Guest lecture for Rutgers Graduate Statistical Quality Control 8/7/2012

1. SQC Guest Lecture
Introduction

2. Goals for Today
Show you to see and (perhaps) solve problems differently

3. • Academics
About Me
– MS Industrial Engineering Rutgers University
– BS Electrical & Computer Engineering Rutgers University
– BA Physics Rutgers University
• Professional
– Principal Industrial Engineer -Medrtonic
– Master Black belt- American Standard Brands
– Systems Engineer- Johnson Scale Co
• Awards
– ASQ Top 40 Leader in Quality Under 40
• Certifications
– ASQ Certified Manager of Quality/ Org Excellence Cert # 13788
– ASQ Certified Quality Auditor Cert # 41232
– ASQ Certified Quality Engineer Cert # 56176
– ASQ Certified Reliability Engineer Cert #7203
– ASQ Certified Six Sigma Green Belt Cert # 3962
– ASQ Certified Six Sigma Black Belt Cert # 9641
– ASQ Certified Software Quality Engineer Cert # 4941
• Publications
– Going with the Flow- The importance of collecting data without holding up your processes- Quality Progress March
2011
– "Numbers Are Not Enough: Improved Manufacturing Comes From Using Quality Data the Right Way" (cover story).
Industrial Engineering Magazine- Journal of the Institute of Industrial Engineers September (2011): 28-33. Print

4. Agenda
18:00 18:20 Introduction
18:20 18:40 Measure
18:40 19:00 Define
19:00 19:20 Brainstorm
19:20 19:40 Break
19:40 20:00 Depict the Data
20:00 20:20 Make Control Charts
20:20 20:40 Process Mapping
20:40 21:00 Map the process
21:00 21:20 Analyze
21:20 21:55 Conclusion
Todays slides are available on Sakai
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5. So Lets Get Moving

6. What is a Process?
• Formal Definition
– A systematic series of actions directed to some end
• Practical Definition
– Any Verb Noun Combination
• Eat Sandwich
• Read Book
• Attend Conference
• Implications of Practical Definition
– Same Tools Techniques and Methods of the Lean Six Sigma
Methodologies can be used for virtually anything
Inputs Outputs
• People • Products
• Materials Process • Hardware
• Methods • Sequence of • Software
• Mother Nature
Value Added • Systems
• Management
• Measurement
Steps • People
System • Services

7. Lean Tool Kit
• 5S-
– Sort
– Straighten
– Shine
– Standardize
– Sustain
• Value Stream Mapping
• Kanban
• Poka-yoke
• Kaizen <- mean continuous improvement

8. Six Sigma Tool Kit
• DMAIC
– Define
– Measure
– Analyze
– Improve
– Control
• SIPOC Diagrams
• Statistical Process Control
• 5 Whys

9. The analogy
The task is to undo a bolt.
Solution 1- Ratchet and Socket
Solution 2- Open Ended /Box Wrench
Solution 3- Vice Grips
Which is Correct?

10. The Answer
• It depends.
– There are certain applications that demand a open
ended wrench
– Others require a socket
– Finally there are situations that require vice grips
• Most cases all three solutions will work
• The same is true for solving Continuous
Improvement problems

11. Types of Statistics
• Descriptive Statistics
– Present data in a way that will facilitate understanding
• Inferential Statistics
– Analyze sample data to infer properties of the
population from which the sample is drawn
• Statistical Significance Does not Mean actual significance.
– (See US Supreme Court Matrixx Initiatives, Inc. v.
Siracusano

12. Normal Distribution
• Also known as Gaussian, Laplace–Gaussian or
standard error curve
• First proposed by de Moivre in 1783
• Independently in 1809 by Gauss
All Normal Distributions Defined by two things
1. The Average µ
2. The Standard Deviation σ
Page 143

13. Area Under the Curve
(c) Probabilities and numbers of standard deviations
Shaded area = 0.683 Shaded area = 0.954 Shaded area = 0.997
68% chance of falling 95% chance of falling 99.7% chance of falling
between and between and between and

14. Effect of Changing Parameters
(a) Changing (b) Increasing
shifts the curve along the axis increases the spread and flattens the curve
1 =6
1 = 2= 6
2= 12
140 160 180 200 140 160 180 200
1 = 160 2 =174 1 = 2 =170

15. What is Process Sigma?
Before Customer
Mean Specification
3
A 3 process
1 Defects (3 standard
2 deviations fit
3 between target and
spec)
A 6 process
Mean Customer
Specification
After 1
2 No Defects!
6 3
4
5
6
15

17. So what are we going to do?
• We are going to apply DMAIC (Define Measure
Analyze Improve Control) to the experience of
going to Starbucks

18. About Starbucks
• Founded 1971, in Seattle’s Pike Place Market.
Original name of company was Starbucks
Coffee, Tea and Spices, later changed to
Starbucks Coffee Company.
• In United States:
– 50 states, plus the District of Columbia
– 7,087 Company-operated stores
– 4,081 Licensed stores

19. ?

20. SQC Guest
Define

21. What is Quality?
– Dictionary Definition
1. a distinguishing characteristic, property, or attribute
2. the basic character or nature of something
3. a trait or feature of personality
4. degree or standard of excellence, esp a high standard
5. (formerly) high social status or the distinction associated
with it
6. musical tone colour; timbre
7. logic the characteristic of a proposition that is dependent
on whether it is affirmative or negative
8. phonetics the distinctive character of a vowel,
– Joseph Juran - > "fitness for intended use"
– W. Edwards Deming -> "meeting or exceeding
customer expectations."

22. What is Critical To Quality?
• What is important to your customer?
• What will delight or excite them?
• What are the hygiene factors?
• These are things that have a direct and
significant impact on its actual or perceived
quality.

23. How do move beyond Brainstorming?
• Nominal Group -> when individuals over power a
group
• Multi-Voting -> Reduce a large list of items to a
workable number quickly
• Affinity Diagram -> Group solutions
• Force Field Analysis -> Overcome Resistance to
Change
• Tree Diagram -> Breaks complex into simple
• Cause- Effect Diagram -> identify root causes

24. Nominal Group Technique
• A brainstorming technique that is used when
some group members are more vocal then
others and encourages equal participation
Page 114

25. Nominal Group Procedure
1. Team Leader Selected
2. Individuals Brainstorm for 10-15 minutes
without talking. Ideas are written down
3. Round Robin each team member reads idea
and it is recorded by the team leader. There
is no discussion of ideas.
4. Once all ideas are recorded discussion begins

26. Multi-Voting
• Multi-voting is a group decision-making
technique used to reduce a long list of items
to a manageable number by means of a
structured series of votes
Page 87

27. Multi-Voting Procedure
1. Develop a Large Group Brainstormed list
2. Assign a letter to each item
3. Each team member votes for their top 1/3 of
ideas.
4. Votes are tallied
5. Eliminate all items receiving less than N votes
(rule of thumb 3)
6. Repeat voting until there are ~4 items left

28. Multi-Voting Example

29. Affinity Diagrams
• A tool that gathers large amounts of language
data (ideas, opinions, issues) and organizes
them into groupings based on their natural
relationships
Page 92

30. Affinity Diagram Procedure
1. Record Ideas on Post It Notes
2. Randomize Ideas Together
3. Sort Ideas into Related Groups
4. Create Header Card
5. Record Results

31. Affinity Diagram Example
1. Randomize Ideas Together 2. Group Ideas
4. Put it Together
3. Create Headers

32. Force Field Analysis
• Is a method for listing, discussing, and
assessing the various forces for and against a
proposed change. It helps to look at the big
picture by analyzing all of the forces impacting
on the change and weighing up the pros and
cons.
Page 109

33. Force Field Procedure
1. Draw a large letter t
2. At the top of the t, write the issue or problem
3. At the far right of the top of t write the ideal state you wish
to obtain
4. Fill in the chart
– List internal and external factors advancing towards the ideal state
– List forces stopping you from obtaining the ideal state

34. Force Field Example

35. Tree Diagram
• Tree diagrams help link a task’s overall goals
and sub-goals, and helps make complex tasks
more visually manageable. Accomplished
through successive steps digging into deeper
detail.
Page 124

36. Tree Diagram Procedure
1. Identify the Goal
2. Generate Tree Headings (Sub Goals)
– ~5 slightly more specific topics that are related to
the general goal
– Place them horizontally on post it notes
horizontally under goal
3. Generate Branches of sub goals as needed
4. Record the results

37. Tree Diagram Example

38. Cause and Effect Diagram
(Fishbone or Ishikawa Diagram)
• Is a tool that helps identify, sort, and display possible
causes of a specific problem or quality characteristic. It
graphically illustrates the relationship between a given
outcome and all the factors that influence the outcome.
Page 97

39. Cause and Effect Procedure
1. Identify and Define the Effect
2. Draw the Fishbone Diagram
– Place Effect as the Head of the fish
3. Identify categories for the main causes of the
effect or use the standard ones (Man,
Machine, Methods, Materials,
Measurements, Mother Nature)
4. Add causes to the categories
5. Add increasing detail to describe the cause

40. Cause and Effect Example
Generic Format 1. Identify Categories
2. Add Causes 3. Add Details

41. Now Apply It!
• Divide yourself into 6 Groups
– Group 1- Nominal Group
– Group 2- Multi-Voting
– Group 3- Affinity Diagrams
– Group 4- Force Field Analysis
– Group 5- Tree Diagram
– Group 6- Cause and Effect Diagram (What Causes a
Bad Cup of Coffee)
• Solve the problem “What Makes a Quality Coffee
Experience?”

42. SQC Guest
Measure

43. Types of Data
 Variable / Continuous Data
• Attribute / Discrete Data  Individual unit can be measured on
– Individual unit categorized into a a continuum or scale Examples:
classification. Examples: • Length
• Counts or frequencies of occurrence • Volume
(# of errors, # of units) • Time
• Size
• Categories (good/bad, pass/fail,
• Width
low/medium/high)
• Pressure
• Characteristics (locations, shift #, • Temperature
male/female) • Thickness
• Groups (complaint codes, error  Can have almost any numeric value
codes, problem type)
 Can be meaningfully subdivided
– Finite number of values is possible into finer increments
– Cannot be subdivided meaningfully
Page 110

44. Data Type – Why is this
important?
Data type is a key driver of your Project Strategy
 
Attribute / Discrete Data Variable / Continuous Data
 Requires larger sample size • More analysis tools available
 Usually readily available • Smaller sample size needed
 To see variation you stratify • Higher confidence in results
• To see variation, you can also
Pareto Chart
100%
80%
look at the distribution
60%
Dotplot Histogram
40%
20%
0%
FM OD ID Burr
Control Chart
Control Chart
P Chart of Resolved
for Individuals
4%
0.4 % Defective 1
Descriptive Statistics
1
Summary for Mystery
UCL=0.3539
3%
0.3
A nderson-D arling N ormality Test
A -S quared
P -V alue <
27.11
0.005
Individuals Chart
Proportion
_ 2%
M ean
S tDev
V ariance
100.00
32.38
1048.78
4%
0.2 P=0.1972 S kew ness
Kurtosis
N
0.00716
-1.63184
500
% Defective 1
0.1 1% M inimum
1st Q uartile
M edian
41.77
68.69
104.20
3%
3rd Q uartile 130.81
40 60 80 100 120 140 160
M aximum 162.82
LCL=0.0404 95% C onfidence Interv al for M ean
0.0 0% 97.15 102.85
95% C onfidence Interv al for M edian
2%
82.78 117.66
1/29 3/5 4/9 5/14 6/18 7/23 8/27 10/1 11/5
95% C onfidence Interv al for S tDev
Week 95% Confidence Intervals
Tests performed with unequal sample sizes
Days Mean
30.49 34.53
1%
Median
80 90 100 110 120
44 0%
Days

45. So how do we translate our CTQs Into
Measurements?
• Quality Functional
Deployment (House of
Quality)
• “Whats into Hows”
Y into Y into x
From the Customer Means Something You Can Measure it`
Internally

46. What is Measurement System Analysis?
• MSA = Measurement System Analysis
• Treats measurement as a process
– Procedures
– Gages
– Fixtures and other equipment
– People
• Assesses adequacy of the measurement system
• Determines sources of variation
46 Page 188

47. So What are We Going To Measure?
– Taste (what is taste?)
• pH
• Total Dissolved Solids
– Blue Meter
– Combined Meter
• Temperature
• Conductivity
– Consistency
• Weight of the beverage

48. Go Measure!
• Create the Following Control Charts
– Group 1: Starbucks Regular Pike
– Group 2: Starbucks Decaffeinated
– Group 3: Dunkin Donuts Regular
– Group 4: Dunkin Donuts Decaffeinated
– Group 5: Starbucks Regular Blond
– Group 6: Starbucks Regular Dark

49. So How Do We Display the Data?
• Dot Plot
• Run Chart
• Box Whisker Plot
• CUSUM
• EWMA
• Scatter Diagrams
• Pareto Charts

50. Box Plot
(Box and Whisker Diagram)
• Is a graphic depiction of groups of
numerical data through their five-
number summaries: the smallest
observation (sample minimum), lower
quartile (Q1), median (Q2), upper
quartile (Q3), and largest observation
(sample maximum). A boxplot may also
indicate which observations, if any,
might be considered outliers.
Page 164

51. Control Chart
• Time plot of data with Center Line (mean average) & Control Limits
– Control limits are based on actual process variation (Not specs!)
• UCL = X-bar (i.e., data mean) + 3 ; LCL = X-bar - 3
40
35 Upper Control Limit
(UCL)
30
25
Center Line
(X-bar)
20
Lower Control Limit
15
(LCL)
10
0 5 10 15 20 25
 Voice Of the Process (X-bar, UCL, LCL are based on actual data!):
 Control Limits and Center Line reflect process variation and stability
 A process is predictable (stable) when data points vary randomly within control
limits. Referred to as a process “in control.”
51 Page 110

52. Before Using Control Charts Check for Normality
Histogram of Normal Probability Plot of Normal
100 Normal
99.9
Mean 168.0
StDev 24.00
80 99
N 500
AD 0.418
95 P-Value 0.328
90
60
Frequency
80
70
Percent
60
40 50
40
30
20
20 10
5
1
0
90 120 150 180 210 240
Normal 0.1
50 100 150 200 250
Normal
Histogram of Positive
200 Probability Plot of Positive
Normal
99.9
Mean 168.0
StDev 24.00
150 99
N 500
AD 46.489
95 P-Value <0.005
Frequency
90
100 80
70
Percent
60
50
40
30
50 20
10
5
0 1
150 180 210 240 270 300
Positive 0.1
100 150 200 250 300
Positive
Histogram of Negative Probability Plot of Negative
Normal
99.9
250 Mean 168.0
StDev 24.00
99
N 500
200 AD 44.491
95 P-Value <0.005
90
Frequency
80
150 70
Percent
60
50
40
100 30
20
10
5
50
1
0 0.1
0 30 60 90 120 150 180 0 50 100 150 200 250
Negative
Negative
Page 173

53. Control Chart Decision Tree
Variable (continuous) Attribute (discrete)
What Type Of Data?
Counting
Data Collected In
Specific Defects or
Groups or Individuals?
Defective Items?
GROUPS INDIVIDUAL
(Averages) VALUES Specific Defective
(n>1) (n=1) Types Of Items
“Defects”
X-Bar R (Means w/Range) Individuals (I Chart)
X-Bar S (Means w/St Dev) With Moving Range (I-MR) You can count only You can count how
defects many are bad and
how many are good
NOTE: X-Bar S is appropriate
Poisson Distribution Binomial Distribution
for subgroup sizes of > 10
Area of
Constant
Opportunity Constant
Sample Size?
In Each Sample
Size?
NO YES NO YES
u Chart c Chart or p Chart np Chart or
u Chart p Chart
Page 110

54. I-MR
Page 317

55. Interpretation

56. Now Apply it
• Create the Following Control Charts
– Group 1: I Chart for pH
– Group 2: I Chart for Temperature
– Group 3: I Chart for TDS- blue
– Group 4: I Chart for Weight
– Group 5: I Chart for Conductivity
– Group 6: I Chart for TDS - Combined

57. SQC Guest
Mapping The Process

58. What is a Process?
• A Process
• Remember “Verb-Noun Combination”

59. Graphically Presenting a Process
• Six Sigma
– SIPOC
– Process Mapping
• Lean
– Value Stream Map
Let the Picture do the talking

60. Suppliers Inputs Process Outputs
Customers (SIPOC)
• Is a high-level picture of a process that depicts
how the given process is servicing the
customer.
Page 51

61. SIPOC Procedure
1. Agree to the name of the process. Use a Verb + Noun format (e.g.
Recruit Staff).
2. Define the Outputs of the process. These are the tangible things
that the process produces (e.g. a report, or letter).
3. Define the Customers of the process. These are the people who
receive the Outputs. Every Output should have a Customer.
4. Define the Inputs to the process. These are the things that trigger
the process. They will often be tangible (e.g. a customer request)
5. Define the Suppliers to the process. These are the people who
supply the inputs. Every input should have a Supplier. In some
“end-to-end” processes, the supplier and the customer may be
the same person.
6. Define the sub-processes that make up the process. These are the
activities that are carried out to convert the inputs into outputs.
They will form the basis of a process map.

62. SIPOC Symbols
• Suppliers: The individuals, departments, or organizations that
provide the materials, information, or resources that are worked on
in the process being analyzed
• Inputs: The information or materials provided by the suppliers.
Inputs are transformed, consumed, or otherwise used by the
process (materials, forms, information, etc.)
• Process: The macro steps (typically 4-6) or tasks that transform the
inputs into outputs: the final products or services
• Outputs: The products or services that result from the process.

63. SIPOC Example

64. Process Maps
• Are a graphical outline or schematic drawing
of the process to be measured and improve.
Page 128

65. Process Map Procedure
1. Identify the process to be studied, identify
boundaries and interfaces
2. Determine Various Steps in the process
3. Build the Sequence of Steps
4. Draw the formal chart with process map
5. Verify Completeness

66. Process Map Symbols

67. Process Map Example

68. Value Stream Mapping (VSM)
• Special type of flow chart that uses symbols
known as "the language of Lean" to depict
and improve the flow of inventory and
information
• Purpose
– Provide optimum value to the customer through
a complete value creation process with minimum
waste
Page 24

69. VSM Procedure
Before doing any steps, determine who owns the process!
1. Identify Process Customers (Y Process Output
Measures)
2. Identify Process Suppliers
3. Map the Material (Process) Flow
• Process General Steps
• Queue or Staging Areas
4. Identify Process Information Systems
5. Map the Information Flow
6. Identify Common Data
7. Gather the Data

70. Common VSM Symbols
Electronic Communication Dotted Line represents
Information Flow manual process connection
Box with Jagged top
represents interaction with
Manual Information Flow Customer customer or supplier.
Red Box and Rectangle Block represents a process
Production
Control represents information MSD Cust. Srvc. step that is performed.
system used.
MRP
70

71. Determine Process Cycle Times &
Identify Value Added Steps
VA
NVA
Value Added Steps are anything that the customer is willing to pay for

72. VSM Example

73. Links to the Videos
• Latte : http://youtu.be/HyAAxMEdB24
• Frap : http://youtu.be/3qk28eEbfc4
• Drip : http://youtu.be/IGuwC1WcjKY
• Clover : http://youtu.be/YtXClUKhLmw

74. Now Apply It!
• Graphically Depict the following
– Group 1: Process Map Latte
– Group 2: Process Map Frap
– Group 3: Process Map Drip Coffee
– Group 4: Process Map Clover
– Group 5: SIPOC for Frap
– Group 6: SIPOC for Clover

75. SQC Guest
Analyze

76. Steps in Test of Hypothesis
1. Formulate the Null and Alternate Hypothesis
2. Determine the appropriate test
3. Establish the level of significance:α
4. Determine whether to use a one tail or two tail test
5. Determine the degree of freedom
6. Calculate the test statistic
7. Compare computed test statistic against a tabled/critical
value
• Remember: tests DON’T PROVE anything.
– They gather sufficient evidence against the null hypothesis Ho
or fail to gather sufficient evidence against Ho.
76

77. Determine The Appropriate Test
• Z
– is any statistical test for which the distribution of the test statistic
under the null hypothesis can be approximated by a normal
distribution.
• T
– is any statistical hypothesis test in which the test statistic follows a
Student's t distribution if the null hypothesis is supported
• Paired T
– is a test that the differences between the two observations is 0
• ANOVA
– Is a test to determine the differences between two or more
treatments
• Chi Squared
– Is a test to determine the goodness of fit of data to a distribution
• Lots of Other Tests

78. Compare the observed test statistic with
the critical value
-Zcrit Zcrit
| Ztest | > | Zcrit | HA
| Ztest | | Zcrit | H0 H0
HA HA
78

79. Compare the observed test statistic with
the critical value
-1.96 1.96
H0
| Ztest | > | 1.96 | HA
| Ztest | | 1.96 | H0 HA HA
79

80. Compare the observed test statistic with
the critical value (1 Tail)
Ztest > 1.645 HA 1.645
Ztest 1.645 H0 H0
HA
80

81. p-value
• p-value is the probability of getting a value of the test
statistic as extreme as or more extreme than that observed
by chance alone, if the null hypothesis H0, is true.
• It is the probability of wrongly rejecting the null
hypothesis if it is in fact true
• It is equal to the significance level of the test for which
we would only just reject the null hypothesis
81

82. Purpose of ANOVA
• Use one-way Analysis of Variance to test when the mean of
a variable (Dependent variable) differs among two or more
groups
– For example, compare whether systolic blood pressure differs
between a control group and two treatment groups
• One-way ANOVA compares two or more groups defined
by a single factor.
– For example, you might compare control, with drug treatment
with drug treatment plus antagonist. Or might compare control
with five different treatments.
• Some experiments involve more than one factor. These
data need to be analyzed by two-way ANOVA or Factorial
ANOVA.
– For example, you might compare the effects of three different
drugs administered at two times. There are two factors in that
experiment: Drug treatment and time.

83. Test Statistic in ANOVA
• F = Between group variability / Within group variability
– The source of Within group variability is the individual
differences.
– The source of Between group variability is effect of independent
or grouping variables.
– Within group variability is sampling error across the cases
– Between group variability is effect of independent groups or
variables
83

84. ANOVA is Appropriate if:
• Independent random samples have been taken from each population
• Dependent variable population are normally distributed (ANOVA is
robust with regards to this assumption)
• Population variances are equal (ANOVA is robust with regards to this
assumption)
• Subjects in each group have been independently sampled
84

85. ANOVA Hypothesis
• Ho: 1= 2= 3= 4
Where
• 1= population mean for group 1
• 2 = population mean for group 2
• 3 = population mean for group 3
• 4 = population mean for group 4
• H1 = not Ho
85

86. ANOVA Compare the Computed Test
Statistic Against a Tabled Value
• α = .05
• If Ftest > FCritcal Reject H0
• If Ftest <= FCritcal Can not Reject H0
Excel is very nice and does it for us!

87. Now we Are going to Apply ANOVA to
Your Data
• Is there Difference Between Starbucks and
Dunkin Donuts? pH? TDS? Conductivity?
• Is there Difference Between decaffeinated and
Regular? pH? TDS? Conductivity?
• Is there Difference Between Different
Starbucks Roasts? pH? TDS? Conductivity?

88. SQC Guest
Conclusion

89. Takeaways
• Industrial Engineering is focused on solving
problems in:
– Manufacturing
– Finance
– Logistics
– Medical
– Services (including Education)
• Six Sigma is one of many tools to solve
problems

91. ASQ Greenbelt
• 100 Multiple Choice Questions
• 4 Hours
• Open Book, Open Notes *No Sample
Problems*
• No graphing calculators allowed
• Results Posted online 7-10 Days after

92. Requirements to Sit for the Exam
• Required Experience
– The Six Sigma Green Belt requires three years* of work experience in
one or more areas of the Six Sigma Green Belt Body of Knowledge.
• Minimum Expectations for a Certified Six Sigma Green Belt
– Operates in support of or under the supervision of a Six Sigma Black
Belt
– Analyzes and solves quality problems
– Involved in quality improvement projects
– Participated in a project, but has not led a project
– Has at least three years of work experience
– Has ability to demonstrate their knowledge of Six Sigma tools and
processes
* The Body of Knowledge is very broad it can be accessed at
(http://prdweb.asq.org/certification/control/six-sigma-green-
belt/bok). For Juniors and Seniors in ISE your course work counts.
Others consider course work, internships and work experience to
meet the requirement.

93. About the Course
• 11 Weekly Sessions starting the Week of 9/17
for the December 1st exam
• Purpose is to train students to pass the exam
• Currently Schedule for Monday Nights. If > 25
students register additional sections will be
added on Wednesday or Thursday
• Text Book
– Certified Six Sigma Handbook

94. Certification Cost
• Exam Preparation = $296 includes
– ASQ Student Membership - $27
– Six Sigma Greenbelt Course- $179
– Textbook - $90
• Exam Fee = $199
• Total Certification Cost $495
More Information @ www.ASQPrinceton.org

95. My Contact Information
• Brandon Theiss
– Brandon.Theiss@gmail.com
– Connect to me on LinkedIn

96. Please Complete the Survey
• https://www.surveymonkey.com/s/39N9Y9X
• Todays slides are available on Sakai