Transcript Welcome to Lean Six Sigma Yellow Belt Training!

The DMAIC Lean Six Sigma Project
and Team Tools Approach
Measure Phase
1
Lean Six Sigma Combo/Black Belt
Training! Agenda – Measure Phase
Welcome Back, Brief Review
Process Thinking, Mapping, and Analysis
Measurement System Analysis
Sigma Level, Baseline Metrics, Types of Data
Capability Analysis
Introduction to Minitab
Pareto Analysis
Theories of Xs and Cause and Effect
Data Collection Plan and Sampling
Lessons Learned / Measure Phase Conclusions
Wrap-Up / Teach-Coach Practice / Quiz
2
Measure Objectives (pg. 8-11)
•
Identify the Project Y
•
Define the performance standards for Y, and its baseline (current state)
performance
•
Clarify understanding of specification limits as well as defect and
opportunity definitions
•
Validate the measurement system (MSA)
•
Collect the data as needed
•
Characterize the data using basic tools and capability
•
Begin funneling the X’s that affect the Y
•
Measure…what is the current state/performance level and potential causes
3
Why spend so much time
in the Measure phase?
“When you can measure what you are
speaking about, and express it in numbers,
you know something about it; but when you
cannot measure it, when you cannot express
it in numbers, your knowledge is of a meager
and unsatisfactory kind…”
Lord Kelvin
“If you can’t measure it, you can’t manage it.”
Peter Drucker
4
Why Do We Measure?
• To thoroughly understand the current state of our
process and collect reliable data on process
inputs that you will use to expose the underlying
causes of problems
• To know “where you are” – the extent of the
problem
• To understand and quantify the critical inputs (xs)
that we believe (theories) are contributing to our
problem (Ys)
5
Lean Six Sigma
DMAIC Phase Objectives
•
Define… what needs to be improved and why
•
Measure…what is the current state/performance level and potential causes
•
Analyze…collect data and test to determine significant contributing causes
•
Improve…identify and implement improvements for the significant causes
•
Control…hold the gains of the improved process and monitor
6
LSS PROJECT FOCUS
Process
Characterization
Define
The right project(s), the right team(s)
r
Response variable, Y
r
Y
Analyze
Process
Optimization
r
Process
Problems and
Symptoms
Process outputs
r
Measure
q
Independent variables, Xi
Process inputs
The Vital Few determinants
Causes
q
Mathematical relationship
q
q
Improve
Control
q
X’s
Goal: Y = f ( x )
7
Measure Phase:
Process Mapping
8
The Basic Philosophy of Lean Six Sigma
•
•
•
•
All processes have variation and waste
All variation and waste has causes
Typically only a few causes are significant
To the degree that those causes can be understood
they can be controlled
• Designs must be robust to the effects of the remaining
process variation
• This is true for products, processes, information
transfer, transactions, everything
• Uncontrolled variation and waste is the enemy
9
Remember - What is Six Sigma…
•A high performance measure of excellence
•A metric for quality
•A business philosophy to improve customer satisfaction
•Focuses on processes and customers
•Delivers results that matter for all key stakeholders
•A tool for eliminating process variation
•Structured methodology to reduce defects
•Enables cultural change, it is transformational
10
Why Process Thinking?
Allows criticism without blaming people
Allows shared understanding of how things work
Helps manage complexity
Provides focus within context
Helps to manage scope of project
Identification of team members
Understand inputs / outputs - leads to measurement
11
High Level Process Map - SIPOC
Process Name
Supplier-Inputs-Process-Outputs-Customer
….………………………………………..…….
….……………………………………………...
….……………………………………………...
….……………………………………………...
…………………………………………………
…………………………………………………
…………………………………………………
12
High Level 1 Box Examples
Inputs
Customer Name
Customer ID
Bill to
Ship to
Credit status
Quoting Job
Outputs
Time to quote
Number of contacts
Quote accuracy
13
High Level Process Flow
INPUTS
PROCESS
OUTPUTS
Specialty available
Chart available
Patient assessment
MD orders consult
Order in chart—complete
Reason for consult
Order flagged
Order placed in correct area
Legible order
Computer system working
Unit Sec enters
consult
Consult stamp on chart
Consult documented in CERNER
Contact information
Call schedules
Assigned vs. Group call schedule
Unit Sec calls consult
Specific MD notified
Answering service notified
MD on-call notified
24 hour chart check
RN reviews chart for
completeness
Consult not met
Failure to meet consult is noted by RN
24 hr chart check signature
RN realizes need to reconsult
RN informs Unit Sec
to reconsult
Unit Sec attempts to reconsult
Contact information
Call schedules
Assigned vs. Group call schedule
Unit Sec/RN verifies
with exchange / office
Office or exchange notifies physician
14
Lean Six Sigma
Project and Team Basic Tools
Process Flow Chart (pg. 33-44)
A visual display of the key steps and flow of a
process, also called a process map. Usually standard
symbols are used to construct process flow charts.
These include boxes (or rectangles) for specific steps,
diamonds for decision points, ovals for defined starting
and stopping points, and arrows to indicate flow.
Processes can include providing a service, making or
delivering products, information sharing, design, etc. –
Should represent the current as-is state of the process!
15
Process Mapping (pg. 33-44)
• A process is a sequence of steps or
activities using inputs to produce an output
(accomplish a given task).
• A process map is a visual tool that
documents and illustrates a process.
• Several styles and varying levels of detail
are used in Process Mapping. Most
common and useful styles are SIPOC,
Flow Diagrams, Box Step, and Value
Stream Maps.
16
Process Mapping
• The team should start with the observed,
current, as-is process.
• Start high-level, and work to the level of
detail necessary for your project (key
inputs).
• As inconsistencies are discovered, the
team can develop a future state or shouldbe process map to improve the key xs and
the overall output (Y) of the process.
17
Levels of Process Mapping
How Low Can You Go?
Level 1: Core Business Processes
Level 2: Processes
Level 3: Subprocesses
Level 4: Activities/Steps
Level 5:Task
18
Patient Care Core Business Process
Admissions
Treatment &
Invervention
Discharge
Medication administration
Physical therapy
Diagnostic and therapeutic imaging
intervention
Lab testing
Cardiology treatment intervention
Pulmonary treatment intervention
Surgical intervention
IV therapy treatment
Nutritional support
Discharge teaching
Physiological monitoring
Implementation of treatments
Communication
Pain management
Billing
19
How Low Can Should You Go?
• Decompose the process until it becomes
unnecessary to go any farther
– Accountability is identified
– Responsibility falls outside the process boundaries
– Root cause becomes evident
– The time required to measure the process exceeds
the time required to perform it
20
Flow Diagrams - Concept
(For Complete List, see: PowerPoint - Shapes - Flowchart)
Activity / Step
Connector
Decision
Off-page Connector
Flow lines
Database
Terminal / End
Document
21
Process Flow - Symbols
Follow the standard symbols; don’t
make up your own.
People who follow your process flow
should be able to understand your work
and documents.
22
Don’t mess with it.
YES
Does the
thing
work?
NO
Did you
mess
with it?
NO
YES
Hide it!
Does
anyone
know?
NO
YES
You poor dummy!
NO
Can you
blame
someone
else?
You big dummy!
Will you
get in
trouble?
NO
YES
NO PROBLEM
Toss it!
23
Suspected Bleeding Disorder
Sample Process Map
H&P
yes
Definitive
Family
History?
Focused Testing
yes
(see list a)
no
no
Symptomatic
Patient or Family
History?
END
yes
Screening Tests:
CBC
PT
PTT
PFA
Thrombin Time
Positive
Test
Result?
no
yes
Further
Testing
Required?
no
Confirmed Dx
yes
Positive
Test
Result?
no
Release or worku
for other Dx
Positive yes
Focused Testing
Screening Test (see list b)
Result?
Review Screening
no
Test Results
24
http://www.qualitym
ag.com
25
Process Flow Chart
Lean Six Sigma Project Selection
A Gap Exists
Define
Potential
Project
Draft Problem
Statement
Redefine
Project Scope
Identify the
Metrics
No
Reconsider
Project
Determine the
Outputs (Y)
Two
Or Fewer
Outputs?
No
Yes
Charter and
Launch
Project
Yes
Meets
Six Sigma
Criteria?
Calculate
Benefits
Quantify the
Opportunity
http://www.oregon.gov
27
A Flow Chart of Process Mapping
Start
Assemble
the Team
Define the
Process
Scope
Macro
Map?
Yes
Create
Process
Flow
Diagram
Identify
VA/NVA
Steps
Find the
Hidden
Factory
Revise
and
Update
Observe
and
Verify
List
Process
Capability
Build a
Detailed
Map
Identify
the
Specs.
Identify
X’s and
Y’s
No
Draft a
Macro
Map
Tools: PowerPoint, Excel, Visio, Process Model
28
Additional Process Mapping
Techniques
• Swim lanes (pgs. 43-44)
• Value stream mapping (pgs. 45-51)
• Time Value Map (pgs. 52-53)
29
Process Mapping Analysis
Detailed Analysis of Process Delays or Errors:
Identifying process delays or potential errors is
an important analyze phase activity. Going
into greater detail in identifying the type and
source of delay or error will help to more
clearly define the root cause and thereby
produce a more robust solution and overall
improvement.
30
Process Mapping Analysis
Types of Process Delays or Errors:
•
•
•
•
•
•
•
31
Gaps
Redundancies
Implicit or unclear requirements
Bottlenecks
Hand-offs
Conflicting objectives
Common problem areas
Process Mapping Analysis
Gaps
– Responsibilities for certain process steps are
unclear, not understood, easy to “skip”
– Process seems “unfocused,” goes off track in
delivering what the customer needs
– Excessive variation
32
Process Mapping Analysis
Redundancies
– Actions or steps are duplicated
– Different groups repeat actions that are done
somewhere else, and they are not aware of the
repeat actions occurring
– Excessive checking (non-value adding)
33
Process Mapping Analysis
Implicit or unclear requirements
– “Word of mouth” instructions, not formally
documented; assumptions
– Operational definitions are not noted; different
groups interpret definitions and instructions
differently
– Unclear measurement system
34
Process Mapping Analysis
Bottlenecks
– A “slow down” of work flow
– Multiple inputs may feed into a process step,
which is then delayed
– Output of entire process may be “controlled”
by the output rate of the bottleneck step(s)
35
Process Mapping Analysis
Hand-offs
– Unclear if a process step has received needed
inputs from an “upstream” step
– Misunderstanding of who is responsible, or
who has done what
– Communication problems
36
Process Mapping Analysis
Conflicting objectives
– Unclear alignment from one group to another
working in the same process
– Direction from leadership and metrics
– Communication problems
37
Process Mapping Analysis
Common problem areas
– Overall weaknesses seen throughout a process,
common failure modes
– Repeated steps or checks in a variety of places
throughout the process flow
– Communication problems
– The “Hidden Factory”
38
The Hidden Factory
All of the work that is performed that is above and
beyond what is required to deliver good products
and services to the customer; work that is not
necessarily tracked (cost, productivity, etc.).
Work-arounds or “built-in” Rework
39
Process Mapping, Measurement and
Analysis
Study your key processes and note any of the
aforementioned potential process delays or
errors directly on your process map. Go to
the source to verify with data. Many key xs
are identified through careful and deliberate
process measurement and analysis.
40
Process Map Analysis
Frequency
of VS
checks?
Start
Can Cerner
flag critical
VS changes?
Ongoing assessment and
monitoring of patients vital
signs and status
Patient
medical
record
Cerner data
Change in patient’s
physical status
Handoff
issues?
NO
Continued
deterioration
YES
NO
Potentially bad
clinical outcome
Use of
MRTs?
NO
Kaizen bursts identify handoffs or transactions that have
the potential to create defects
41
Did we
recognize
change?
Nursing
skill to
recognize
shock?
Are we
effectively
communicating
vital info?
Did we
act
quickly?
YES
Does a
full ICU
mean delays?
Was the
action
appropriate?
YES
Appropriate care
delivered
Best possible
outcome
42
Measure Phase:
Measurement System Analysis (MSA)
Can the variation in the parts (output) be detected over and
above the variation caused by the measurement system?
43
Baseline Data Questions
• What is the current process capability? (Where
are we now in terms of consistently meeting the
customer’s needs?)
• Is the process stable?
• How much improvement do you need to meet
your goal, to make a meaningful impact?
• What data are currently available?
• How will you know whether there has been an
improvement?
• How does the current state compare to the CTQs?
44
Measurement System Analysis (MSA)
(pgs. 87 – 103)
Is it the right data to answer
the question at hand?
or
Is it the best question
the existing data can answer?
45
Look Carefully
46
Measurement System Analysis (MSA)
(pg. 87 – 103)
A measurement system analysis is performed to
determine if the measurement system can
generate true reliable data, and to assure the
variation observed is due to the actual
performance of the process being studied, and not
due to excessive variation in the measurement
system itself.
47
Measurement System Analysis (MSA)
“In any program of control we must start with observed
data; yet data may be either good, bad, or indifferent.
Of what value is theory of control if the observed data
going into that theory are bad? This is the question
raised again and again by the practical man (woman).”
- Walter Shewhart
48
Reliable Data ?
49
Separate what we
think is happening
from what is really
happening!
50
Data Integrity?
• What assumptions were made?
• Is the data representative of the process ?
• Who generated the data?
• How was it measured?
• What is the noise in the measurement?
• If required, does it pass an audit?
• Can we trust the data and the measurement
system used to generate the data to properly
investigate the process?
51
Inspection
Exercise: You have 60 seconds to document the number
of times the 6th letter of the alphabet
appears in the following text:
The Necessity of Training Farm Hands for First Class
Farms in the Fatherly Handling of Farm Live Stock is
Foremost in the Eyes of Farm Owners. Since the
Forefathers of the Farm Owners Trained the Farm
Hands for First Class Farms in the Fatherly Handling
of Farm Live Stock, the Farm Owners Feel they
should carry on with the Family Tradition of Training
Farm Hands of First Class Farmers in the Fatherly
Handling of Farm Live Stock Because they Believe it
is the Basis of Good Fundamental Farm
Management.
52
6 Items To Look For In A Good
Measurement System
Resolution
Consistency
Repeatability
Reproducibility
Linearity
Accuracy
53
Resolution
• Is the measuring base unit small enough to adequately
evaluate the variation in the process?
• Can we “see” differences in what the process is producing?
• Must monitor the process frequently enough to catch it varying,
or going from good to bad.
• As a general rule, we should use units of measure that are at
least 10 subdivisions of the range of measurement being
investigated. “Ten bucket rule”
Examples of issues
with resolution in
your projects?
54
Consistency (Stability) Issue
• Does the measurement system error
remain stable or predictable over time,
across equipment, across operators,
across all shifts, across all facilities, etc…?
• Will we get reliable measurements from the
process even if the measurements are
taken on the weekends, during night shifts,
by different employees, etc.?
55
Measurement
Systems
Would it be OK if the time clock your
employees get paid by is off by:
1 hour every day?
1 hour a week?
1 hour per month?
1 hour per year?
Measurement Systems must be
Repeatable & Reproducible if we
are to draw adequate conclusions
56
Repeatability / Precision
• The variation in measurements obtained when
one operator uses the same measuring process
for measuring the identical characteristic of the
same parts or items ( part dimension, blood
pressure cuff, chemistry analyzer, etc.).
• Can the variation in the parts be detected over
and above the variation caused by the
measurement system?
• How closely will successive measurements of the
same part or process by the same person using
the same instrument repeat themselves?
57
Reproducibility
• The variation in the average of measurements
made by different operators using the same
measuring process when measuring identical
characteristics of the same items (two abstractors
reviewing same chart).
• Reproducibility is very similar to repeatability.
The primary difference is that instead of looking
at the consistency of one person, we are looking
at the consistency between people.
• Are the average measurements for each part
reproducible across different operators, gages,
machines, locations, etc…?
58
Linearity
• Is the measurement system consistent across
the entire range of the measurement scale?
• Are measurements reliable even at the
extremes?
59
Accuracy
• Are the measurements truly
representative of the output of the
process being studied?
• On average, do I get the “true data”
from the output of the process?
60
Accuracy vs. Precision
.
.
. .
.
. .
.
Not Accurate, Not Precise
.
.. .
... . . .
Accurate but not precise
.. ...
..
.. ....
..
Precise but not accurate
Accurate and Precise
61
62
Key Questions for a MSA?
(Your Project’s Measurement System)
• Is my measurement system repeatable will I get the same results if I take the
measurement more than once?
• Is my measurement system reproducible will someone else be able to complete the
same measurement and get the same
results?
• Is my measurement system accurate - will
the results from my study match the actual
value, or expert data?
63
MSA Recap
ADEQUATE
Most of the variation is
accounted for by physical or
actual differences in the process
or components.
- All sources of measurement
variation will be small
- You can have higher
confidence that actions you
take in response to the data
are based on reality
INADEQUATE
Variation in how the
measurements are taken is
high.
- You can’t tell if differences
between units or process
observations are due to the way
they were measured, or are true
differences
- You can’t trust your data and
therefore shouldn’t react to
perceived patterns, special
causes, etc.—they may be false
signals
64
Why do we conduct MSA?
(Your Project’s Measurement System)
• While many statistical tools may be very powerful,
they can also provide misleading results if there is too
much measurement error.
• We conduct MSA to gain an understanding of the
quality, or trustworthiness, of data being collected to
drive decisions about improving your process(es).
• Some part of the total observed variation inherent to a
process is, in fact, caused by the measurement
system itself. – How much variation can we tolerate?
• A good measurement system is vital for your baseline
data as well as your investigations of possible Xs.
65
Measure Phase:
Calculating Sigma Levels
and
Baseline Data and Metrics
66
Why are Baseline Measures
so Important?
“If we could first learn where we are and
where we are going, we would be better
able to judge what to do and how to do it.”
Abraham Lincoln
67
Calculating the Approximate Sigma Level
1. Define your opportunities
2. Define your defects
3. Measure your opportunities and
defects
4. Calculate your yield
5. Look up process Sigma
68
Calculating the Approximate Sigma Level
Define your opportunities and defects
• An opportunity is any area within a product, process, service, or
other system where a defect could be produced or where you fail
to achieve the ideal product or service in the eyes of the customer
.
• A defect is any type of undesired result. The defect threshold
may be as superficial as whether or not the product works. But it
may be more subtle.
– This may be the difference between “Does the car run?” and
“Does the car have a flawless paintjob, the tires I want, the
brand of CD changer I want, etc, etc…”
– It’s usually not enough just to ask whether the product “meets
expectations”… the expectations need to be defined.
69
Calculating the Approximate Sigma Level
Measure your opportunities and defects and
calculate your yield – the percent without defects.
Opportunities - Defects
Opportunities
x 100
Total number of widgets minus widgets
with defects
Total number of widgets
156
183
x 100
x 100
85.24%
70
Calculating the Approximate Sigma Level
• Look up process Sigma
A 85.24% yield is a process Sigma of 2.5 to 2.6
Discussion: What is your estimate of your process
Sigma
71
Activity
Working individually
1. Define an opportunity in your process. What’s a
ballpark estimate of the number of opportunities in
your process?
2. Define the defects in your process. What’s a
ballpark estimate of the number of defects in
your process?
3. Calculate your process yield
Opportunities - Defects
Opportunities
x 100
4. Find your Sigma level
(10 minutes to complete)
72
Balancing Measures
• Balancing measures are often identified to prevent
important process, input, or output factors from being
sacrificed at the expense of achieving a narrow goal.
• Prevent “tunnel-vision”
• Be alert for unintended consequences
• “Need to know” versus “nice to know”
• Balancing measures are those things we don’t want
to lose sight of as we drive toward meeting our goal.
73
Introductory Statapult Activity!
•
Working in teams,
–
–
–
–
–
–
Try to hit a target distance (specification) with a projectile of
your choice and your assigned statapult
Collect the distance for each shot by team member in
sequential order (6 total shots for each team member)
In addition to the actual distance shot, also record if the shot
is “in spec”, or “out of spec”
Collect and record the total time it takes each team member
to complete their respective 6 shots
List potential xs that explain variation in the distance the
projectile travels (Y) (If you have any variation?)
List any waste that occurred in your statapult process
How well did your team perform? What is your team’s sigma level?
Are you individually a good statapultician?
74
Baseline Data Questions
• What is the current process capability?
• Is the process stable?
• How much improvement do you need to meet
your goal?
• What data are currently available? How many
samples do I need to collect (pg. 85-86)
• How will you know whether there has been an
improvement?
• How does the current state compare to the CTQs?
75
Types of Data
Two major types of data (pg 70)
– Continuous (or “variable”)
• Measurement along a continuum, length, height, age,
time, dollars, etc.
– Discrete (or “attribute”)
• Categories, yes/no, names, labels, counts, etc.
76
Types of Data
Continuous
– Any variable that can be measured on a continuum or
scale that can be infinitely divided
– There are more powerful statistical tools for interpreting
data continuous data, so it is generally preferred over
discrete/attribute data
– Examples: height, weight, age, respiration rate, etc.
77
Types of Data
Discrete
Data Type
Definition
Example
Count
How many?
Count of errors;
How many patients got evidencebased care?
How many specimens were tested?
Binary
Data that can have only one of Was delivery on-time? Was the
two values
product defect-free? Alive/dead;
Male/female; Yes/No
Nominal
The data are names or labels
with no intrinsic order or
relative quantitative value
Colors; dog breeds; diagnoses;
brands of products; nursing units;
facility
Ordinal
The names or labels represent
some value inherent in the
object or item (there is an
obvious order to the items)
Product performance: excellent,
very good, good, fair, poor;
Severity: mild, moderate, severe,
critical
78
Types of Data
Example:
Type of data:
• Product meets design
specifications
• Discrete – Binary
• Heart rate
• Continuous
• Distribution managers
• Discrete – Nominal
• Gasoline grades
(regular, plus,
premium)
• Discrete - Ordinal
79
Baseline Capability
• A baseline capability study basically
answers how well the current “as is”
process meets the needs (specifications)
of the customer. It can be tracked over
time via run chart, control chart, etc.
• Process Capability compares the output of
a process to the needs of the customer for
a given key measure.
80
Process Capability
Uncontrolled Variation is Evil
Traditional Philosophy
Taguchi Philosophy
“goalpost mentality”
LSL
USL
Anything outside
the specification limits
represents quality losses
LSL
USL
Any deviation from
the target causes
losses to the business
81
Process Capability: Variation
The New Goalpost Scoring
The New Business Reality
3 Points
2 Points
1 Point
82
Characteristic of the Performance Gap… (Problem)
Accuracy and/or Precision
Off-Target
Variation
LSL
LSL
USL
USL
On-Target
Center
Process
Reduce
Spread
LSL
USL
LSL = Lower spec limit
USL = Upper spec limit
The statistical approach to
problem solving
83
Process Capability:
Short Term and Long Term
Short Term
Long Term
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-5
-4
-3
-2
-1
0
1
2
3
4
5
84
Process Capability:
Short Term and Long Term
• Processes experience more variation over a longer
term than in the short term.
• Capability can vary depending on whether you are
collecting data over a short term or a long term.
• The equations and basic concepts for calculating
capability are identical for short term and long term
except for how standard deviation is calculated to
account for the increased variation over the long
term.
85
Is a 3s process a capable process?
Long-term
Capability
LSL
USL
Consistent,
but not
always
accurate
Perfect
World –
Accurate &
Consistent
Time
Shortterm
Capability
86
Process Capability:
Short Term and Long Term
• Short Term (Cp and Cpk calculations)
– Gathered over a limited number of cycles or intervals
– Gathered over a limited number of shifts & associates
• Long Term (Pp and Ppk calculations)
– Gathered over many cycles, intervals, equipment, &
operators
– May be attribute or variable
– Assumes the data has “seen” at least 80% of the
total variation the process will experience
87
Process Capability:
Short Term and Long Term
(pgs. 135 – 140)
• Cp (short term) and Pp (long term)
calculations compare the amount of
variation in the process output to the total
range of variation allowed (customer
specifications)
88
A Problem With Cp and Pp
Which is the better process?
What is the difference in Cp
between the two processes?
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-5
-4
-3
-2
-1
0
1
2
3
4
5
What can be done to make Cp
more effective as a process
capability statistic?
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|
-4
-3
-2
-1
0
1
2
3
4
5
89
Process Capability:
Short Term and Long Term
(pgs. 135 – 140)
• Cpk (short term) and Ppk (long term)
compares the amount of variation and the
location of the mean from the process
output to the total range of variation
allowed (customer specifications)
90
Meet Ppk / Cpk
Process Performance
C pk  min{C pl , C pu }
Example:
  LSL
C pl 
3s
A process mean is 355,
standard deviation is 15,
upper spec. limit is 380,
and lower spec. limit is
270
USL  

3s
C pu
What is the Cpk?
What is the Cp?
|
|
|
|
|
|
|
|
|
|
-4
-3
-2
-1
0
1
2
3
4
5
91
Capability – Cpk’s
Centered Process
Cpk = USL-Mean
Cp = USL – LSL
3s
6s
OR
Cpk = Mean – LSL
LSL
USL
m
Shifted Process
Shifted Process
Cp =
same
C =
Cp =
same
C =
pk
pk
less
LSL
m
3s
less
USL
LSL
m
USL
92
Cpk and Process Sigma
USL
LSL
-6
-5
-4
-3
LSL
-2
-1
0
+1 +2
+3
+4
USL
LSL
Cpk = 1
Cpk = 1.67
+/- 3σ within
spec limits
+/- 5σ within
spec limits
+5
+6
USL
-6 -5 -4 -3 -2 -1 0 +1 +2 +3 +4 +5 +6
LSL
USL
Cpk = 1.33
Cpk = 2
+/- 4σ within
spec limits
+/- 6σ within
spec limits
93
-6 -5 -4 -3 -2 -1 0 +1 +2 +3 +4 +5 +6
-6 -5 -4 -3 -2 -1 0+1 +2+3+4+5+6
Run Charts
The Importance of Data Over Time
Continuous Y (e.g.Length of Stay)
Graphical display: Run charts (also calledTime-series charts)
average
Discrete X (e.g. Month)
94
Data Analysis / Statistical Software:
Minitab
Brief Overview
95
Improving how we Improve!
(Through Data Analysis and Minitab)
Minitab is a tool consisting of many tools and
techniques for thorough data analysis.
1. Do not think of Minitab as “giving you the answer.”
2. If you do not have reliable data, and/or you are not
asking the proper analysis questions, Minitab will
be of little value – if any!
96
Improve: Data-Driven Approach
Is there a difference between
Data and Information?
Data – factual information used as a basis
for reasoning
Information – the communication or
reception of knowledge obtained from
investigation, study, or instruction
97
Minitab
• Typical desktop icon for Minitab
98
Minitab Overview
Toolbar
Session Window
Test results and
messages will appear as
running text. The text in
this window can be
modified, copied, and
pasted
Worksheet
You can have multiple
worksheets with your data
arranged in columns. The
grey line is where you put
your column labels
99
Minitab Overview
100
Text column
101
Date column
Numeric data column
Data Analysis and Minitab
Remember the triple C’s for Data in Minitab
1. Organize data into Columns
2. Record/Input data Chronologically as appropriate
3. Data must be Clean (no commas, dollar signs, etc.)
102
Descriptive Statistics
• Using the data collected in the statapult
exercise, look at the descriptive stats
– Stat>Basic Statistics>Display Descriptive Statistic
– Stat>Basic Statistics>Graphical Summary
103
Descriptive Stats
Descriptive Statistics: Distance
Variable
Distance
N
75
N*
0
Variable
Distance
Maximum
87.000
Mean
78.880
SE Mean
0.549
StDev
4.756
Minimum
55.000
Q1
77.000
Histogram (with Normal Curve) of Distance
30
Mean
StDev
N
25
Frequency
20
15
10
5
0
104
55
60
65
70
75
Distance
80
85
90
78.88
4.756
75
Median
79.000
Q3
81.000
Graphical Summary
Summary for Distance
A nderson-D arling N ormality Test
55
60
65
70
75
80
A -S quared
P -V alue <
1.66
0.005
M ean
S tDev
V ariance
S kew ness
Kurtosis
N
78.880
4.756
22.621
-1.83016
7.84318
75
M inimum
1st Q uartile
M edian
3rd Q uartile
M aximum
85
55.000
77.000
79.000
81.000
87.000
95% C onfidence Interv al for M ean
77.786
79.974
95% C onfidence Interv al for M edian
78.286
80.000
95% C onfidence Interv al for S tDev
9 5 % C onfidence Inter vals
4.098
5.668
Mean
Median
78.0
78.5
79.0
79.5
80.0
105
Capability Analysis
• Stat>Quality Tools>Capability Analysis
106
Short Term Variation - Example
• Use Minitab to estimate short term variation:
– Stat > Quality Tools > Capability Analysis (Normal)
Process Capability of ChambTemp
LS L
USL
Process Data
LSL
123.50000
Target
*
USL
126.50000
Sample Mean 124.60711
Sample N
35
StDev(Within)
0.49662
StDev(Overall)
0.48982
Within
Overall
Potential (Within) Capability
Cp
1.01
CPL
0.74
CPU 1.27
Cpk
0.74
CCpk 1.01
Overall Capability
Pp
PPL
PPU
Ppk
Cpm
123.6
Observed Performance
PPM < LSL 28571.43
PPM > USL
0.00
PPM Total 28571.43
Exp. Within Performance
PPM < LSL 12897.49
PPM > USL
69.05
PPM Total 12966.54
Project: Untitled; 9/ 9/ 2004
124.2
124.8
125.4
Exp. Overall Performance
PPM < LSL 11902.71
PPM > USL
55.66
PPM Total 11958.37
126.0
1.02
0.75
1.29
0.75
*
Capability Six-Pack
Process Capability Sixpack of Distance
Individual Value
I Chart
Capability Histogram
75
S pecifications
LS L 75
U S L 85
LCL=67.18
1
60
8
15
22
29
36
43
50
Moving Range Chart
20
1
57
64
71
55
60
65
70
75
80
85
90
Normal Prob Plot
1
1
A D: 1.656, P : < 0.005
1
UCL=14.37
10
__
MR=4.40
0
LCL=0
1
8
15
22
29
36
43
50
57
64
71
60
Last 25 Observations
70
80
Within
S tDev 3.89951
Cp
0.43
C pk
0.33
70
Within
O v erall
60
55
60
65
Observation
90
Capability Plot
80
Values
USL
_
X=78.88
1
Moving Range
LSL
UCL=90.58
90
70
75
S pecs
O v erall
S tDev 4.75611
Pp
0.35
P pk
0.27
C pm
*
108
Measure Phase:
Pareto Charting and Analysis
(The 80/20 Rule)
109
Pareto chart
• A Pareto chart is a special type of bar graph where
the categories are arranged from largest to smallest
with a line indicating the cumulative percent
Vilfredo Pareto observed that 80% of the
land in Italy was owned by 20% of the
population.
Later, Joseph Juran called this “80-20 rule”
the Pareto principle.
80% of the effects come from 20% of the
causes.
110
Lean Six Sigma
Project and Team Basic Tools
Pareto Analysis (pg. 142-144)
A Pareto chart is simply a bar graph with the bars
arranged typically in descending order from highest to
lowest frequency by discrete category. It graphically
displays the 80/20 rule. Approximately 80% of the
quantifiable results (frequency), will be attributed to
20% of the causal categories.
111
Create the Pareto Chart
•
•
•
•
Go to Stat>Quality Tools>Pareto Chart
Select “Chart Defects Table”
Defects or attribute data in: Colors
Frequencies in: Counts
112
Create the Pareto Chart
• Click on Options
• Label the X axis “M&M
Color”
• Label the Y axis “Count”
• Give your chart a title
• Click on OK
• Click on OK again
113
Your Pareto Chart
…should look something like this:
114
Lean Six Sigma
Project and Team Basic Tools
115
Measure Phase:
Cause and Effect Analysis
(Collecting the “theories” of x’s)
116
Statapult Activity Follow-up
•
Working with your team
–
–
–
–
–
–
–
117
Discuss the effect (Y results) of your statapult process
(the head of your fishbone diagram)?
How satisfied are you with the measurement system for
your process output
List some potential xs (theories) that affect your process
outcome (Y).
Construct a fishbone diagram of the potential x’s
Discuss how we might determine the most significant x’s
List some categories of waste experienced by your team
Prepare a mini-presentation (5 mins) to share with class
Lean Six Sigma
Project and Team Basic Tools
Cause and Effect Diagrams (pg. 146-149)
A C&E diagram (also called a fishbone diagram), is
a pictorial display of the potential or likely causes of a
given effect. The causes are grouped and arranged in
meaningful categories, sometimes called branches.
There are numerous ways to name the grouped
branches. The most common names include: Material,
Method, Manpower, Machinery, Measurement, and
Mother Nature (Environment).
118
119
120
Lean Six Sigma
Project and Team Basic Tools
121
Other Fishbone categories
• 6 Ms
– Method, Material, Manpower, Machinery,
Measurement, Mother Nature
• 4 Ps
– Policies, Procedures, Personnel, Place
122
Cause & Effect Matrix Form
Natural break, Sanity check
1
2
3
4
5
6
7
8
9
10
11
12
13
Create Customer Header
Identify Products
Identify Products
Generate price
Generate price
Generate price
Generate price
Customer creates header
Identify Products
Identify Products
Identify Products
Issue
123 quote
Identify Products
4
10
1
2
3
Quote accuracy
Process Step
8
# of contacts
Rating of Importance to
Customer
Time to quote
Cause & Effect Matrix
Total
9
9
9
9
9
9
9
3
9
3
3
3
9
9
9
9
3
3
1
1
9
9
3
3
1
3
9
9
9
9
9
9
9
9
3
9
9
9
3
198
198
198
174
174
166
166
150
138
126
126
118
114
Process Input
Cust ID
Parametric design
Supercedes ref
SCR200
SPA file
Price sheet
Marketing approval
Credit status
Tech rep exp
Spec features
SCR8000 Xref
Marketing approval
Cust prod ID
Cause and Effect Chart
• Stat>Quality Tools>Cause-and-Effect
• In Minitab, you can build your C&E Chart from
lists of potential Xs in the workbook or by
keying them into the dialogue box
124
Xs in the Worksheet
125
Xs typed in as constants
126
Sub-branches
127
Measure Phase:
Data Collection Plan
and Preparation for Analysis
(Data Collecting for the “theories” of x’s)
128
Data Collection Plan
(pgs. 72 – 81)
• Data are the documentation of an observation or
measurement. Data are facts, but you may need
information – data which provide the answers to
questions you have.
• A good data collection plan helps ensure data will
be useful (measuring the right things) and
statistically valid (measuring things right).
129
Data Collection Plan
(pgs. 72 – 74)
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
130
Decide what to collect
Decide on stratification factors as needed
Develop operational definitions
Determine the appropriate/needed sample size
Identify the source/location of data
Develop data collection forms/check sheets
Decide who will collect the data
Train data collectors
Do ground work for analysis
Execute your data collection plan
Data Collection Plan
1.
Formulate the question or theory: What is the question
we are trying to answer?
2.
Decide how data will be communicated and analyzed.
3.
Decide how to measure: population or sample?
4.
Collect data with a minimum of bias.
131
Data Collection Plan
Asking the Right/Best Question
Time to ABX in
Minutes is
captured using
a continuous
measure:
“How many
minutes did it
take?”
What kind of
data will you
be collecting?
132
Patient
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
Month
January
January
January
February
February
February
February
March
March
March
March
April
April
April
May
May
May
June
June
June
June
Time to ABX
administration in
minutes
109
205
256
245
250
264
157
125
223
215
315
125
267
207
185
162
243
239
235
225
237
Were ABX
administered
within 4 hours?
Y
Y
N
N
N
N
Y
Y
Y
Y
N
Y
N
Y
Y
Y
N
Y
Y
Y
Y
It can be
converted into a
discrete
measure: “Was
it done within
four hours?”
Data Collection
Asking the Right Question
Is the measure you are using a good one?
• Understandable
• Provides information for decision making
• Applies broadly
• Is conducive to uniform interpretation
• Is economical to apply
• Is compatible with existing design of sensors
• Is measurable even in the face of abstractions
133
Data Collection Plan
Communicating the Results
• Although you may not know what the data reveals – and
it may seem odd to be thinking about how your team will
analyze and display the data -- having some idea about
the sort of analysis and display you will use will help you
make decisions about the data you collect.
• If you wait until after the data are collected to think about
analysis, you may find that the data do not support the
kind of analysis you want to conduct.
134
Sampling
Qualities of a Good Sample
• Free from bias
– Bias is the presence of some undue influence on the sample
selection process that causes the population to appear different
than it actually is
• Representative
– The data should accurately reflect a population. Representative
sampling helps avoid biases specific to segments of the
population
• Random
– The data are collected in no predetermined order and each
element has an equal chance of being selected
135
Sampling
• Random Sampling – each element has an equal
chance of being selected
– Simple random (no pattern)
– Systematic random (every Nth value)
• Stratified Random Sampling – the population is
grouped into levels or “strata” according to some
characteristic and proportional samples are
drawn randomly from each stratum
136
Random Sampling
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Population
137
Sample
X
X
X
X
X
X
Each element has an equal
chance of being chosen
Stratified Random Sampling
XXXXX
YYYYY
YYYYY
YY
ZZZZZ
ZZZZZ
Population
138
Sample
• Randomly sampled from
each stratified category or
group
• Sample sizes for each
stratum are generally
proportional to the size of the
group within the population
Sampling
The following are NOT appropriate ways to
get a valid random sample:
• Fixed percentage sampling – leads to
undersampling from small populations and
oversampling from large populations
• Judgment sampling – using judgment to select x
number of “representative” samples - guess
• Chunk or convenience sampling – selecting
sample simply because the items are
conveniently grouped
139
Sampling (pgs. 85-86)
Sample size calculation for continuous data
2
n = 1.96s
Δ
140
n
Minimum sample size
1.96
Constant representing a confidence interval of 95% (valid
when sample size is 30 or more)
s
Estimate of standard deviation of data
Δ
The level of precision desired from the sample you are trying
to detect (same units as s)
Sampling
Sample size calculation for discrete data
2
n = 1.96s
Δ
141
P (1-P )
n
Minimum sample size
1.96
Constant representing a confidence interval of 95% (valid
when sample size is 30 or more)
s
Estimate of standard deviation of data
P
Estimate of the proportion defective
Δ
The level of precision desired from the sample you are trying
to detect (same units as s)
Effective Data Driven Practice
Steps to Effective
Data Driven Practice
Ask the Right Question
- Bias the question with existing
belief system
Right/Appropriate Data
- No easy access to data systems
- Substitute what is needed with what is available
- Missing and incomplete data
- Data values are incorrect
Proper Analysis
Correct Audience
Correct Interpretation
Appropriate Action
142
Potential Failure Modes
- Insufficient statistical skill
- Inadequate statistical software
- Analysis paralysis
- Unable to take action
- Decision errors from false positives / false
negatives
- Refusal to accept the facts
- Bias the interpretation with existing belief system
- Intellectual dishonesty
- Unwilling to take action
- Analysis paralysis
Lean Six Sigma
DMAIC Phase Objectives
•
Define… what needs to be improved and why
•
Measure…what is the current state/performance level and potential causes
•
Analyze…collect data and test to determine significant contributing causes
•
Improve…identify and implement improvements for the significant causes
•
Control…hold the gains of the improved process and monitor
143
Project Name:
Project Scope:
Champion: Name
Process Owner: Name
Black Belt: Name
Green Belts:
Enter scope description
Names
Problem Statement:
Mislabeled example
Define
Measure
Analyze
Start Date: Enter Date
End Date: Enter Date
Start Date: Enter Date
End Date: Enter Date
 Benchmark Analysis
 Project Charter
 Formal Champion
Approval of Charter
(signed)
 SIPOC - High Level
Process Map
 Customer CTQs
 Initial Team meeting
(kickoff)
 Identify Project Y(s)  Identify Vital Few
 Identify Possible Xs
Root Causes of
(possible cause and
Variation Sources &
effect relationships)
Improvement
 Develop & Execute
Opportunities
Data Collection Plan  Define Performance
 Measurement
Objective(s) for Key
System Analysis
Xs
 Establish Baseline
 Quantify potential $
Performance
Benefit
 Not Complete
144
 Complete
 Not Applicable
Customer(s):
CTQ(s):
Defect(s):
Beginning DPMO:
Target DPMO:
Estimated Benefits:
Actual Benefits:
Start Date: Enter Date
End Date: Enter Date
Improve
Control
Start Date: Enter Date
End Date: Enter Date
Start Date: Enter Date
End Date: Enter Date





 Implement
Sustainable Process
Controls – Validate:
 Control System
 Monitoring Plan
 Response Plan
 System Integration
Plan
 $ Benefits Validated
 Formal Champion
Approval and Report
Out
Generate Solutions
Prioritize Solutions
Assess Risks
Test Solutions
Cost Benefit
Analysis
 Develop &
Implement
Execution Plan
 Formal Champion
Approval
Directions:
•Replace All Of The Italicized, Black Text With Your Project’s Information
•Change the blank box into a check mark by clicking on Format>Bullets and
•Numbering and changing the bullet.
Author: Enter Name
Date: July 7, 2015
Going Forward with your
Project and Analysis
“What’s different in me is that I still pose to
myself the questions that people quit
making when they were five years old.”
Albert Einstein
145