Statistical Process Control

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It was Walter Shewhart who developed the theory of Statistical Process Control in 1924 while at Bell Labs. His theory and lectures were published in a book called Economic Control of Quality Manufactured Product. He was the first to apply statistical techniques to manufacturing processes.

Post WWII W. Edwards Deming took Shewhart’s SPC techniques to Japan and trained 100’s of Japanese engineers. Deming promised that Japanese quality would be known world-wide within in five years. It only took four!

Prophet of Quality in Japan - America Learns

Statistical Process Control (SPC)

Statistical Process Control, commonly referred to as SPC, is a form of Statistical Quality Control. It uses graphical displays known as control charts to determine when a process should be adjusted.

SPC is Prevention! Acceptance Sampling is Detection!

Control Chart

A control chart is a form of time series plot. It allows you to monitor process performance over time. More specifically, with a control chart you can detect:

  • Changes in process average performance.
  • Changes in process variation

In the time series chart below a shift in the process average can clearly be seen beginning at index value 20.. But, some meaningful changes are not so easily seen by eye. SPC will detect these small meaningful process changes.

Time Series Plot

Change in process average beginning at Index value 20.

Changes in process variation can also be seen in graphical charts. 

The time series plot below shows an increase in process variation at Index values 23 and 24. Like the process average above, SPC can detect much smaller meaningful changes that are not clearly evident by eye.

Run Chart Plot

Increase in process variation at Index values 23 and 24.

Special-cause Variation:

Significant “one-of”, type events. The plot below shows a sharp spike in performance. Some “one-of” type event. And yes, once again, with SPC you can detect much smaller meaningful changes.

Time Series Plot Data Outlier

Special-cause variation (one-of type event)

Understanding Variation

Variations Impact

It can be said that the perfect process is one with no variation. A target is set and is hit every time. Unfortunately, no perfect process exists. yet. All processes exhibit variation!

At some point variation can hurt! The symptoms of its existence in business are repair, rework, late deliveries, loss of customer goodwill, high prices, the list goes on and on. For these reasons variation must be controlled.

Controlling process variation minimizes the time, cost and “pain” associated with not producing to requirements! Variation is the enemy of all processes.

Using simple statistics and simple graphical control charts, Shewhart developed statistical process control “weapons” to combat variation.

He showed that variation acted upon processes in two very distinct ways!

  1. There’s variation that is natural, it’s an inherent part of the process and is always there. It’s common to all similar things. This is known as common-cause variation. Common-cause variation is systemic and typically management controlled,
  2. Then there’s variation that is “intermittent and special”. It is not always present and is discoverable and removable. This is known as special-cause variation. Special-cause variation is typically worker controlled.

SPC Theory

  • All processes exhibit variation,
  • This variation consists of two types; common-cause variation and special-cause variation,
  • Variation can be quantified by using statistical distributions,
  • Changes in these distributions can be seen by plotting data over time.

When only common-cause variation exists in a process the process is highly predictable.

Processes that are experiencing the second type of variation, special-cause variation, are not predictable. A part that breaks in a production machine or an untrained associate performing a task are examples of potential sources of special-cause variation.

SPC vs. SPM (Statistical Process Monitoring)

Bill Smith, a Quality Engineer at Motorola in the 1980’s and the father of Six Sigma, gets my credit for redefining the application of SPC.

Smith’s Six Sigma drove home the point that the output is a function of the inputs. Statistical Process Control books were re-written because of Smith’s work.

Statistical Process Control is best applied to process inputs. You can control the inputs but only monitor the outputs.

Sources of Variation

Process Model - Sources of Variation

Process Model - Sources of Variation

Statistical Process Control Basics

Statistical Process Control

Control Charts

Control Chart Selection

Control Limits

Capability Indices - Cpk, Etc.

Process Capability Study

Statistical Process Control Control Charts

Xbar & R (Range) Chart

Xbar & s (Standard Deviation) Chart

I (Individuals) & MR (Moving Range) Chart

p Chart

np Chart

u Chart

From Statistical Process Control to Statistical Quality Control.

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