# Use of Control Charts for Monitoring KPIs with Target Percentages

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This topic contains 6 replies, has 7 voices, and was last updated by Chris Seider 1 week, 3 days ago.

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- October 23, 2019 at 12:12 pm #243136

datasciencedweebParticipant@datasciencedweeb**Include @datasciencedweeb in your post and this person will**

be notified via email.Hi there,

I’m wondering is a control chart the appropriate tool to use to monitor KPIs where the data is a percentage, and the target is 100%?

Since the target is 100%, and a lot of the time that target is hit, the upper control limits are expanding beyond the 100% limit. Should the solution here be to remove upper control limits and only include lower?

The main goal of using the control chart is to identify a trend. Even though the target is 100%, realistically that target will not be met week after week. Therefore there will be expected variation from the 100% target. So I want to use a control chart and trend analysis to monitor at what point is our performance going beyond the expected variation, and more towards a signal that we are deviating too far from our target.

- This topic was modified 2 weeks, 5 days ago by datasciencedweeb.
- This topic was modified 2 weeks, 5 days ago by Katie Barry.

0October 23, 2019 at 3:04 pm #243145

Mike CarnellParticipant@Mike-Carnell**Include @Mike-Carnell in your post and this person will**

be notified via email.@datasciencedweeb If you are using a software package check it to see if you can list 100% as a boundary.

0October 25, 2019 at 10:26 am #243165

Nicholas IaconoParticipant@nai102**Include @nai102 in your post and this person will**

be notified via email.To your first question, yes it is appropriate to use a control chart to monitor KPIs.

Since you are monitoring a percentage, which control chart to use will depend on how that percentage is calculated (percentages as a metric should throw a yellow flag that you need to do further investigation into that calculation). For example, if the base data behind the percentage is pass/fail, then you’ll be looking at attribute control charts like a p-chart or np-chart. Minitab (if that’s your software) has also added the Laney p’-chart in case your pass/fail data doesn’t fit a binomial distribution (and a p-chart could give you false indications)

0October 28, 2019 at 8:43 am #243204I typically use a Time Series Plot with a reference line for the goal in Minitab. Since you’re just looking at performance trends you don’t really need control limits You could also calculate the standard deviation and place a LCL at -3 standard deviations with another reference line.

0October 28, 2019 at 10:24 am #243208

Anonymous@**Include @ in your post and this person will**

be notified via email.I concur with Nicholas that if the percentages are calculated from discrete data, say defectives then you should be using the discrete data (number of defectives and sample size) to calculate the control limits. In this case when the number of defectives are 0, this corresponds to your 100% case. The control limits for this chart will never exceed 100%. The Laney P’ chart is typically used when your sample sizes are very large.

[EDITED BY MODERATOR]

0November 1, 2019 at 9:17 am #243271

Jess L CottenParticipant@lamonte14**Include @lamonte14 in your post and this person will**

be notified via email.The control charts are good, but you may want to investigate each point of the control limits variance for the true root cause. This may take you in a different direction in regard to the measure.

0November 1, 2019 at 7:29 pm #243282

Chris SeiderParticipant@cseider**Include @cseider in your post and this person will**

be notified via email.Consider what you mean by target of 100%.

Your control chart would be great if no losses were assumed and the theoretical output was included–not planned output.

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