whether a process is maintaining the improvements that were implemented. Control charts will indicate special cause and common cause variation, empowering process operators to maintain process quality.
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Quick reference
Statistical Process Control
Statistical process control uses real-time process data to determine whether a process is maintaining the improvements that were implemented. Control charts will indicate special cause and common cause variation, empowering process operators to maintain process quality.
When to use
Statistical process control should be used after the solution is implemented to measure the process output of key parameters to ensure the process stays stable. Control charts can also be used in the Measure phase to set a baseline; in the Analyze phase to identify the presence of special cause variation, and in the Improve phase to illustrate the magnitude of the impact of the improvement.
Instructions
Control charts are used to track the process output performance to determine whether special cause or common cause variation is present. When special cause variation is present, the process is no longer stable and predictable. When there is only common cause variation present, the process is stable and predictable. The control chart does not show the customer requirements. However, the output can of course be compared to customer requirements to determine if it is in specification. There are four possible conditions:
- The ideal state shown in the upper right quadrant is just that, ideal. The process is stable and producing acceptable results.
- The threshold state is shown in the upper left quadrant is stable but producing some unacceptable results. Depending upon the magnitude and impact of the unacceptable results, the organization can choose to improve the process.
- The lower left quadrant is a process that is unstable and producing unacceptable results. The good news about this is that probably everyone knows that and will support a Lean Six Sigma project to improve this situation.
- The worst case is the lower right quadrant. Although currently producing good results the process is unstable and may at any point start producing unacceptable results.
During the Control phase of the Lean Six Sigma project and after the project completes, the control chart is used by the process operators and supervisors to monitor the process and ensure the improvement continues.
A control chart can also be used in other phases of a Lean Six Sigma project. During the Measure phase it can be used to establish baseline performance. During the Analyze phase it can be used to assist in the identification of special causes. During the Improve phase it can be used to show the impact of the improvement by comparing improved performance to the baseline performance recorded during the Measure phase.
Hints & tips
- While all control charts work the same way, the calculation of control limits varies depending upon the type of chart. Be sure you are using the correct chart based upon the process performance characteristic being tracked.
- Don’t overuse control charts. Too many charts become background noise and are no longer useful for process management. Just track the key input or output parameters of the process. Your Design of Experiments can help to identify those parameters.
- It may be difficult to get process operators or supervisors to understand the danger of the lower right quadrant performance (unstable but in specification) since the results are currently acceptable. Paint a worse-case scenario of the process shifting to an unacceptable output condition with no warning.
- 00:04 Hi, I'm Ray Sheen.
- 00:06 Another control approach that is often part of the solution for a Lean Six Sigma
- 00:11 project is the use of control charts based upon statistical process control.
- 00:16 Now we have an entire course on statistical process control and
- 00:19 the creation of control charts.
- 00:21 So in this module I wanna focus more on how to use those
- 00:25 control charts in your process.
- 00:28 I'll start with a review of process control and process performance.
- 00:33 There are four states of process performance and control.
- 00:37 These four states are a combination of the conditions of a process that is in
- 00:41 statistical control or not.
- 00:43 And whether the process currently is delivering 100% good parts, or
- 00:47 completing some portion of the output in an out of specification condition
- 00:51 resulting in scrap or rework.
- 00:52 Let's look at the implication of these four conditions.
- 00:56 When the process is in statistical control and delivering 100% good results,
- 01:01 it is operating in an ideal state, the results are good, and
- 01:04 thanks to the process control, they're likely to stay good.
- 01:07 Next is the case where the process is in statistical control, but
- 01:11 it's creating some process results that are outside the customer's specification.
- 01:15 This is known as threshold performance,
- 01:18 because it is in statistical control process output is predictable.
- 01:22 The organization can decide whether the cost of the defective output is such that
- 01:26 a project to reduce variation would be worthwhile.
- 01:29 The question is, what are the costs and performance thresholds?
- 01:33 The answer will determine what should be done.
- 01:36 The next condition is a process that is not in statistical control and
- 01:39 is producing some defective outputs.
- 01:42 This will lead to high costs, delays, and often chaos.
- 01:46 The only good news here is that everyone knows there is a problem.
- 01:50 You probably have inspection and testing in place to screen for good and
- 01:53 bad product, and you can probably get support to improve things.
- 01:57 The final case is when the process is not in statistical control, but
- 02:01 it's not currently producing any defective units.
- 02:04 This will often lead to a false sense of security.
- 02:07 The output is good from a customer perspective, but
- 02:10 the problem is that the process is unpredictable.
- 02:14 While it's good today, it may be much different and out of control by tomorrow.
- 02:18 Then everyone will be angry and
- 02:20 questioning, what happened, how did you lose the recipe?
- 02:23 Well, the facts were that you never had the recipe to begin with,
- 02:27 you were just lucky.
- 02:29 So let's take a moment and look at process control, control charting, and
- 02:32 control limits.
- 02:34 Recall that a process is in statistical control when
- 02:37 there are no special cause issues present.
- 02:39 The process is stable with a predictable mean, and predictable standard deviation.
- 02:44 A control chart is designed so as to discriminate between special cause and
- 02:49 common cause variation within the process.
- 02:51 The mean value and control limits are used to help the process operators and
- 02:55 managers know when a special cause is injected into the process.
- 02:59 If the process outputs are between the control limits,
- 03:02 the process is normally operating under control.
- 03:05 One thing to keep in mind, control limits are not spec limits,
- 03:09 the specification may be wider or narrower than the control limits.
- 03:14 Also the customer target and spec limits may even be above or
- 03:17 below the process mean and control limits.
- 03:20 In fact, let's look at the anatomy of a control chart, and what that means for
- 03:24 managing the process.
- 03:26 The horizontal axis is time, and the units or
- 03:29 data points are a sequential plot of the process output.
- 03:33 The plot includes the mean, or in some cases median, and the control limits,
- 03:37 which are the upper and lower bounds for the data, when the process is in control.
- 03:42 These limits and the mean are used to determine whether there is
- 03:45 only common cause variation present, or if there is special cause.
- 03:49 We discussed how to calculate these in the statistical process control course.
- 03:53 You can see we are showing here two special causes.
- 03:56 One is a value above the upper control limit.
- 03:58 The other is more than eight consecutive points below the mean,
- 04:02 which indicates that the mean has shifted.
- 04:05 While these charts reflect the voice of the process, and
- 04:08 what we can expect from the process, it's easy to compare these
- 04:11 points to the customer requirements so that we know which of the four states of
- 04:15 the quadrant I discussed in the first slide apply to our condition.
- 04:19 Finally, let's talk about the strategies for
- 04:22 using control charts in a Lean Six Sigma project.
- 04:25 For starters let me say that we use these in several stages of the Lean Six Sigma
- 04:30 project.
- 04:30 We often think of these in the control stage as a way to maintain the improve
- 04:34 status, but we can use them earlier if we have the data.
- 04:39 We can use them in the measure stage to establish base line performance
- 04:42 as part of our as is process,
- 04:45 that will also indicate which quadrant we are currently operating in.
- 04:48 They can be very helpful during the analysis stage to help isolate any
- 04:52 problems, and can provide insight into the possibility
- 04:55 of a process output that is really a combination of several processes.
- 05:00 We can also use them in the improve stage to demonstrate the impact of
- 05:04 the improvement, by comparing the improve stage control
- 05:07 charts to the baseline control charts created during the measure stage.
- 05:11 One final comment about the use of control charts in the control stage.
- 05:15 Control charts aren't needed for every parameter on every process.
- 05:18 Use them wisely, but use them sparingly.
- 05:22 A proliferation of control charts on every possible measure
- 05:25 just leads to people becoming numb to them.
- 05:28 Decide what are the key control parameters for your process, your design of
- 05:32 experiments could help with that, and just put control charts on those items.
- 05:37 Control charts, when well understood and applied to appropriate measures,
- 05:41 will help you get your process into the ideal state and keep it there.
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