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Control Charting Issues Can Lead to Firefighting In {Business~Control Charting Performance Measures~X-bar and R Control Charting Issues That Can Lead to Firefighting~Firefighting In Business: Control Charting Issues May Be The Problem}

Organizations can have many problems because they are measuring the wrong things, which can lead to unbeneficial or detrimental behaviors.  Almost every organization’s measurement and improvement systems need to be improved.  These organizations should avoid red-yellow-green balanced scorecard measures, which often lead to distruptive firefighting that is a detriment to the enterprise as a whole.

To create a valid process capability statement data needs to be from a stable or “in control” process.  We should also strive to have a sampling process so that if more than one person examines the same process their conclusions should be similar whether 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 easier to understand 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.  Probability plots can also be very useful when making a process capability/performance metric statement even if there is no specification.  To get organizations out of the firefighting mode, probability plotting is not the most important issue relative to creating a no nonsense balanced scorecard measures system.

Corporate objectives are achieved in the Integrated Enterprise Excellence (IEE) measurement system.  In this IEE methodology, there can become a measurement pull for the creation of projects when a predictive metric does not yield a desirable response for the enterprise as a whole.  To achieve this goal, a measurement system should be developed which is independent upon how a person creates a sample collection system.

The overall view of customer experience should be the primary purpose of a 30,000-foot-level balanced scorecard measures (control charting) system. 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.  Typically conveyed in Lean Six Sigma classes but not often occurring in the “real world”, this is in contrast to the timely identification of an issue using a control chart to stop a manufacturing line for problem resolution because of an out of control signal.

For your consideration: a hypothetical situation where process raw material is changed from day to day.  Also, consider that some raw material characteristic does affect the process’ output.  Would raw material be considered as a potential common cause variability source or should it be considered a special cause variability source?

Raw material should be considered a source of common cause variability in most opinions.  If we all agree 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?  Given our current belief system the fundamentals underlying the X-Bar and R chart may seem inconsistant. X-bar and R chart control limits are only a function of within subgroup variability. When it comes to 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 control chart upper and lower control chart limits might consider the variability between raw material lots; but 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.  Blindly following X-bar and R charts can lead to a lot of fire fighting.   X-bar and R charts are not used when making an IEE 30,000-foot-level assessment.

Some may find it hard to believe that the x-bar and R chart they learned in their 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.

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