Transcript 20151207_v001_msamc_courseware_quality_ppt4

Multi-State
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Consortium
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PRIMARY DEVELOPER – Janaina Gasparelo, Henry Ford College
INTEGRATED QUALITY
SESSION 4
PROFESSOR JANAINA GASPARELO
HENRY FORD COLLEGE
JANUARY, 2015
US DEPARTMENT OF LABOR
MULTI-STATE
ADVANCED MANUFACTURING CONSORTIUM
MULTI-STATE
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20151207_v001_msamc_courseware_quality_ppt4 found in Resources by
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12/3/2015
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AGENDA
•
•
•
•
•
•
Current Events
Quick review on 7 Basic Quality Control Tools
Basic Statistics & Normal Theory
Overview (APQP, PPAP, FMEA, SPC, Gage R&R, MSA)
8D Problem Solving
Define Corrective Action, Preventive Action,
Containment Action and Layered Process Audits
• Discuss Continuous Process Improvements
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BASIC QUALITY CONTROL TOOLS
1. Cause-and-effect diagram (also called Ishikawa or
fishbone chart): Identifies many possible causes for an
effect or problem and sorts ideas into useful categories.
2. Check sheet: A structured, prepared form for collecting
and analyzing data; a generic tool that can be adapted
for a wide variety of purposes.
3. Control charts: Graphs used to study how a process
changes over time.
4. Histogram: The most commonly used graph for showing
frequency distributions, or how often each different
value in a set of data occurs.
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BASIC QUALITY CONTROL TOOLS
1. Pareto chart: Shows on a bar graph which factors are
more significant.
2. Scatter diagram: Graphs pairs of numerical data, one
variable on each axis, to look for a relationship.
3. Stratification: A technique that separates data gathered
from a variety of sources so that patterns can be seen
(some lists replace “stratification” with “flowchart” or
“run chart”).
Excerpted from Nancy R. Tague’s The Quality Toolbox, Second Edition, ASQ Quality Press, 2005,
page 15.
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BASIC STATISTICS
• Ways of Describing Data
Describing
Data
Numerical
Center
Graphical
Spread
Center Spread
Mean
Range
Histogram
Median
Standard
Deviation
Dot Plot
BoxPlot
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BASIC STATISTICS
MULTI-STATE
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20151207_v001_msamc_courseware_quality_ppt4 found in Resources by
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BASIC STATISTICS
MULTI-STATE
ADVANCED MANUFACTURING CONSORTIUM
20151207_v001_msamc_courseware_quality_ppt4 found in Resources by
the M-SAMC Multi-State Advanced Manufacturing Consortium www.msamc.org is licensed under a
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BASIC STATISTICS
MULTI-STATE
ADVANCED MANUFACTURING CONSORTIUM
20151207_v001_msamc_courseware_quality_ppt4 found in Resources by
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BASIC STATISTICS
MULTI-STATE
ADVANCED MANUFACTURING CONSORTIUM
20151207_v001_msamc_courseware_quality_ppt4 found in Resources by
the M-SAMC Multi-State Advanced Manufacturing Consortium www.msamc.org is licensed under a
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BASIC STATISTICS
MULTI-STATE
ADVANCED MANUFACTURING CONSORTIUM
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BASIC STATISTICS
• Give me some real world examples of using mean and
median
• Are there times when this information could be
misleading?
• How do you solve that?
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BASIC STATISTICS
MULTI-STATE
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20151207_v001_msamc_courseware_quality_ppt4 found in Resources by
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BASIC STATISTICS
MULTI-STATE
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BASIC STATISTICS
MULTI-STATE
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20151207_v001_msamc_courseware_quality_ppt4 found in Resources by
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BASIC STATISTICS
MULTI-STATE
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BASIC STATISTICS
The Normal Distribution
• The Normal Distribution is a distribution of data which
has certain reliable properties
• To understand the characteristics of the underlying
process from which the data were obtained it is
important to use the properties
• It is possible to represent many natural phenomena and
man-made processes and normally distributed. Some
are naturally distributed.
• Don’t be worried if your data is not normal.
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BASIC STATISTICS
MULTI-STATE
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20151207_v001_msamc_courseware_quality_ppt4 found in Resources by
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BASIC STATISTICS
MULTI-STATE
ADVANCED MANUFACTURING CONSORTIUM
20151207_v001_msamc_courseware_quality_ppt4 found in Resources by
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BASIC STATISTICS
MULTI-STATE
ADVANCED MANUFACTURING CONSORTIUM
20151207_v001_msamc_courseware_quality_ppt4 found in Resources by
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BASIC STATISTICS
MULTI-STATE
ADVANCED MANUFACTURING CONSORTIUM
20151207_v001_msamc_courseware_quality_ppt4 found in Resources by
the M-SAMC Multi-State Advanced Manufacturing Consortium www.msamc.org is licensed under a
Creative Commons Attribution 4.0 International License.
BASIC STATISTICS
MULTI-STATE
ADVANCED MANUFACTURING CONSORTIUM
20151207_v001_msamc_courseware_quality_ppt4 found in Resources by
the M-SAMC Multi-State Advanced Manufacturing Consortium www.msamc.org is licensed under a
Creative Commons Attribution 4.0 International License.
BASIC STATISTICS
MULTI-STATE
ADVANCED MANUFACTURING CONSORTIUM
20151207_v001_msamc_courseware_quality_ppt4 found in Resources by
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Creative Commons Attribution 4.0 International License.
BASIC STATISTICS
MULTI-STATE
ADVANCED MANUFACTURING CONSORTIUM
20151207_v001_msamc_courseware_quality_ppt4 found in Resources by
the M-SAMC Multi-State Advanced Manufacturing Consortium www.msamc.org is licensed under a
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BASIC STATISTICS
MULTI-STATE
ADVANCED MANUFACTURING CONSORTIUM
20151207_v001_msamc_courseware_quality_ppt4 found in Resources by
the M-SAMC Multi-State Advanced Manufacturing Consortium www.msamc.org is licensed under a
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BASIC STATISTICS
MULTI-STATE
ADVANCED MANUFACTURING CONSORTIUM
20151207_v001_msamc_courseware_quality_ppt4 found in Resources by
the M-SAMC Multi-State Advanced Manufacturing Consortium www.msamc.org is licensed under a
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NORMAL THEORY
• Is Something Fishy?
• You manage the shipping department for a manufacturer
of medical devices. You get a charge for $642 for
expedited parts that you think usually comes in closer to
$500. You obtain 250 recent charges for that same item
to make comparisons. You want to know if there is
something fishy about this charge or if it is within the
usual range for that item.
• How can you use the data to answer the question?
MULTI-STATE
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20151207_v001_msamc_courseware_quality_ppt4 found in Resources by
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NORMAL THEORY
• Is Something Fishy?
• Option 1: Create a control chart of the data
– Use a control chart of charges to see if the fishy
charge is outside the limits
• Option 2: Apply Normal theory
– Use Normal theory, a body of statistics built around
the properties of the Normal distribution
– See where the fishy value falls in the distribution of
this data
MULTI-STATE
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20151207_v001_msamc_courseware_quality_ppt4 found in Resources by
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Creative Commons Attribution 4.0 International License.
NORMAL THEORY
• Definition
– A probability distribution where the most frequently occurring
value is in the middle and other probabilities tail off
symmetrically in both directions. This shape is sometimes called
a bell-shaped curve.
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20151207_v001_msamc_courseware_quality_ppt4 found in Resources by
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NORMAL THEORY
• 1. Make a histogram to see if
(continuous) data are Normal.
50
Looks roughly Normal
Frequency
40
30
20
10
0
350
450
550
650
Express Delivery Charges ($)
• 2. Get the mean and standard deviation of data.
X
 X  $500
n
 X  X 
2
S
n 1
 $50
A standard deviation is the typical
amount an observation varies from
the mean
MULTI-STATE
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20151207_v001_msamc_courseware_quality_ppt4 found in Resources by
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NORMAL THEORY
• 3. Draw Normal curve for data.
350
400
450
500
550
600
650
Express Delivery Charges ($)
$642
• 4. Compare the value-of-interest ($642) to the Normal
curve.
MULTI-STATE
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20151207_v001_msamc_courseware_quality_ppt4 found in Resources by
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NORMAL THEORY
• 5. How many standard deviations away from the mean
is the value-of-interest?
-3
-2
-1
0
1
2
3
The “standard”
Normal or Z-value
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NORMAL THEORY
• 6. Look up the probability of a charge being that value
or higher in the Normal table of Z-values.
Z
Area = probability
• 7. If probability is low . . . then something’s probably
fishy.
“Low” usually means < .05
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NORMAL THEORY
X
X +S
X S
X  2S
X + 2S
X  3S
-3
-3
X + 3S
-2
-2
-1
-1
0
0
1
1
2
2
3
3
Normal
data;
“any” scale
Fill in the
blanks for
data with
X = 500
s = 50
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20151207_v001_msamc_courseware_quality_ppt4 found in Resources by
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NORMAL THEORY
Z  (X  X)
S
X = value-of-interest
Standard Normal
(or Z-values)
x=0
s=1
-3
-3
-2
-2
-1
-1
00
11
22
33
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20151207_v001_msamc_courseware_quality_ppt4 found in Resources by
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NORMAL THEORY
A “Standard” Normal Distribution
mean = 0
st. dev. = 1
-3
-2
-1
-3
-2
-1
0
0
1
1
2
2
3
3
Z-value anywhere
on this scale
• Z-value
• How many standard deviations
the value-of-interest is
away from the mean
Z
(value - of - interest)  X
S
MULTI-STATE
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20151207_v001_msamc_courseware_quality_ppt4 found in Resources by
the M-SAMC Multi-State Advanced Manufacturing Consortium www.msamc.org is licensed under a
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NORMAL THEORY
• Probabilities of Z-values
Area under the
standard Normal
curve = probability
Area = 1
-3
-2
-1
-3
-2
-1
0
0
1
1
2
2
What is the probability
a Z-value will be  zero?
3
3
Area = .5
Probability = .5 or 50%
-3
-3
-2
-2
-1
-1
0
0
1
1
2
2
3
3
What is the probability
a Z-value will be 2.84?
Area = ?
MULTI-STATE
ADVANCED MANUFACTURING CONSORTIUM
-3
-2
-1
0
1
2
3
20151207_v001_msamc_courseware_quality_ppt4 found in Resources by
-3
-2
-1
0
1
2
3
the M-SAMC Multi-State Advanced Manufacturing Consortium www.msamc.org is licensed under a
Creative Commons Attribution 4.0 International License.
Value-of-interest
8D PROBLEM SOLVING
• Eight Disciplines (8Ds) Problem Solving is a method
developed at Ford Motor Company used to approach and
to resolve problems, typically employed by engineers or
other professionals. Its purpose is to identify, correct,
and eliminate recurring problems, and it is focused on
product and process improvement. It establishes a
permanent corrective action based on statistical analysis
of the problem and on the origin of the problem by
determining the root causes.
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20151207_v001_msamc_courseware_quality_ppt4 found in Resources by
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8D PROBLEM SOLVING
• Although it originally comprised eight stages, or
'disciplines', it was later augmented by an initial planning
stage. 8D follows the logic of the PDCA cycle.
• PDCA (plan–do–check–act or plan–do–check–adjust) is
an iterative four-step management method used in
business for the control and continuous improvement of
processes and products. It is also known as the Deming
circle/cycle/wheel, Shewhart cycle, control circle/cycle,
or plan–do–study–act (PDSA).
MULTI-STATE
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8D PROBLEM SOLVING
The disciplines are:
• D0: Plan: Plan for solving the problem and determine the
prerequisites.D1: Use a Team: Establish a team of people
with product/process knowledge.
• D2: Describe the Problem: Specify the problem by
identifying in quantifiable terms the who, what, where,
when, why, how, and how many (5W2H) for the problem.
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8D PROBLEM SOLVING
• D3: Develop Interim Containment Plan: Define and
implement containment actions to isolate the problem
from any customer.
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8D PROBLEM SOLVING
• D4: Determine, and Verify Root Causes and Escape
Points: Identify all applicable causes that could explain
why the problem has occurred. Also identify why the
problem was not noticed at the time it occurred. All
causes shall be verified or proved. One can use five whys
or Ishikawa diagrams to map causes against the effect or
problem identified.
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8D PROBLEM SOLVING
• D5: Verify Permanent Corrections (PCs) for Problem will
resolve problem for the customer: Using pre-production
programs, quantitatively confirm that the selected
correction will resolve the problem. (Verify that the
correction will actually solve the problem.)
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8D PROBLEM SOLVING
• D6: Define and Implement and Corrective Actions:
Define and Implement the best corrective actions.
• D7: Prevent Recurrence: Modify the management
systems, operation systems, practices, and procedures to
prevent recurrence of this and all similar problems.
• D8: Congratulate Your Team: Recognize the collective
efforts of the team. The team needs to be formally
thanked by the organization.
MULTI-STATE
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20151207_v001_msamc_courseware_quality_ppt4 found in Resources by
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Multi-State
Advanced Manufacturing
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US DOL SPONSORED TAACCCT GRANT: TC23767
RELEASE DATE
VERSION
PAGE
12/3/2015
v 001
44 of 44
PRIMARY DEVELOPER – Janaina Gasparelo, Henry Ford College
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