NCK Consulting

Statistical process control (SPC) procedures can help you monitor process behavior.Arguably the most successful SPC tool is the control chart, originally developed by Walter Shewhart in the early 1920s. A control chart helps you record data and lets you see when an unusual event, e.g., a very high or low observation compared with “typical” process performance, occurs.

Control charts attempt to distinguish between two types of process variation:

• Common cause variation, which is intrinsic to the process and will always be present.
• Special cause variation, which stems from external sources and indicates that the process is out of statistical control.

Various tests can help determine when an out-of-control event has occurred. However, as more tests are employed, the probability of a false alarm also increases.

Background

A marked increase in the use of control charts occurred during World War II in the United States to ensure the quality of munitions and other strategically important products. The use of SPC diminished somewhat after the war, though was subsequently taken up with great effect in Japan and continues to the present day.

Many SPC techniques have been “rediscovered” by American firms in recent years, especially as a component of quality improvement initiatives like Six Sigma. The widespread use of control charting procedures has been greatly assisted by statistical software packages and ever-more sophisticated data collection systems.

Over time, other process-monitoring tools have been developed, including:

• Cumulative Sum (CUSUM) charts: the ordinate of each plotted point represents the algebraic sum of the previous ordinate and the most recent deviations from the target.
• Exponentially Weighted Moving Average (EWMA) charts: each chart point represents the weighted average of current and all previous subgroup values, giving more weight to recent process history and decreasing weights for older data. 

More recently, others have advocated integrating SPC with Engineering Process Control (EPC) tools, which regularly change process inputs to improve performance.

Gage R&R (Gage Repeatability and Reproducibility) is the amount of measurement variation introduced by a measurement system, which consists of the measuring instrument itself and the individuals using the instrument. A Gage R&R study is a critical step in manufacturing Six Sigma projects, and it quantifies three things:

• Repeatability – variation from the measurement instrument
• Reproducibility – variation from the individuals using the instrument
• Overall Gage R&R, which is the combined effect of (1) and (2)

The overall Gage R&R is normally expressed as a percentage of the tolerance for the CTQ being studied, and a value of 20% Gage R&R or less is considered acceptable in most cases. Example: for a 4.20mm to 4.22mm specification (0.02 total tolerance) on a shaft diameter, an acceptable Gage R&R value would be 20 percent of 0.02mm (0.004mm) or less.

The Difference Between Gage R&R and Accuracy

A Gage R&R study quantifies the inherent variation in the measurement system (the combination of items 1 and 2 noted above), but measurement system accuracy must be verified through a calibration process.  For example, when reading an outdoor thermometer, we might find a total Gage R&R of five degrees, meaning that we will observe up to five degrees of temperature variation, independent of the actual temperature at a given time.  However, the thermometer itself might also be calibrated ten degrees to the low side, meaning that, on average, the thermometer will read ten degrees below the actual temperature.  The effects of poor accuracy and a high Gage R&R can render a measurement system useless if not addressed.

Measurement system variation is often a major contributor to the observed process variation, and in some cases it is found to be the number-one contributor. Remember, Six Sigma is all about reducing variation.

Think about the possible outcomes if a high-variation measurement system is not evaluated and corrected during the Measure phase of a DMAIC project – there is a good chance that the team will be mystified by the variation they encounter in the Analyze phase, as they search for variation causes outside the measurement system.

Measurement system variation is inherently built into the values we observe from a measuring instrument, and a high-variation measurement system can completely distort a process capability study (not to mention the effects of false accepts and false rejects from a quality perspective). The following graph shows how an otherwise capable process (Cpk = 2.0: this is a Six Sigma process) is portrayed as marginal or poor as the Gage R&R percentage increases:

What You Should Know About Failure Mode and Effects Analysis (FMEA)

All products or processes have failure modes. The effects are the impacts when the failures occur. A FMEA is a tool to:

• Identify the relative risks designed into a product or process
• Develop and take action to reduce the risks with the highest potential impact
• Track the results of the action to determine risk reduction or elimination

The Purpose of an FMEA

FMEAs help us focus on and understand the impact of potential process or product failures

A disciplined analysis is used to rank the risks relative to each other.

A Risk Priority Number, or RPN, is calculated for each failure mode and its resulting effect(s).

The RPN is a function of three factors: The Severity of the effect, the frequency of Occurrence of the cause of the failure, and the ability to Detect the failure or effect.

 The RPN = The Severity ranking X the Occurrence ranking X the Detection ranking.
 The RPN can range from a low of 1 to a high of 1,000.

Develop a Corrective or Preventive Action Plan to reduce risks with unacceptable high RPNs.

Use FMEAs as the basis for Control Plans. Control Plans are a summary of proactive defect prevention and reactive detection techniques. Control Plans must mirror the FMEA.