Statistical Process Control 101

Learn all about SPC for manufacturing.

Statistical Process Control

Have you heard about statistical process control (SPC) but aren’t quite sure what it is or how it could improve your bottom line? We’ve put together this short guide to answer some of the most common SPC manufacturing questions.

Statistical Process Control (SPC) Definition

At its most basic, statistical process control (SPC) is a systematic approach of collecting and analyzing process data for prediction and improvement purposes. SPC is about understanding process behavior so that you can continuously improve results. 

As you learn about SPC, you’ll encounter terms  that describe central tendency:
  • Mean: the arithmetic average of a set of collected values
  • Mode: the value that occurs most often within a set of collected values
  • Median: the value that defines where half a set of collected values is above the value and half is below

You will also come across terms that describe the width or spread of data:
  • Variation: a term used to describe the amount of dispersion in a set of data
  • Range: a measure of dispersion that is equal to the maximum value minus the minimum value from a given set of data
  • Standard deviation: a measure used to quantify a data set’s dispersion from its mean value 
 
LEARN MORE: WHAT IS STATISTICAL PROCESS CONTROL (SPC)?
 

SPC Origins:
Shewhart Statistical Process Control (SPC) Charts

Dr. Walter A. Shewhart (1891–1967), a physicist at Bell Labs who specialized in the use of statistical methods for analyzing random behavior of small particles, was responsible for the application of statistical methods to process control. Up until Shewhart, quality control methods were focused on inspecting finished goods and sorting out the nonconforming product. 

As an alternative to inspection, Shewhart introduced the concept of continuous inspection during production and plotting the results on a time-ordered graph that we now know as a control chart. By studying the plot point patterns, Shewhart realized some levels of variation are normal while others are anomalies.
 
Using known understandings of the normal distribution, Shewhart established limits to be drawn on the charts that would separate expected levels of variation from the unexpected. He later coined the terms common cause and assignable cause variation.

Dr. Shewhart concluded that every process exhibits variation: either controlled variation (common cause) or uncontrolled variation (assignable cause). He defined a process as being controlled when “through the use of past experience, we can predict, at least within limits, how the process may be expected to vary in the future.”

He went on to develop descriptive statistics to aid manufacturing, including the Shewhart Statistical Process Control Chart—now known as the X-bar and Range (Xbar-R) chart. The purpose of the Shewhart Statistical Process Control Chart is to present distributions of data over time to allow processes to be improved during production. This chart changes the focus of quality control from detecting defects after production to preventing defects during production.

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