QUALITY MANAGEMENT

1
QUALITY MANAGEMENT

• Defining Quality
• Total Quality Control
• The Costs Of Quality
• Statistical Process Control
• P Charts For Attributes
• X-bar And R Charts For Variables

2
DEFINING QUALITY

• What is product "quality"?
• Those factors that influence customer
  satisfaction
• Design quality -- is the product designed to
  satisfy customer needs?
• Conformance quality -- is the product
  manufactured to design specs?

3
TOTAL QUALITY CONTROL

• Design quality control -- ensure product is both
  marketable and manufacturable
• Product/service quality control -- ensure
  facility achieves conformance quality
• Statistical process control potential problem
  detection
• Incoming material quality control -- ensure
  suppliers achieve conformance quality

4
THE COSTS OF QUALITY

• Isnt TQC expensive? Not as expensive as not
  doing it
• This is illustrated by identifying the costs of
  quality at a facility
• Costs of not doing TQC
• Costs of internal failure (scrap)
• Costs of external failure (warranties)
• Costs of doing TQC wrong
• Costs of inspection
• Costs of doing TQC right
• Costs of prevention (TQC system)

5
TOTAL QUALITY CONTROL A BRIEF HISTORY I

• Walter Shewhart (1891-1967)
• Developed control charts, PDCA cycle at Bell Labs
• Economic Control of Quality of a Manufactured
  Product, 1931
• W. Edwards Ed Deming (1900-1993)
• Yale class of 1928
• Worked for the U.S. Census bureau
• Went to Japan to administer a census (1950),
  gives lectures on quality control
• Invited back to Japan by J.U.S.E.
• Developed the 14 points

6
PLAN/DO/CHECK/ACT CYCLE

• Four basic steps of quality improvement
• Plan
•  How can quality be improved?
• Do
• Implement the plan on a trial basis
• Check
• Did the trial work?
• Act
• Adopt or reject the plan

7
TOTAL QUALITY CONTROL A BRIEF HISTORY I I

• Armand Vallin Feigenbaum (1922- )
• Top quality guru for G.E. By 1944
• Total Quality Control, 1951, published while
  attending graduate school (MIT)
• "Total Quality Management" (1980s)
•  Quality throughout the organization
•  Malcolm Baldridge Award (1987)
•  Focus customer satisfaction
• ISO 9000/ANSI Q9000 standards (1987)
• Focus quality systems and procedures

8
ISO 9000 AND BALDRIDGE

• ISO 9000 emphasizes
• Documentation
• Basic procedures for TQC
• But latest version (ISO 90002000) has more
  customer focus
• Malcolm Baldridge emphasizes
• Customer satisfaction
• Quality results
• Business results
• Baldridge awards world-class, ISO certifies
  base-line

9
ATTRIBUTE VERSUS VARIABLE QUALITY CHARACTERISTICS

• To control quality, we must measure it
• Quality characteristics are what we measure
• Dimensions
• Other chemical, physical properties
• Defects per unit
• Quality characteristics may be
• Variable (continuous) data
• Attribute (discrete) data

10
STATISTICAL PROCESS CONTROL

• SPC is used to detect potential problems
• This means distinguishing between
• Unassignable error -- randomness due to inherent
  variability in the process
• The process is in control
• Assignable error -- variability resulting from a
  change in the process
• The process is out of control

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CONTROL LIMITS FOR PROBLEM DETECTION

• A control chart is a graphical aid to help the
  worker make this distinction
• UCL -- upper control limit for Q.C.
• LCL-- lower control limit for Q.C.

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CONTROL LIMITS FOR PROBLEM DETECTION

• If an observation falls inside the limits
• Conclude process is in control
• If an observation falls outside the limits
• Conclude process is out of control

13
MISTAKES TO AVOID

• Need to set control limits to minimize two types
  of mistakes
• Type I -- conclude we're out of control when
  we're in control
• Type II -- conclude we're in control when we're
  out of control
• Probability of each type of mistake
• Type I -- a Type II-- b
• H0 the process is in control

14
SETTING CONTROL LIMITS

• Focus on minimizing Type I error first
• If control limits set to /- 3 standard
  deviations of the statistic, probability of a
  Type I error lt 1
• Assumes normal distribution
• What about Type II error?
•  Increase sample size

15
P CHARTS FOR ATTRIBUTES

• Tracks "go/no-go" quality characteristics
•  n -- Sample size
• m Number of samples collected to date
•  Xi -- Number of nonconformities in ith sample
•  pi Fraction nonconforming in the ith sample
• Xi is a binomial random variable
• Example Leaking juice cans
• n 100
• m 10

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P CHARTS FOR ATTRIBUTES

• Estimate the mean fraction nonconforming by
  calculating p-bar from all data

• Estimate the standard deviation of the fraction
  nonconforming in the samples

17
P CHARTS FOR ATTRIBUTES

• Set control limits 3 standard deviations above
  and below the mean

18
X-BAR AND R CHARTS FOR VARIABLES

• A normal distribution has two parameters
• Mean (Location)
• Variance (Dispersion)
• A control chart is needed to track each parameter
• If either one or both is out of control, the
  process is out of control
• X-bar chart tracks mean
• R chart tracks dispersion

19
X-BAR AND R CHARTS

• Tracks continuously variable quality
  characteristics
•  n -- Sample size
• m Number of samples collected to date
• X-bari Mean of ith sample
• Ri Range of ith sample

• X-bari are approximately normally distributed
• Example Bursting strength (in psi) of 1-liter
  soda bottles
• n 4
• m 10

20
X-BAR AND R CHARTS

• Tracks continuously variable quality
  characteristics
•  n -- Sample size (4)
• m Number of samples collected to date (10)
• X-bari Mean of ith sample
• Ri Range of ith sample

21
X-BAR AND R CHARTS

• Then the grand mean (X-bar-bar) and the mean
  range (R-bar) are

22
X-BAR CHART

• The control limits for the charts are in practice
  obtained using a table of control chart constants

23
R CHART

• The control limits for the charts are in practice
  obtained using a table of control chart constants