Control charts, ushered in by Walter Shewhart in 1928, continue to provide real-time benefits in today’s most modern factories. When first introduced, there were seven basic types of control charts, divided into two categories: variable and attribute.
Variable Data Charts
- IX-MR (individual X and moving range)
- Xbar-R (averages and ranges)
- Xbar-s (averages and sample standard deviation)
Attribute Data Charts
- p (proportion defective for subgroup sizes that vary)
- np (number of defectives in a fixed subgroup size)
- u (defects per unit for subgroup sizes that vary)
- c (defect counts in a fixed subgroup size)
For those that make control charts their business know that there have been significant contributions to the chart offerings since the original seven were introduced; in fact, there are now 100’s of control charts to choose from. Options that perfectly model a process’ statistical personality can be realized as long as the right control chart is selected.
So, that brings up the reason for why I’m writing this blog – how do you pick the best control chart for your specific situation? The answer is rooted in knowing the factors that contribute to defining the chart type. But before we get into the details of chart type combinations let’s define, at a high level, what control charts are and what they are not.
Control charts ARE:
- REAL-TIME graphical process feedback tools
- Designed to tell the operator to do SOMETHING or do NOTHING
- Time-ordered representation of process PERSONALITIES or BEHAVIORS
- Designed to SEPARATE signals from NOISE
- Detect changes in either the process mean and/or standard deviation
- Used to determine if a process is STABLE (predictable) or OUT-OF-CONTROL (not predictable)
Control charts are NOT:
- They are not a substituted for capability analysis
- They are not useful in receiving inspection (time order is lost)
- They are very inefficient comparative analysis tools
- They are not to be confused with Run charts or PRE-Control charts a)Run charts are time-ordered, but no statistical-based limits b)PRE-Control charts are comparing plot points to specification limits
Control charts utilize limits to help identify when the process has significantly change or to isolate an unusual event. Because control limits are derived from the data, one cannot know them until after a representative series of data have been collected. If used for the wrong reasons, control limits can cause confusion and counterproductive actions by those asked to use the charts to monitor and improve their processes.
Control limits ARE:
- Limits based on EXPECTED plot point variation
- Calculated from MEAN and STANDARD DEVIATION (derived after representative plot points have been gathered)
- Typically expressed as +/- 3 standard deviations of the plot points (not the standard deviation of the underlying distribution)
- Limits should be updated when a process improvement has been verified
Control limits are NOT:
- Based on a percentage of the specification limits
- 75% of the specification limits
- Production limits
- Anything to do with specification limits or desired limits
In my next blog post, I will be discussing sampling considerations such as sample size and number of process streams. Control Charts: Which One Should I Use Part II.