Control Chart

Control Charts are not as frequently used and are used most often with tracking production quality.

History

The control chart was invented by Walter A. Shewhart while working for Bell Laboratories in the 1920s. The company's engineers had been seeking to improve the reliability of their telephony transmission systems. Because amplifiers and other equipment had to be buried underground, there was a business need to reduce the frequency of failures and repairs. By 1920 the engineers had already realized the importance of reducing variation in a manufacturing process. Moreover, they had realized that continual process-adjustment in reaction to non-conformance actually increased variation and degraded quality. Shewhart framed the problem in terms of Common- and Special-causes of variation and, on May 16, 1924, wrote an internal memo introducing the Control Chart as a tool for distinguishing between the two. 

Shewhart stressed that bringing a production process into a state of statistical control, where there is only common-cause variation, and keeping it in control, is necessary to predict future output and to manage a process economically.

A basic Control Chart will look like the sample below. The longer the chart is used the more predictive you can be about its performance if there are no new influencers.

Why should a Control Chart be used?

The purpose of a Process Control Chart is to evaluate the output or activity of a process and determine if the process is in control. ‘In control’ means that the current data is an accurate source of future predictions. ‘Out of control’ indicates that there is an error in the current process that needs correction.

All processes have some form of variation. A Control Chart helps you distinguish between normal and unusual variation in a process. If you want to reduce the amount of variation in a process, you need to compare the results of the process with a standard.

The control chart is the fundamental tool of SPC (Statistical Process Control), as it indicates the range of variability that is built into a system (known as common cause variation). Thus, it helps determine whether a process is operating consistently or if a special cause has occurred to change the process mean or variance.

The bounds of the control chart are marked by upper (UCL) and lower control limits (LCL) that are calculated by applying statistical formulas to data from the process. Data points that fall outside these bounds represent variations due to special causes, which can typically be found and eliminated. On the other hand, improvements in common cause variation require fundamental changes in the process.

Variation can exist for two reasons:

1. Common Causes are flaws inherent in the design of the process.
2. Special Causes are variations from standards caused by employees or by unusual materials, circumstances, or events.

NOTE: Most variations in processes are caused by flaws in the system or the process, not by the employees. Once you realize this, you can stop blaming the employees and start changing the systems and processes that cause the employees to make mistakes.

It is important to remember, however, that some variations are not "mistakes" introduced by employees, but, rather, they are innovations. Some variations are deliberately introduced to processes by employees specifically because these variations are found to be more practical. Example three at the end of this document shows how improvements lead to a change in the UCL, CL, and LCL values.

Advantages of Using a Control Chart

As with most QC Tools, the benefit is generally two-fold. First, as we study the information we begin to understand the information better. Second, it brings the operation data into a visual form. Visibility of statistics alone will often generate improvements.

• Makes data visual
• Provides information when change is needed
• Information enables tightening of variations over time

General Information

Data should be distributed revolving around a mean (average). Measurements need to be independent of one another. In the example, the measurements are in subgroups. Each data point will have a subgroup and a measurement number.

If a single quality characteristic has been measured or computed from a sample, the control chart shows the value of the quality characteristic versus the sample number over time. In general, the chart contains a center line that represents the mean value for the in-control process CL (Center Line). Two other horizontal lines called the Upper Control Limit (UCL) and the Lower Control Limit (LCL), are also shown on the chart. These control limits are chosen so that almost all of the data points will fall within these limits as long as the process remains in control. (See Example 1 below, or zone chart following this for the indicated Means (CL), UCL, and LCL lines.

Example 1 – Is this Process In Control?  Why?

There are three reasons on the chart that makes the process Out of Control. The problems are circled and from left to right are numbers 4, 5, and 4 in the list reasons process is Out of Control on the previous page.

Example 2 – Unstable Condition

YouTube How to create a Control Chart using QI Macros (a plug-in for Excel):

http://www.qimacros.com/spc-charts/control-chart-excel.php?gclid=CKKz34aP2KYCFYa7KgodtnQVHw

Read more: How to Create a Statistical Process Control Chart | eHow.com http://www.ehow.com/how_2248596_create-statistical-process-control-chart.html#ixzz1C9Y67EFF