Control Charts

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This post will briefly explain and answer “What is a Control Chart?” and also quickly explain “How to Create a Control Chart?” through a video tutorial of a Control Chart.

First off: entire books and PhD dissertations are written about Control Charts – this short post won’t do it justice. So, please learn on your own what I will most likely not cover in this article.

Every process varies. There is an inherent variation, but it varies between predictable limits. There are two types of variation: “common cause” and “special cause”. If you are cutting diamonds, and someone bumps your elbow, the special cause can be expensive. But, in diamond cutting and no elbow was bumped, the process itself will inherently have variation – that is called common cause.

For many processes, it is important to notice special causes of variation as soon as they occur and appropriately respond.

All control charts have three basic components:

• A centerline, usually the mathematical average of all the samples plotted.
• Upper and lower statistical control limits that define the constraints of common cause variations.
• Performance data plotted over time.

Here is an example of a control chart:

Here is a short video tutorial on how to create a Control Chart:

	This post was written by Pete Abilla