The document describes an agenda for a Rutgers Governor School event on industrial engineering and quality. The agenda includes an introduction, defining key terms, examining how to measure the quality of coffee, analyzing coffee quality data, making control charts, mapping coffee-making processes, conducting hypothesis testing, and concluding. The slides for the event are available online, as is a feedback survey.

1. Rutgers Governor School
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
9:00 9:30 Introduction
9:30 10:00 Define
10:00 10:30 What Makes a Quality Cup of Coffee
10:30 11:00 Measuring Coffee
11:00 11:30 Analyze
11:30 12:00 Making Control Charts
12:00 12:30 Lunch
12:30 13:00 Lunch
13:00 13:30 The Process
13:30 14:00 Mapping the Process
14:00 14:30 Hypothesis Testing
14:30 15:00 Conclusion
Todays slides are available at
http://www.slideshare.net/brtheiss/rutgers-governor-
school
Also Please Complete the Online Feedback Survey
https://www.surveymonkey.com/s/93CJQCV

5. So Lets Get Moving

6. What is Industrial Engineering?
• is a branch of engineering dealing with the
optimization of complex processes or systems.
• “Engineers make things. Industrial engineers
make things better.”

7. 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

8. So How do Make “it” Better
• Statistics
• Lean
• Six Sigma
• Modeling

9. 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

10. Key Descriptive Statistical Terms
Population Parameters
Size = N
Mean = m
Std. Dev. = s
Proportion = p
Sample Statistics
Size = n Mean = x
ˆ
Std. Dev.= s Prop. = p
Page 143

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

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

13. Modeling
• A mathematical model is a
description of a system using
mathematical concepts and
language.
Histogram of Time
Weibull
25 Shape 2.007
Scale 216106
N 94
20
15
Frequency
10
5
0
0 100000 200000 300000 400000 500000
Time

14. 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?

15. 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 Industrial
Engineering problems

16. A History Lesson
 Lean
 Mid 1800’s: Interchangeable parts
 1913: Moving assembly line at Ford
 Post-WW2: Toyota Develops a Production System (TPS).
 1996: James Womack documents general application of TPS and for
any organization and calls it “Lean Thinking”.
 Six Sigma
 Mid-1900s: Shewhart and Deming advocate PDCA methodology
 1980’s: Motorola developed MAIC method for reducing defects in it’s
products. Won the Baldrige Quality Award in 1988. GE applies Six Sigma
to non manufacturing
 1990’s-2000’s: Siemens, Allied Signal and others drive 6 Sigma DMAIC
top-down. Companies in many industries practice Six Sigma, some
successfully, some not (MDT dabbled in early 1990’s).
16

17. People to Know
• William Edwards Deming (October 14, 1900 – December 20, 1993) "There
is no substitute for knowledge.“
– emphasis on total quality management
• Joseph Moses Juran (December 24, 1904 – February 28, 2008) "It is most
important that top management be quality-minded. In the absence of
sincere manifestation of interest at the top, little will happen below."
- Managing for Quality
• Taiichi Ohno (February 29, 1912 – May 28, 1990) -Toyota
– Production System (TPS)- Just in Time (JIT)
• Walter Andrew Shewhart ( March 18, 1891 - March 11, 1967) -"Dr.
Shewhart prepared a little memorandum only about a page in length.
About a third of that page was given over to a simple diagram which we
would all recognize today as a schematic control chart.”
– Control Charts
Page 6

18. What is Six Sigma?
• Six Sigma seeks to improve the quality of process
outputs by identifying and removing the causes of
defects (errors) and minimizing variability in
manufacturing and business processes.
• It uses a set of quality management methods, including
statistical methods, and creates a special infrastructure
of people within the organization ("Black Belts",
"Green Belts", etc.) who are experts in these methods.
• Each Six Sigma project carried out within an
organization follows a defined sequence of steps
(DMAIC) and has quantified financial targets (cost
reduction and/or profit increase).

19. What is Sigma (s)?
4 Definitions:
A letter in the Greek alphabet s
A statistical measure of variation (standard deviation)
A measure of a process defect level
Six Sigma - an improvement methodology (DMAIC)
19

20. 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

21. 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 

22. 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

23. What is Process Sigma?
Before Customer
Mean Specification
3s
A 3s process
1s Defects (3 standard
2s deviations fit
3s between target and
spec)
A 6s process
Mean Customer
Specification
After 1s
2s No Defects!
6s 3s
4s
5s
6s
23

25. 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

26. 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

27. ?

28. Rutgers Governor School
Define

29. 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."

30. 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.

31. 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

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

33. 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

34. 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

35. 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

36. Multi-Voting Example

37. 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

38. 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

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

40. 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

41. 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

42. Force Field Example

43. 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

44. 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

45. Tree Diagram Example

46. 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

47. 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, Measur
ements, Mother Nature)
4. Add causes to the categories
5. Add increasing detail to describe the cause

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

49. 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?”

50. Rutgers Governor School
Measure

51. 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

52. 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
52 0%
Days

53. 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

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

55. Go Measure!
• Create the Following Control Charts
– Group 1: Starbucks Regular
– Group 2: Starbucks Decaffeinated
– Group 3: Dunkin Donuts Regular
– Group 4: Dunkin Donuts Decaffeinated

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

57. Dot Plot
• Is a statistical chart consisting of data points
plotted on a simple scale, typically using filled in
circles representing the frequency of observation
Page 164

58. Run Chart
• Is a graph that displays observed data in a time
sequence. Often, the data displayed represent
some aspect of the output or performance of a
manufacturing or other business process.
Page 166

59. 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

60. CUSUM
(Cumulative Sum Chart)
• Is a sequential analysis technique used for
monitoring changes

61. EWMA
(Exponential Weight Moving Average)
• Is a type of control chart used to monitor either
variables or attributes-type data using the monitored
business or industrial process's entire history of output

62. Scatter Diagrams
• Is used to display a relationship or association
between two variables
Page 167

63. Pareto Chart
• Named after Vilfredo Pareto, is a type of chart that contains both
bars and a line graph, where individual values are represented in
descending order by bars, and the cumulative total is represented
by the line.
Page 136

64. 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) + 3s; LCL = X-bar - 3s
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.”
64 Page 110

65. 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

66. 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

67. X-Bar R
Page 313

68. X-Bar S
Page 315

69. P Chart
Used to monitor the proportion of nonconforming units in a sample, where the
sample proportion nonconforming is defined as the ratio of the number of
nonconforming (defective) units to the sample size, n
Page 319

70. Np Chart
NP Chart of Wrong Answers
40
1
1
30 1
1
1 1
Sample Count
UCL=24.25
20
__
NP=15.44
10
LCL=6.63
11
1
0 1
1 6 11 16 21 26 31 36 41 46
Sample
Used to monitor the number of nonconforming units in a sample. It is an
adaptation of the p-chart and used in situations where personnel find it easier
to interpret process performance in terms of concrete numbers of units rather
than the somewhat more abstract proportion.
Page 321

71. I-MR
Page 317

72. Words Have Meaning
• Defect
– any nonconformance of the unit of product with
the specified customer requirements
• Defective
– is a unit of product which contains one or more
defects that effects the operability of the product
as determined by the customer

73. C Chart
Used to monitor "count"-type data, typically total number of nonconformities
(defects) per unit. It is also occasionally used to monitor the total number of
events occurring in a given unit of time.
Page 325

74. U Chart
used to monitor "count"-type data where the sample size is greater than one,
typically the average number of nonconformities per unit
Page 323

75. Interpretation

76. Page 369

77. Now Apply it
• Create the Following Control Charts
– Group 1: I-MR Chart for pH
– Group 2: I-MR Chart for Temperature
– Group 3: I-MR Chart for TDS
– Group 4: I-MR Chart for Weight

78. Regular Starbucks
I-MR Chart of Temp I-MR Chart of TDS
130 108.0 U C L=107.751
U C L=128.95
106.5
Individual Value
Individual Value
125
_ 105.0 _
X=122.69 X=104.5
120 103.5
102.0
LC L=116.43
LC L=101.249
115
1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10
O bser vation O bser vation
8 4 U C L=3.993
U C L=7.696
6 3
M oving Range
M oving Range
4 2
__ __
M R=2.356 M R=1.222
2 1
0 LC L=0 0 LC L=0
1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10
O bser vation O bser vation
I-MR Chart of PH I-MR Chart of Mass
5.0 U C L=176.60
U C L=4.815 175
Individual Value
Individual Value
4.5 170
_
_ 165 X=164.78
X=4.135
4.0
160
155
3.5 LC L=3.455 LC L=152.96
1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10
O bser vation O bser vation
U C L=0.8350 16
0.8 U C L=14.52
12
M oving Range
M oving Range
0.6
0.4 8
__ __
M R=0.2556 M R=4.44
0.2 4
0.0 LC L=0 0 LC L=0
1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10
O bser vation O bser vation

79. Decaf Starbucks
I-MR Chart of Temp I-MR Chart of PH
1 5
1 U C L=4.929
U C L=122.36
122
Individual V alue
Individual V alue
4
120
_ _
118 X=118.13 3 X=2.946
116 2
114 LC L=113.90 1 LC L=0.963
1
1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10
O bser vation O bser vation
U C L=5.191 2.4 U C L=2.436
4.8
M oving Range
M oving Range
1.8
3.6
2.4 1.2
__ __
M R=1.589 M R=0.746
1.2 0.6
0.0 LC L=0 0.0 LC L=0
1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10
O bser vation O bser vation
I-MR Chart of Mass I-MR Chart of TDS
1
U C L=84.38 200
80
Individual V alue
Individual V alue
190
_ 180 U C L=180.62
70 X=69.81
_
170 X=168.5
60
160
LC L=55.24 LC L=156.38
1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10
O bser vation O bser vation
1
20
U C L=17.90
20
15
M oving Range
M oving Range
15 U C L=14.88
10
10
__
5 M R=5.48 __
5 M R=4.56
0 LC L=0 0 LC L=0
1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10
O bser vation O bser vation

80. Regular Dunkin Donuts
I-MR Chart of Temp I-MR Chart of TDS
144 180 1
U C L=143.28
U C L=169.16
Individual V alue
Individual V alue
141 165
_ 150 _
138 X=137.77 X=147.13
135
135
LC L=125.09
LC L=132.27 120 1
132 1
1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8
O bser vation O bser vation
U C L=6.768 30
U C L=27.07
6.0
M oving Range
M oving Range
4.5 20
3.0
__ __
M R=2.071 10
M R=8.29
1.5
0.0 LC L=0 0 LC L=0
1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8
O bser vation O bser vation
I-MR Chart of PH I-MR Chart of Mass
U C L=3.273 140
3 U C L=135.25
Individual V alue
Individual V alue
130
2 _
X=1.81 _
120
X=118.22
1
110
LC L=0.347
100 LC L=101.20
0
1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8
O bser vation O bser vation
2.0 U C L=20.91
20
U C L=1.797
1.5
M oving Range
M oving Range
15
1.0 10
__ __
M R=0.55 M R=6.4
0.5 5
0.0 LC L=0 0 LC L=0
1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8
O bser vation O bser vation

81. Decaf Dunkin Donuts
I-MR Chart of Temp I-MR Chart of TDS
122 U C L=122.357 140 U C L=140.33
Individual Value
Individual Value
120
_ 135 _
118 X=117.988 X=134.25
116
130
114 LC L=128.17
LC L=113.618
1
1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8
O bser vation O bser vation
6.0 8
U C L=7.468
U C L=5.368
4.5 6
M oving Range
M oving Range
3.0 4
__ __
1.5 M R=1.643 2 M R=2.286
0.0 LC L=0 0 LC L=0
1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8
O bser vation O bser vation
I-MR Chart of PH I-MR Chart of Mass
U C L=4.3083 300
U C L=285.9
Individual Value
4.2
Individual Value
250
_ _
X=4.0575 200 X=201.6
4.0
150
LC L=117.2
3.8 LC L=3.8067
100
1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8
O bser vation O bser vation
0.3 U C L=0.3081 100 U C L=103.6
M oving Range
M oving Range
75
0.2
50
__ __
0.1 M R=0.0943 M R=31.7
25
0.0 LC L=0 0 LC L=0
1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8
O bser vation O bser vation

82. Rutgers Governor School
Mapping The Process

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

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

85. 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

86. 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.

87. 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.

88. SIPOC Example

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

90. 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

91. Process Map Symbols

92. Process Map Example

93. 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

94. 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

95. 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
95

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

97. VSM Example

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

99. Now Apply It!
• Create a SIPOC, Process Map or Value Stream
Map for the “make drink” process
– Latte
– Frappuccino
– Drip Coffee
– Clover

100. Rutgers Governor School
Analyze

101. What is an Hypothesis Test?
 Hypothesis Test determines which is more likely to be true:
Null hypothesis (Ho) or Alternative hypothesis (Ha)
 Ho always starts with “There is no difference between….”
p-Value: Probability Ho is true given the evidence
If p is low: Reject Ho and accept Ha
Example:
Null hypothesis (Ho): Defendant is guilty (not a Key X)
Alternative Hypothesis (Ha): Defendant is not guilty (A Key X)
p-Value: Probability defendant is not guilty given the evidence
If p is small (reasonable doubt): Reject Ho and conclude defendant is not guilty (Key X!)
101

102. 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.
102

103. Formulate the null and alternative hypotheses.
a. NULL HYPOTHESIS (H0): H0 specifies a value for the population parameter
against which the sample statistic is tested. H0 always includes an
equality.
b. ALTERNATIVE HYPOTHESIS (Ha): Ha specifies a competing value for the
population parameter.
 Ha is formulated to reflect the proposition the researcher wants to verify.
 Ha always includes a non-equality that is mutually exclusive of H0.
 Ha is set up for either a 1-tailed test or a 2-tailed test.

104. 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

105. Choose α, our significance level
It really depends on what we are testing
– α = 0.05
– α = 0.01
– Type I error
105

106. Find the critical value of the test statistic
• Standard normal table
• Student’s t distribution table
• Two-sided vs. one-sided
• F Distribution Table
• Chi Square Distribution Table
106

107. Two-sided tests  Zα/2
107

108. One-sided tests  Zα
108

109. F Table

110. Compare the observed test statistic with
the critical value
-Zcrit Zcrit
| Ztest | > | Zcrit | HA
| Ztest |  | Zcrit | H0 H0
HA HA
110

111. Compare the observed test statistic with
the critical value
-1.96 1.96
H0
| Ztest | > | 1.96 | HA
| Ztest |  | 1.96 | H0 HA HA
111

112. Compare the observed test statistic with
the critical value (1 Tail)
Ztest > 1.645 HA 1.645
Ztest  1.645 H0 H0
HA
112

113. 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
113

114. The Chi Square Test
• A statistical method used to determine goodness
of fit
– Goodness of fit refers to how close the observed data
are to those predicted from a hypothesis
• Note:
– The chi square test does not prove that a hypothesis is
correct
• It evaluates to what extent the data and the hypothesis have
a good fit

115. 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.

116. What Does ANOVA Do?
• ANOVA involves the partitioning of variance of the
dependent variable into different components:
– A. Between Group Variability
– B. Within Group Variability
• More Specifically, The Analysis of Variance is a method
for partitioning the Total Sum of Squares into two
Additive and independent parts.
116

117. 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
117

118. 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
118

119. ANOVA Hypothesis
• Ho: m1 = m2 = m3 = m4
Where
• m1 = population mean for group 1
• m2 = population mean for group 2
• m3 = population mean for group 3
• m4 = population mean for group 4
• H1 = not Ho
119

120. ANOVA Compare the Computed Test
Statistic Against a Tabled Value
• α = .05
• If Ftest > FCritcal Reject H0
• If Ftest <= FCritcal Can not Reject H0
Fα is found in table on 374
or using Excel FINV function