Sometimes a picture is worth a thousand words. I have four pictures – and I’ll still need a few words to explain them. Hang on for the ride…

In the last two posts I’ve been talking about why Short Run SPC (Statistical Process Control) has been on the increase, and I’ve described the three most common Short Run Coding methods. This post shows a simple example of a Target / Nominal control chart.

First some background: We’re tracking a cut-off process. For this process, the operator resets a target value. The company makes several sizes of these parts, and any one part (length) may run for a few hours or several days. Depending on customer demand, a part that runs today may not run again for a week, or several months, or even longer.

If we keep data for two of these parts on separate charts here is what they look like:

(Click on either chart to open it full size, in a new window.)

If you use traditional SPC, you have to put these parts on separate charts because the scaling is all goofed up. Notice how the target for parts on the first chart is about half an inch while the second is about three-quarters?

What isn’t as easy to see is how these charts disguise the gap in time between process runs. If we put the second chart on a time-series graph this becomes a little more obvious because you can see at least three separate runs for this product. Notice the way the points are clustered on the chart.

So there are at least two problems with traditional part-based SPC charts in this type of environment: You can’t see the gaps in time in subsequent runs for each part, and you can’t see what was happening to the other parts during those gaps in time. That’s the problem with focusing on Product instead of Process.

The Short Run Control Chart takes care of those problems by coding the expected variation (in this case the target) out of the data and plotting data for all the parts on one chart.

On this chart, I’ve shaded the data for the first chart in red and the second part in green so you can see how they interact on the Short Run Chart.

The first goal of Statistical Process Control is to detect signals of process instability. Do you see how that works in this example? If you look closely at the point shaded in green at the right side of this chart you see that it is actually the 7th point of a seven point run above the mean. The traditional SPC chart (the last point on the second chart) at the top of the page misses this signal completely.

Do you see any other signals in the Short Run chart that the traditional chart misses? Use the ShareThis button below to mark this page, or leave a comment, tweet me, schedule a conversation, or call 800-958-2709.