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This information is brought to you by Resource Engineering, Inc. developers of SPC Workout, computer-based training on the statistical process control and process capability.

What is Statistical Process Control?

Statistical Process Control (SPC) is a tool for monitoring and controlling manufacturing processes.  Dr. W. Edwards Deming claimed that the majority of variation in a process is due to operator over-adjustment.  SPC gives operators a tool to determine when a statistically significant change has taken place in the process or when an seemingly significant change is just due to chance causes.  SPC involves:

• Determining the critical process parameters that need to be monitored
• Setting up an initial control chart and confirming that the process is in-control, and 
• Collecting and plotting future data on the chart and interpreting the chart to determine if the process has gone out-of-control.

Why do companies use SPC?

How can SPC help companies improve quality and productivity?

How does SPC work?

What are some mistakes companies make when they use SPC?

How can my company get started using SPC?

What is the best way to teach people how to use SPC?

Why do companies use SPC?

There are a number of reasons why companies use SPC.  Often an internal champion initiates the use of control charts and other SPC techniques to reduce variation and to improve manufacturing processes.  Sometimes companies implement SPC to satisfy customer requirements or to meet certification requirements.  

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How can SPC help companies improve quality and productivity?

SPC itself will not make improvements.  Rather, SPC will give operating personnel a tool to identify when a special cause of variation has entered the process so that the special cause can be eliminated (if the special cause has a negative impact on the process) or built into the process (if the special cause has a positive impact on the process).  With this tool, constant tweaking of the process is eliminated.  In addition, SPC can be helpful in identifying opportunities for improvement that can lead to reduced variation and processes that are better aimed at their target.

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How does SPC work?

The key tool of SPC is a control chart.  While there are control charts for attribute data (data that must be counted, for example, in terms of number of defective items) and variable data (data that is take from a variable scale such as length, width, height), variable data control charts provide more valuable information and should be used wherever practical.  Variable data control charts typically monitor the process target or mean and the process variation or range.  There are a number of different types of variable data control charts but the most common chart is the x-bar and R chart.  

A control chart has a centerline, an upper control limit and a lower control limit.  The centerline for the x-bar chart is the process mean and the centerline for the R chart is the mean range.  The control limits are set to represent plus and minus 3 standard deviations from the mean or where 99.7% of all data points should fall.  Data is then collected from the process, typically in subgroups of 3 to 5 and the subgroup mean and range is plotted on the x-bar and R charts respectively.  Once a point is plotted, the chart is interpreted to determine if the process is staying in-control or if the process is out-of-control.

There are many different rules to select from and then follow when interpreting control charts.  All of the rules are based on statistical probabilities of the pattern occurring due to random, common cause variation.  The patterns a company uses depends on the variability of the process, the criticality of the process, and customer requirements.  The most common patterns to watch out for are:  One point outside of the control limits, eight points in a row on either side of the centerline, eight points in a row trending in the same direction, and cycles or recurring trends.

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What are some mistakes companies make when they use SPC?

Here are the top reasons why SPC does not work:

1. Putting spec limits on control charts. 

2. Using control charts only to satisfy customer needs. 

3. Plotting data for a control chart in the QA lab, after the process has already been run.  It is like driving your car using your rearview mirror. 

4. Using the wrong type of control chart for the process resulting in false signals or muted signals. 

5. Not reviewing control charts and how they are used on the shop floor with operators on a regular basis.

6. Thinking that if you use a computer program that generates control charts that you don't need to teach operators how to use SPC.

7. Not first conducting a process capability study. 

8. Not taking random samples from the process or not using a sampling frequency or sample size that captures the variation in the process.

9. SPC in used to control product characteristics after a part is manufactured and the defect has been made rather than monitoring key process parameters that affect whether or not a defect is made.  That's why it is called statistical PROCESS control and not statistical PRODUCT control.