Statistical Process Control
Statistical process control is the application of statistical methods to monitor and control the quality of a production process. Statistical process control is appropriate to support any repetitive process, including financial auditing and accounting, IT operations, health care processes, and clerical processes such as loan arrangement and administration, customer billing etc.
Walter A. Shewhart pioneered SPC at Bell Laboratories in the early 1920s, developed the control chart in 1924.
W. Edwards Deming invited Shewhart to speak at the Graduate School of the U.S. Department of Agriculture and served as the editor of Shewhart’s book Statistical Method from the Viewpoint of Quality Control (1939), which was the result of that lecture.
Process Behavior Charts (Shewhart individuals control chart)
Use
 Find out when a change has occurred in a process
 Evaluate if your process changes made any significant difference to the process
 Determine whether a process is predictable or not
 In predictable processes, predict the range within further measurements will fall if the conditions do not change
 Assess a process performance against a requirement e.g. customer demand and expectations.
References
 Deming Alliance: Process Behaviour Charts  An Introduction
 2013 Donald J. Wheeler: When the XmR Chart Doesn’t Seem to Work
 2023 Commoncog: How to become data driven
 2023 Commoncog: Process Behavior Charts
 Shewhart individuals control chart
Calculations
 Some value with ideally over 23 data points
 calculate average(X)
 calculate mR
 calculate average(mR)
 calculate UNPL = average(x) + (2.66 * average(mR))
 calculate LNPL = average(x)  (2.66 * average(mR))
 calculate URL = 3.269 * average(mR)
Terms
 mR – moving range, the difference between two successive values
 UNPL – Upper Natural Process Limit, gives us the best case scenario forecast
 LNPL – Lower Natural Process Limit, gives us the worst case scenario forecast
 URL – Upper Range Limit
Interpreting

Any point outside the limits indicates the presence of an exceptional cause that has a dominant effect.
 If not accounted by cyclical or seasonal patterns
 Run of eight successive values all on the same side of the central line may indicate the presence of an exceptional cause that has a weak but sustained effect.
 Runs near the limits three successive values all within 25% of the upper or lower limits may indicate the presence of an exceptional cause that has a moderate but sustained effect.
 Alternating at short intervals likely means two systems of causes that need to be separated.
 Wide limits are a noise warning that it would be a mistake to react to changes in these values as it is virtually impossible to assign an explanation to why a point goes up or why it goes down.
In a stable process, most of values fall within the 50% area between the limits.