Avoiding the Pain of Out-of-Specification Results - Failing to apply important statistical principles leads to unnecessary investigations and wastes time and resources. Control charts can help. - Bio
Avoiding the Pain of Out-of-Specification Results
Failing to apply important statistical principles leads to unnecessary investigations and wastes time and resources. Control charts can help.
 Jun 1, 2008 BioPharm International Volume 21, Issue 6

A BIOLOGIC EXAMPLE

 Figure 2. Two views of chemical cleave data: batch release charts set at three sigma and using Shewhart limits. The top chart is a run chart with specifications set at plus or minus three sigma. The bottom charts are the same data with Shewhart control limits set and process shifts incorporated (moving range chart is omitted from the Shewhart chart).
Figure 2 shows data from a chemical cleave step in a biologic process.2 The top chart shows the data with limits set at plus or minus three standard deviations. No points are observed beyond these 3 σ limits. Assuming the data are reasonably continuous, less than 1% of any data set can be expected to fall beyond 3 σ limits, regardless of how unstable the system may be, and irrespective of the functional form of the distribution involved. This is a central problem associated with the use of 3 σ limits. As the process is upset by special causes of variation or by drifts in the process mean, the 3 σ limits will grow without bound, making the process under examination look stable even when it is not.3

The lower charts in Figure 2 show control limits calculated and placed according to Shewhart's methods, and much more information is available. It is important to note that in an individual point and moving range control chart, all the control limits are developed from the average of the moving ranges, rather than from the individual data themselves. It is the special relationship between the moving ranges chart and the control limits that allow control limits to be developed that are not significantly widened by upsets to the system, as so often happens with 3 σ limits.2 The estimate for σ using Shewhart's methods is nearly half of that calculated by the more usual root mean square deviation function found in calculators and spreadsheets.

The process in Figure 2 is unstable. It is not behaving in a predictable manner and sooner or later will record an OOS event. The chart created using Shewhart's methods exposes several special causes and changes to the process mean. These signals provide clues for detecting changes to both manufacturing and analytical processes in an attempt to reduce variation and improve quality and productivity. In this case, the Shewhart chart has identified where something changed the process average, and where upsets have introduced special cause events. Investigating these signals will lead to a better understanding of the process, and to proper identification of causality as well as the appropriate corrective action.

The more traditional "average plus or minus three sigma" chart found no signals in the data. The Shewhart control chart found seven.

The approach pioneered by Shewhart suggests that key variables be tracked in real time on a control chart.3 In this way, disturbances or changes to the process can be identified and corrected, often before an OOS result is recorded.