Run Chart

Quick reference

• 00:05 Hi, I'm Ray Sheen.
• 00:06 Let's take a few minutes to talk about run charts.
• 00:09 This basic statistical tool is helpful for both problem-solving and process control.
• 00:16 So what is a run chart?
• 00:18 It is a display of the process performance over time.
• 00:21 We can take any key output parameter and plot it on a run chart.
• 00:25 A run chart is a special case of the line graph.
• 00:28 The vertical scale is based upon the output parameter values.
• 00:32 And the horizontal scale is a time increment or
• 00:35 unit number of process output.
• 00:38 Since it is a line graph, each data point is connected with a line to the points
• 00:42 immediately preceding and following that point in the sequence.
• 00:46 One thing that can be clearly seen from a run chart is trends.
• 00:50 Is the output parameter getting larger or smaller over time?
• 00:54 Another element of most run charts is a horizontal line that is drawn at the value
• 00:59 of the median.
• 01:00 The median is used for several reasons.
• 01:02 First, it is not significantly influenced by outliers.
• 01:05 Second, we don't know if we have a normal distribution.
• 01:09 Recall that the mean is used with the normal distribution and
• 01:12 the median is used with non-normal distributions.
• 01:15 If the distribution is normal, the median and mean will be virtually identical.
• 01:20 So we don't lose anything by using the median, and
• 01:23 we are protected against non-normal data.
• 01:25 The value of the run chart is that it is the voice of the process.
• 01:30 Now, earlier, we discussed the voice of the customer.
• 01:32 Now, we're talking about the voice of the process.
• 01:36 Let's take a minute to discuss how to create and use a run chart.
• 01:40 They are incredibly easy to create.
• 01:43 Just take a measure of the output, and
• 01:44 record it on the chart at the next point in the sequence.
• 01:48 One of the ways we use the chart is to provide a picture of
• 01:52 what is happening in the process for our stakeholders and our team members.
• 01:56 The run chart lets them visualize what is happening.
• 01:59 Now, I mentioned that the run chart uses the median value.
• 02:01 When you first start to plot the process output, there is no median.
• 02:06 Don't try to determine that until you have at least 15 data points.
• 02:09 And then you can periodically update the median over time.
• 02:12 There are several uses of the run chart information and the insight it provides.
• 02:18 Of course, an obvious one is that the run chart shows when a shift has occurred in
• 02:21 process performance.
• 02:23 This can be useful both in problem-solving and
• 02:26 by helping to identify when something negative occurred.
• 02:29 And it can be an excellent way to demonstrate the impact
• 02:33 of a process improvement, as it illustrates the before and
• 02:36 after condition of the process parameter.
• 02:39 In addition, there are five rules that we can use to determine if
• 02:42 there are special causes present in the process.
• 02:45 Recall that we want to eliminate all special cause variation so
• 02:49 that we only have common cause variation and, therefore,
• 02:52 our process is stable and predictable.
• 02:54 Let's look at each of the special cause rules than can be observed on a run chart.
• 02:59 The first is nine consecutive points on the same side of the median.
• 03:03 This demonstrates a shift in performance.
• 03:05 If this was not a planned shift, then something special has happened.
• 03:09 This shift could be good or bad from an overall process performance perspective.
• 03:13 That is why we need to determine the cause.
• 03:15 The second is an astronomical point.
• 03:19 This means a point that is an outlier.
• 03:21 It is far removed from the typical values of the process parameter.
• 03:25 How far off?
• 03:25 Well, right now that's a judgement call.
• 03:28 You don't have a standard deviation or
• 03:30 control limits to help you like you will in a control chart.
• 03:33 So we leave that to your judgement.
• 03:35 Third, is a trend.
• 03:37 This is when there are at least six consecutive points
• 03:40 that are changing in the same direction as compared to the preceding point.
• 03:44 These don't need to be above or below the median.
• 03:46 They just all need to be going in the same direction.
• 03:49 This indicates that there is a new effect impacting the process parameter.
• 03:54 Fourth is the saw tooth pattern.
• 03:56 In this case, there are at least 14 points that alternate, going higher or lower,
• 04:00 than the preceding data point.
• 04:02 This effect is often due to some external effect attempting to overcontrol
• 04:06 the process, rather than the normal process performance occurring.
• 04:11 A fifth way of identifying special causes is called consecutive runs.
• 04:15 It's tricky one, and is based upon two many or too few runs in the data.
• 04:20 Of course, you're probably asking what's a run?
• 04:21 A run is one or
• 04:23 more consecutive, useful observations that are on the same side of the median.
• 04:28 And the definition of a useful observation is that at any value that is not
• 04:32 equal to the median, based upon the total number of useful observations,
• 04:37 there's a minimum and maximum number of runs that are expected.
• 04:40 Too few or
• 04:41 too many indicates a special cause is overriding the process performance.
• 04:46 So let's take a look at this run chart.
• 04:48 There are 27 data points, but four of them are on the median.
• 04:52 So there are only 23 useful observations.
• 04:55 The first point is on the median.
• 04:57 The next two are below, so that constitutes a run.
• 05:00 Then there are three points above the median, which constitutes another run.
• 05:04 That is followed by a point on the median, and
• 05:07 then three more points below the median for the third run.
• 05:10 There are then four points above the median for
• 05:13 run number four before we have another point on the median.
• 05:17 Then three points below, four points above, four points below for runs five,
• 05:21 six, and seven.
• 05:22 Finally, a point back on the median.
• 05:25 This is a total of seven runs.
• 05:27 When we go to the table at the right, we find that for 23 useful observations,
• 05:32 we should have a minimum of eight runs and a maximum of 16 runs.
• 05:36 Since we are below the minimum,
• 05:38 we know that some special cause is overriding the normal process performance.
• 05:45 The run chart is one of the easiest and most practical data gathering and
• 05:49 data analysis techniques that's available to a Lean Six Sigma team.

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