Organizations can have many problems because they are measuring the wrong things, which can lead to unbeneficial or detrimental behaviors.  Improvements are needed in many organization’s measurement and improvement systems.  Red-yellow-green balanced scorecard measures, should be avoided because they can lead to firefighting measures that do not benefit the overall enterprise.

Data needs to be from a stable process or “in control” process to create a valid process capability statement.  Let’s also strive to have a sampling process so that if more than one person reviews the same process their conclusions should be considered similar even if the process is considered “in control” or not.  With this statement, reference is made to how they sample from the process, not a chance occurrence.  “Predictable” is often a better term than “in control”. 

It is better to use ppm as a response for process capability and process performance indices, rather than Cp, Cpk, Pp, and Ppk, which can be very confusing and deceiving.  A better practice is to use a probability plot to describe process capability/performance, since a probability plot offers more output flexibility and data understanding potential than process capability analyses that also provided Cp, Cpk, Pp, and Ppk outputs.  When make a process capability/performance metric statement probability plots are also very useful, even though no specification exists.  Please note that probability plotting is not the most critical issue relating to the creation of a no-nonsense balanced scorecard measures system that keeps organizations out of the firefighting business.

With IEE (or Integrated Enterprise Excellence) measurement system when implemented from the top down can help you achieve your corporate objectives.  In the IEE methodology, there can become a measurement “pull” for the initiation of projects when a predictive metric does not produce a desirable improvement for the enterprise as a whole.  To make this happen, a measurement system is most be developed which is independent from how someone designed a sample collection system.

The primary purpose of a 30,000-foot-level control charting should be an overall view of customer experience. Assuming that there is consensus with this position, a couple questions need to be asked to determine if there is agreement as to what should be considered as a potential common cause and special cause input variable source in a 30,000-foot-level control chart.  Compare this to the timely identification of a problem using a control chart to halt a manufacturing line to fix the problem because of a signal that is out of control.

Let’s discuss a typical situation where process raw material is changed day by day.  Add to that you must consider that some raw material characteristic affects the output of the process.  Would raw material be considered as a potential common cause variability source or should it be considered a special cause variability source?

Most will agree that raw material should be considered a source of common cause variability.  If there is agreement on this and there is also agreement that control charting should provide information consistent with what we believe with respect to special and common cause identification, we will not use x-bar and R charts.  

Why is this?  With our current belief system, the fundamentals behind the creation of an X-bar and R chart can seem inconsistent. X-bar and R chart control limits are only a function of within subgroup variability. For x-bar and R control chart limits, variability between subgroups has no affect.   The control limit calculations of an individuals control chart provides control limits that are a function of the variability between subgroups.  The individuals control chart upper and lower control chart limits would consider the variability between raw material lots; however, this would not occur in an x-bar and R chart.

One could use x-bar and R charts to calculate process capability in statistical programs such as Minitab; however, this is not consistent with our belief system.  X-bar and R charts can force an enterprise into a lot of damage control.   X-bar and R charts are not used when making an IEE 30,000-foot-level assessment.

It can be tough for some to accept that the taught x-bar and R chart in a basic statistics class has problems.  Several 30,000-foot-level articles can be found in the “On-line Resource Library” link at www.SmarterSolutions.com, which provides more details and shows an example, not only for a continuous response output but other outputs as well.

The volume, Integrated Enterprise Excellence Volume III – Improvement Project Execution: A Management and Black Belt Guide for Going beyond Lean Six Sigma and the Balanced Scorecard describes on how to create 30,000-foot-level charts for various situations and much more. You can also visit www.ieeblackbelt.com for more information.