Statistical Process Control Explained

By Sean Stevenson – Latest Revision February 4th, 2021

What is Statistical Process Control?

Statistical process control (also known as SPC) is a measurement of quality assurance within a production process or method. 

SPC uses large samples of statistical monitoring to accurately gauge the overall quality of the products being produced.  This allows for a process to be actively monitored and improved upon over time.

Often, statistical process control can reveal important insights as to the inherent strengths and weaknesses of a productive process.  To this end, it can greatly assist in the development of solutions for any issues found.

In the case where a specification is required for a product, the use of statistical process control can help measure the quality of existing outputs. 

SPC would be commonly found in a manufacturing setting, to monitor production.

Understanding Statistical Process Control (SPC)

SPC has three distinct phases:

  • Phase 1 – The creation of a productive process, the specification of its desirable limits, and the establishing of its routine durations.
  • Phase 2 – The eliminating of special sources of variation.  These are variations which are not inherent to the production process, and so can be altered to the betterment of overall quality.  This inherently creates a more stable, economic, and repeatable process.
  • Phase 3 –This will be the time in which statistical process control is used to actively monitor the output of the productive process.  Control charts are often used to assist in the recording of important data herein.  Any detection of variations will be immediately addressed by altering the inputs.  Again, monitoring will ensue, and the resultant quality of product being produced will be recorded.

From the inference of the data collected from SPC, the outputs can be accurately measured in terms of quality assurance. 

Statistical Process Control Explained

A key advantage of statistical process control is that it greatly accelerates the detection of outstanding issues within the productive process.  This allows for preventive measures to be taken as soon as possible, which can save countless hours and significant amounts of capital.

Since the goal with SPC monitoring is to be proactive rather than reactive, it often prevents needless amounts of scrap or rework.  Moreover, the root causes of undesirable variations can be identified for future reference.

The Essential SPC Tool

Perhaps the most popular of all tools most associated directly with statistical process control, is the control chart.

Originally developed by Walter Shewhart in the early 1920s, the control chart is essential for effectively recording data using SPC.  It allows for deviations to be readily identified, along with the time and variance of their occurrence.

Statistical Process Control Explained

Depicted above, is a control chart.  Utilization of this format allows for easy dissemination of volumetric data over a predetermined period of time.

A key metric of control charts is their ability to differentiate between different variations within the process.  These variations are broken down into two distinct categories:

  1. The common cause variation – This variation is inherent to the underlying process. As a result, it cannot be removed.
  2. The special cause variation – This variation is an indication of interference from outside elements. Its existence would indicate that the process is compromised and is therefore a deviation from standard SPC.  It can be remedied by altering or amending the inherent process.

Further tests are often used to accurately assess when a special cause variation has occurred and why.  A prudent quality department will take special care not to employ too many tests, however.  As this can compromise the validity of findings and create false alarms.

The 7 Quality Control Tools

Dr. Kaoru Ishikawa specifically devised the “7-QC tools” in his Guide to Quality Control.  They became known in quality associations and academia around the world as effective methods of measuring productive output conformity.

The 7 quality control tools are:

  1. Cause-and-effect diagram
  2. Check Sheet
  3. Control chart
  4. Histogram
  5. Pareto chart
  6. Scatter diagram
  7. Stratification

Using these in tandem with statistical process control allows for a far greater understanding of the merits and dysfunctions of a particular production process.

7-SUPP Or 7 Supplemental Tools For Quality

The 7-QC tools are well-known and often utilized in many quality departments around the globe.  However, it is remiss to not mention the 7 supplemental tools that provide additional statistical quality:

  1. Data stratification
  2. Event logs
  3. Process flowcharts
  4. Defect maps
  5. Progress centers
  6. Sample size determination
  7. Randomization

History of Statistical Process Control

During the course of World War II, the use of control charts and statistical control became a necessity.  Under President Roosevelt’s leadership from 1939-1945, the US saw a massive industrialization effort to produce weaponry, aircraft, planes, and naval vessels, to support its allies. 

Ensuring the quality of these munitions, materials, and other war supplies, was of integral importance to the ongoing efforts to defeat the Nazis.  Hence, SPC became an ideal quality standard, to supplement and uphold the existing production taking place.

To date, statistical process control has only grown more sophisticated.  As technology has improved, so has our capacity for devising sophisticated data collection systems.

A noteworthy triumph that reflects the influence of SPC and total quality management, was the efforts of the notable W. Edwards Deming in Japan. 

Using what would become Deming’s own theory (literally known as Deming theory), Japan was able to turn itself into a preeminent manufacturing superpower.  Within the span of a few short decades, the tiny island and its people had overtaken the United States in terms of manufacturing capability.  That is, they could produce better items and sell the abroad for less.

It was only when Americans realized they were buying cheaper and higher quality Japanese products at home, that they realized how sharply their own manufacturing had declined.

A lack of competition in the immediate post World War II era, had given the Americans the impression that they would never have to actively compete with foreign markets again. 

As Japan had shown, their rationalizations had been in error.  Using methods such as statistical process control, a manufacturing process could be continuously improved upon.  It was only a matter of time before the Japanese became proficient enough to challenge other manufacturing economies.

Statistical Process Control Explained

By the early 1970’s, Japan’s manufacturing prowess had become internationally renowned.  As a major competitor, they were even outperforming the US.  The use of Kaizen and lofty standards under TQM (total quality management) had created a burgeoning Japanese economy.  Many countries only realized how far the Japanese had come when they realized they were purchasing their high quality products, at lower prices.

Applying Statistical Process Control To A Non-Manufacturing Process

Many processes have variations that can be actively analyzed.  Determining underlying root-causes and implementing corrective actions can be markedly improved by the use of the SPC quality format.

An example of this could revolve around a service-based business.  In the event of producing the service, there may be outliers that seemingly occur at random.  By devising a means of observing and recording these outliers, the sources of variation could be more aptly determined.

Once these variations have been addressed, higher profitability could be realized.  

There is some controversy however, in apply SPC to processes which are not repetitive by nature.

Moreover, in certain instances, statistical process control may not be advisable.  A particularly acute example of this is systems engineering.  Due to the variance of the code itself, it would be difficult to implement a consistent statistical process control that made sense.

In general, statistical process control is most ideally used in repetitive tasks, or specifically, in manufacturing settings.

When Using Statistical Process Control…

SPC is an excellent addition to any productive process.  It can safeguard and bolster your quality assurance methodology.  However, there are a few considerations that must be noted…:

Make Use of Control Charts

The data from control charts helps to identify areas of concern within your process.  Some variations will be inherent to your process.  These represent little concern.

However, sources of special variation must be addressed as soon as possible.  These variances represent a design or input flaw within your process which is entirely unnecessary.  In effect, this issue is costing you money, and can be fixed with the proper expertise in place.

By making constant use of control charts, your monitoring efforts will be streamlined towards addressing quality concerns quickly and effectively.

Identifying Root Causes of Excessive Variation

When detecting a breach of control rules, it is imperative to discover the source(s) of variation that are present.  This can take the form of performing an investigation, or by seeking system-wide changes that have been made recently.

Once the source(s) of variation have been discovered, they must be recorded for future reference.  This includes the circumstances that allowed their existence in the first place.

Take careful care to minimize or eliminate the variation entirely.

To prevent future occurrences, you might include the following:

  • Further staff development and training.
  • Error-proofing a particularly problematic process.
  • Redefining of standards.
  • Altering the process entirely.
  • Making changes to inputs.
  • Adding secondary protocols.
  • Raising awareness of prevalent or possible issues.

Conforming To A Stable Process

When no significant detections are occurring, the process can be said to be stable.

During this time, it is essential to continue monitoring efforts.  At the same time, a study into the process capability should be performed with due haste.  This will allow for further prevention of nonconformities in the near future.

A continuing effort to produce a stable process will ensure variances outside of specification are kept within a minimal threshold.  This will also be demonstrated by the statistical safeguarding of the upper statistical limitations and the lower statistical limitations of all quality specifications.

Any process which can maintain minimal variations and hold to the implemented specifications will remain a stable process.

Using Metrics To Assess Process Stability

Statistical process control requires careful monitoring with control charts.  It is inherently practical to calculate all metrics related to processes that are in need remedial actions.

Example

One portion of the process has been calculated to have created several non-conformities that are inherent to the process.

However, another portion has created grave non-conformities which are not inherent to the process.

The use of statistics to analyze and determine the difference of these two non-conforming inputs will allow for a productive maintenance to occur.  Naturally, the non-inherent conformities must be targeted, as they are malleable to corrective efforts by their very nature.   

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