Introduction to Statistical Process Control

1
Introduction to Statistical Process Control

• Engineering Experimental Design
• Valerie L. Young

2
Outline

• Description of and justification for Statistical
  Process Control
• Fundamental definitions and principles
• Variability, specifications, capability
• Process characterization
• Why focus on variability first?
• Constructing control charts
• Calculating process capability ratios

3
What is Statistical Process Control?

• Strategy for process improvement that uses
  statistics-based techniques to evaluate the
  process and identify opportunities for
  improvement
• Strategy that focuses on quantifying,
  classifying, and reducing variability in the
  process
• Based on the philosophy that making the right
  product in the first place is better than trying
  to rework the wrong product

4
Quality Control vs. Process Control

• Traditional quality control focuses on the
  product
• Monitor product quality
• Rework or scrap off-spec product
• Statistical process control focuses on the
  process
• Monitor process behavior (including product
  quality)
• Adjust the process to eliminate off-spec
  production

5
Quality Control vs. Process Control

• Traditional quality control focuses on the values
• A value outside specifications is a signal that
  the product must be reworked or scrapped
• Statistical process control focuses on the
  variability
• Variation outside usual limits in ANY process
  variable is a signal that the process should be
  adjusted to prevent production of unacceptable
  product

6
Why Not Just Inspect Reject?

• Reality of escaping defects
• Even the most careful inspection misses sometimes
• Bad product means unhappy customers
• Inspection costs money
• Rejection wastes resources
• Reworking/scrapping wastes time, money, and
  resources

7
Why Use Statistics?

• Intuition and gut feelings
• Simple problems
• Inexpensive solutions
• Low risk in case of failure
• Statistical evaluation
• Complex problems
• Expensive solutions
• High risk in case of failure

8
Is this theory, or is this relevant?

• Major corporations all over the world have
  adopted a Statistical Process Control strategy
  called Six Sigma, and are applying it to ALL
  operations, including production, marketing, and
  customer service.
• Many of the tools of Statistical Process Control
  (control charts, capability indices) can be used
  without any theoretical understanding of
  statistics.

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Two Types of Variability

• Common cause (Random)
• Always present, even when process operation is
  consistent
• Can be quantified with summary statistics that
  are consistent over time
• CANNOT be reduced by adjusting the existing
  process, only by changing it
• Special cause (Assignable)

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Two Types of Variability

• Common cause (Random)
• Special cause (Assignable)
• Response to some inconsistency in process
  operation (purposefully adjusting that factor
  would give a predictable response)
• Results in summary statistics that are not
  consistent over time
• CAN be reduced by adjusting the existing process

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Two Types of Variability
How could you reduce the variability from each of
these sources?

• Common cause (Random)
• Precision limits of instrumentation
• Changes in ambient conditions
• Special cause (Assignable)
• Each operator has his own style
• Raw materials purchased from different suppliers
• Equipment wear

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Two Types of Variability(This may hurt your
brain at first)

• Common cause (Random)
• Random, so its effect on the product is
  predictable. If only common cause variability is
  present, then product quality will only vary
  within a specified range. (99 of product will
  be within 3 standard deviations of the mean
  value.)
• Special cause (Assignable)
• Non-random, so its effect on the product is
  UNpredictable until you identify the special
  cause. When special cause variability is
  present, but the cause has not been identified,
  product quality can change in any direction at
  any time.

13
Specifications

• The range of acceptable values
• May be given as Value Tolerance
• May be given as USL (upper specification limit)
  and LSL (lower specification limit)
• Determined by the user, not by the process
• Not calculated from process data
• Product that does not meet specifications is
  termed off-spec

14
Process Capability Ratios
(Desired Performance) / (Actual Performance)
Process performance is not necessarily centered
between the spec limits
The shaded areas represent the percentage of
off-spec production
This curve is the distribution of data from the
process
Voice of Customer
Voice of Process
15
Process Characterization

• Ideal State
• Process in control (all special causes of
  variability are eliminated, and only random
  variability remains)
• 100 acceptable product (mean value
  variability of product is inside the
  specification limits)
• Threshold State
• Brink of Chaos
• State of Chaos

16
Process Characterization

• Ideal State
• Threshold State
• Process in control
• all special cause variability eliminated
• only random variability remains
• Some off-spec product
• Mean value not centered between specification
  limits and/or
• Random process variability exceeds specification
  limits
• Brink of Chaos
• State of Chaos

17
Process Characterization

• Ideal State
• Threshold State
• Brink of Chaos
• Process out of control product quality wanders
  due to
• Uncontrolled special causes AND
• Inherent random variability
• 100 acceptable product
• State of Chaos

18
Process Characterization
Which problem should you address first an
off-center mean, or special cause variability?

• Ideal State
• Threshold State
• Brink of Chaos
• State of Chaos
• Process out of control product quality wanders
  due to
• Uncontrolled special causes AND
• Inherent random variability
• Some off-spec product
• Mean value not centered between specification
  limits and/or
• Process variability exceeds specification limits