Statistical Quality Control in Manufacturing
Statistical quality control methods utilize data-driven measurements to detect and eliminate defects or variations in the manufacturing process that lower the quality of products. This article summarizes the origins of quality control and statistical quality control; examines Six Sigma, a widely-recognized methodology for statistical quality control; notes the situations where the use of Six Sigma is undesirable; and provides a glossary of relevant terms.
Keywords Control Chart; Deviation; DMAIC; Lean Manufacturing; Quality; Quality Control; Six Sigma; Standard Deviation; Statistical Process Control; Statistical Quality Control; Tolerance
What is Quality Control?
Quality control refers to the oversight of the manufacturing process to ensure that products are manufactured as error-free as possible.
Origins of Quality Control
Quality control methods have been used in manufacturing for centuries. Formal quality control measures have existed at least as far back as the Middle Ages, when the craft guilds required apprentices to undergo long and rigorous training and also demonstrate proficiencies in their crafts before they would be considered master craftsmen (National Institute of Standards and Technology, 2006).
What is Statistical Quality Control?
Statistical quality control methods utilize data-driven measurements to detect and eliminate defects or variations in the manufacturing process that lower the quality of products.
(The term "statistical process control" is sometimes used interchangeably with the term "statistical quality control." However, according to the American Society for Quality, the two terms do differ somewhat; see the "Glossary" below.)
Origins of Statistical Quality Control
Statistical quality control methods were introduced in 1924 by Walter A. Shewhart, an engineer at Western Electric and parent company, Bell Telephone Laboratories. In 1924, Shewhart sent a memo that included a diagram of a modern control chart. (A control chart is a diagram that indicates "upper and lower control limits on which values of some statistical measure for a series of samples or subgroups are plotted. The chart frequently shows a central line to help detect a trend of plotted values toward either control limit" (American Society for Quality, 2005, Glossary).
Shewhart and his colleagues at Bell Laboratories continued to refine the theory and application of statistical quality control and in 1931, Van Nostrand published Shewhart's book, Economic control of quality of manufactured product (National Institute of Standards and Technology, 2006).
Six Sigma: A Contemporary Statistical Quality Control Methodology
Currently, Six Sigma is one of the most widely-recognized methodologies of statistical quality control. Six Sigma methodology "values defect prevention over defect detection. It drives customer satisfaction and bottom-line results by reducing variation and waste" (American Society for Quality,2995, Six Sigma Overview). Motorola is credited with creating Six Sigma in 1987. Soon afterwards, the concept was adapted by other leading manufacturers including Texas Instruments, IBM, General Electric, and Whirlpool (Dossenbach, 2004, p. 25).
This section examines Six Sigma methodology as it applies to statistical quality control in the manufacturing industry.
Six Sigma: Goal of the Six Sigma Methodology
The goal of Six Sigma methodology is to consistently manufacture products that have no defects by utilizing statistical quality control tools. According to the American Society for Quality (2005), quality manufacturing performance will yield no more than 3.4 defects per million opportunities.
Six Sigma: DMAIC: A Five Step Process
Six Sigma relies upon a five-step process:
The process is commonly known by the abbreviation "DMAIC" which denotes the first word in each step. Calabrese (2007, p. 31) further expands upon the five Six Sigma, DMAIC steps as follows:
- "Define: Identify the variable to be improved."
- "Measure: Capture data on the identified data."
- "Analyze: Brainstorm the root cause variables and their relationship with the variable that is to be improved."
- "Improve: Remove root causes and/or minimize variations around the mean of the target variable."
- "Control: Sustain the improvements in the process via control chart applications."
Six Sigma: DMAIC: The Significance
Control charts (see step 5 in "Six Sigma: DMAIC: Five Steps to Perfection" above) are the most significant feature of the Six Sigma methodology. The control chart is the tool that is used to plot and graph a process over time in order to detect variations that deviate from the allowable standard deviation. The allowable standard deviation is also known as "tolerance."
The basic control chart procedure involves collecting and charting data for a specific time period and analyzing the data for out-of-control signals (deviations). Out-of-control signals may illustrate any of the following deviations (Tague, 2004, p. 155-158 (as cited by the American Society for Quality, 2005)):
- A single point outside the control limits;
- Points that deviate from the control limits;
- Data that indicates unusual data or process patterns.
Six Sigma: Training
The Six Sigma methodology has become so popular that training and certification in the five levels of expertise are offered by many organizations, including the American Society for Quality.
Five Levels of Expertise
There are five levels of expertise in Six Sigma; each is labeled with a martial arts term.
- White Belt
- Yellow Belt
- Green Belt
- Black Belt
- Master Black Belt
The following descriptions of the five levels are from the American Society for Quality (2005) and are arranged from lowest to highest level of knowledge and responsibility:
- "White Belt: Can work on local problem-solving teams that support overall projects but may not be part of a Six Sigma project team."
- "Yellow Belt: Participates as a project team member; reviews process improvements that support the project."
- "Green Belt: Assists with data collection and analysis for Black Belt projects; leads Green Belt projects or teams."
- "Black Belt: Leads problem-solving projects; trains and coaches project teams."
- "Master Black Belt: Trains and coaches Black Belts and Green Belts; functions more at the Six Sigma program level by developing key metrics and the strategic direction; acts as an organization's Six Sigma technologist and internal consultant."
Six Sigma: Case Study: Boeing's Satellite Development Center (SDC)
Boeing's Satellite Development Center (SDC) is the world's largest manufacturer of satellites. The satellites are manufactured for military, weather, space, and communications purposes.
Precision in the manufacturing of the satellites and their parts is absolutely essential for the safety and accuracy of the finished...
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