Measure Performance
Introduction and Determine What
to Measure
Define
Opportunities
Measure
Performance
Analyze
Opportunity
Improve
Performance
Control
Performance
Business
Opportunity
Document and Analyze
Processes
Define Customer
Requirements
Build Effective Teams
Determine what to
measure
Manage measurement
Understand variation
Determine Sigma
Performance
Excellent team
performance
process stratification
and Analysis
determine root causes
validate root causes
manage creativity
improvement ideas
evaluate and select
solution
present
recommendations
Implement change
Develop and Execute
pilot plan
plan and implement
solution
process integration
closure and
recognition
Define Opportunities
Measure Performance
Analyze Opportunity
Improve Performance
Control Performance
Review and Transition
• In Section ,we learned how to：
– Understand the role of effective teams in process
improvement.
– Apply team evaluation tools to identify opportunities to
improve team effectiveness.
• Section ，Measure Performance, covers the
following areas:
Business
Opportunity
1 .2 Document and
Analyze
Processes
Build
Effective
Teams
Define
Customer
Requirement
Prepared Team
Determine what
to measure
2 .2
Manage
Measurement
Determine
Sigma
performance
Understand
Variation
Excellent Team
Performance
Summary of “ Measure Performance”
• What to Measure
– Understand the role that data plays in process improvement
– Understand the cause and effect relationships that occur inside the team’s process
– Determine the indicators needed to evaluate current process performance
• Measurement
– Understand different types of data and how each type can provide the team with different
insights and knowledge of a process
– Develop operational definitions and data collection plans that build validity and consistency in
the data which the team gathers
• Variation
– Understand the concept of variation and how a process can be evaluated by assessing its
variation over time
– Plot and calculate the variation of the team’s business process
– Gain hands on experience with the use of the statistical software package MINITABTM
• Sigma Performance
– Understand the various calculations associated with determining process sigma
– Calculate the sigma performance of the team’s process
– Calculate the rolled-up Sigma for the business
• Team Performance
– Understand the role of high-performance work teams in process improvement
– Use team diagnostics and assessments to evaluate the team strengths and
opportunities for improving its own performance
Measure
Determine What to Measure
What to
Measure
Objective
To identify different types of measures and an understanding of how the
measures relate to critical customer requirements.
Key Topics
•Performance Measurement
•Input, Process,and Output Indicators
•Indicator Relationships
Determine
Sigma
Performance
Determine What
to Measure
Manage
Measurement
Understand
Variation
Excel Team
Performance
Performance Measures-
Customer Value Achieved?
供货商 流程输入 业务流程 流程产出
关键客户要求
基于联合客户期望和流程
业绩表现的重要决定
输入衡量 流程衡量 产出业绩
表现衡量
客户价值
Process Elements and Indicator
Relationships
Input Indicators Process Indicators Output Indicators
Measures that evaluate the degree to
which the inputs to a process, provided
by suppliers, are consistent with what
the process needs to efficiently and
effectively convert into customers-
satisfying outputs.
Examples:
•#of customer inquiries
•Type of customer inquiries
•# of orders
•# of positions open
•Type of position open
•Accuracy of the credit analysis
•Timeliness of the contract submitted for
review
Measures that evaluate the
effectiveness, efficiency and quality of
the transformation processes-the steps
and activities used to convert inputs into
customer-satisfying outputs
Examples:
•Availability of service personnel
•Time required to perform credit review
•%of non-standard approvals required
•#of qualified applicants
•Total cost of service delivery
•Total overtime hours
Measures that evaluate dimensions of
the output-may focus on the
performance of the business s well as
that associated with the delivery of
services and products to customers.
Examples：
•#of calls/hour taken by each service rep
•2nd year customer retention figures
•Total # of meals delivered
•%customer complaints
Effective improvement requires information from the entire supplier-
customer,cause and effect relationship.
Customers:Suppliers: Inputs: Outputs:
Process
Start Boundary___________ End Boundary____________
Input, Process and Output Indicators
Efficiency Measures
• Cost per transaction
• Time per activity
• Amount of rework
• Turnaround time
• Variability of an activity
• Efficiency Measures
• Percent defective
• Number of errors
• Total response time
• Invoice/billing accuracy
• Revenue
Input
Indicators
Process
Indicators
Output
Performance
Indicators
CTQ’S
CTP’S
Process Output Indicators include
CTQ’s & CTP’s
VOB-Voice of the Business
CBR-Critical Business Requirements
CTP-Critical to the Process
VOC-Voice of the Customer
CCR-Critical Customer Requirements
CTQ-Critical to Quality
VOB
Business
Issues CBR’S
CTQ’S
CTP’S
Output
Indicators
CCR’S
Customer
Issues VOC
CTQ & CTP Examples
CTP’S
Cost/Unit
Productivity
Compliance with
Regulations
Changeover Time
Safety
Training Hours
CTQ’S
Price/Unit
Delivery Time
Dimensions
Purity
Reliability
Color
Service Level
Process Output
Indicators
CTQ’S
CTP’S
Critical to:
The Customer
The Market
Critical to:
The Business
The Regulator
The Employees
Success Derived From Project Focus
Example:Medical Diagnostic Tube Life
After 8 projects January’96 to May ‘97 average tube life doubled.
Y
y1
Oil Dielectric Quality
GTD-3
y2
Focal Spot Control
GTD-7
y3
Generators Spits
GTD-17
y4
Rotor Failures
GD-14
yn
X4,1 X4,2 X4,3 X4,4 X4,N
X2,N
X3,N
X2,4X2,3X2,2
X3,1 X3,2 X3,3 X3,4
X2,1
X1,NX1,4X1,3X1,2X1,1
Top level Y is big enough to be seen at
OBU level-an operational business
objective.
Frequently the parent project does at least a
verification of this top level Y with the
customer
GB materialGB material
S I G M AI G M A
CCR’s and Multiple Output Indicators
In the previous section, teams translated a
variety of VOC data into critical customer
requirements. Teams were careful to
recognize that some customer feedback and
statements need to be clarified, and that a
process for specifying CCRs involves
considering key issues customers may have
with a product or service. From these
issues, the team was able to specify the
critical customer requirements of the
process output.
Some CCRs may be measured in terms of
one specific expectation a customer has.
Others may require several output
indicators. The table shows how one CCR
can have one or several associated output
indicators.
Output Indicator
•Product delivery cycle time from
the completion of the customer
order to the delivery of the product
•Number and type of vehicle
specifications delivered correctly
•Actual delivery time VS promised
•Delivery time for each vehicle
•Number of times vehicles were
delivered to location other than
what is specified on agreement
Critical Customer
Requirement
Product is delivered
within three hours of
order taken.
Right vehicle is
delivered at the right
time to the right location
Output Indicator
•Number and type of vehicle
specifications delivered
correctly
•Actual delivery time Vs
promised delivery time for
each vehicle
•Number of times vehicles
were delivered to location
other than what is specified
on agreement
critical Customer
Requirement
•The vehicle delivered
meets the vehicle
specifications as
described in the contract.
•The vehicle is delivered
within the time specified in
the contract.
•The vehicle is delivered at
the location specified in
the contract.
Selecting the Right Process Indicators
• In addition to making sure that the indicators provide the team with valid
and quantifiable data, teams must be sure that what they are measuring
actually enables them to evaluate the cause and effect relationships
occurring inside the process. Below is a list of questions that each team
should review after identifying output, process, and input indicators.
• Are each of the process indicators true “predictors,”or leading indicators, of
at least one output indicator?
• Do the process indicators evaluate areas of the process that are known to
adversely affect the quality of the process output?
• Has the team identified process indicators for the process variables that
most influence the ability of the process in meeting critical customer
requirements and therefore the output indicators?
• If a critical customer requirement is not met,does the team know why(root
cause)? What additional process indicators may be needed to answer this
question?
Selecting the Right Input Indicators
• Input indicators allow measurement of the
consistency of the inputs to the process.
– Do the input indicators measure the critical requirements we
have of our suppliers’ products or services?
– Do the indicators measure elements of the input that are
known to affect the ability of our process to meet critical
customer requirements?
– Are the input indicators true “predictors,” or leading
indicators,of at least one process indicator?
– Do the indicators measure aspects of the input that would,
within a specified tolerance, eliminate significant inspection,
scrap,rework or excessive cycle time?
Indicator Relationships
• Link Output Performance to Process and Input Indicators
• First look to establish output indicators because they indicate how
effective your process is at meeting CCRs. Once you understand
the key output performance measures, determine what key input
and process indicators you need in order to meet the desired
outcomes and therefore satisfy customer requirements.
• You can use a relationship matrix to help show the relationship
between the output performance measures and key input and
process measures. The relationship matrix will help prioritize the
most important measures.
• Note: The strength of the relationship is based on how likely
changes in the input of process indicator will cause changes in the
output indicator.
Link Output Performance to Process
and Input Measures
Example:Call Center
Relationship of Process & Input Measures
Note:The strength of the relationship is based on how likely changes in the
input/process measure will cause changes in the output performance measure.
process
&Input
Output
Indicators
Performance Indicators
Answer
Speed
Employee
Experience
First Time
Resolution
Call Abandon Rate
Customer Satisfaction
Strong Relationship weak Relationship
medium Relationship Blank No Relationship
Review and Transition
• In Determine What to Measure, we learned:
– The role that data plays in process improvement
– The cause and effect relationships that occur inside the team’s
process
– How to determine the indicators needed to evaluate current
process performance
• In Management Measurement, we will learn:
– The different types of data and how each type can provide the
team with different insights and knowledge of a process
– How to develop operational definitions and data collection
plans that build validity and consistency in the data which the
team gathers
Determine What
to Measure
Manage
Measurement
Understand
Variation
Determine
Sigma
Performance
Excel Team
Performance
Measure Performance
Manage Measurement
Manage Measurement
• objective
– To establish a discipline and a methodology to be cost-efficient and effective in
collecting data to measure performance.
• Key Topics
– Data Collection
– Operational Definition
– Measurement Plan
– Performance Date Versus Cause Data
– Sampling
Determine What
to Measure
Manage
Measurement
Understand
Variation
determine
Sigma
Performance
Excel Team
Performance
Data Collection
Measurement
management starts
with a data collection
methodology.
Data Collection Method
Identify
Measures
Step 2
Develop measurement plan
Step 1
Develop operational definitions
for measure
Step 3
Collect data
Step 4
Display and Evaluate data
Operational Definition
• Step 1:Operational Definition
• An operational definition is a concept that helps guide the team’s thinking on
what they need to measure as well as the key attributes of the
measure:what,how,and who. It provides the foundation for the team to reach
agreement and build consistency and reliability into data collection. This helps
ensure any person using the agreed-on definition will be measuring the same
thing.
• Operational Definition
• A precise description of the specific criteria used for the measures(the what),the
methodology to collect the data(the how),the amount of data to collect(how
much),and who has responsibility to collect the data(the who).
– Provides everybody with the same meaning.
– Ensures that consistency and reliability are built in up front.
– Describes the scope of the measure(what is included and what is not included).
“an operational definition puts communicable meaning into a concept。”
------W。Edwards Deming
Example:Operational Definition
Poor:Cycle time for applications.
Good:Collect data from all applications received by fax from January 3,1999 to January
17,1999. The response time will be determined by the date and time of the fax received
as shown on the faxed application to the time the approval or rejection letter is faxed to
the applicant as shown on the fax log.
Six Sigma and Operational Definitions
• Operational definitions enable a team to fully agree on how a particular
characteristic of a process is to be measured. It is the process
characteristic that is critical to the satisfaction of the customer.
• Therefore, when developing an operational definition,it is important for
the team to fully understand and agree that the definition reflects
exactly what information the team is attempting to gather on the
process.
• Clarity is even more important when developing and selecting the
measures that will be used to determine the sigma performance of a
process.
• Operational definitions may determine if a team is to count all the
defects on an invoice(required to calculate defects per million
opportunities) or the total number of defective invoices(any invoice with
any defect) or the type of defects encountered on an invoice (to
eliminate the most common defects first). Each of these cases may
require a very different approach for gathering the data.
Operational definitions help ensure that the team does it right the first time
when it comes to data collection.
Exercise: Operational Definition
• Objective
• To practice developing an operational definition.(20 minutes)
• Instructions
• one of the first three examples below and one from your process.
• an operational definition for each that will be clear to all who need to
understand it .
• to share the definitions with the class.
• -Time Departures. A consumer organization wants to rate airlines on how
successful they are at meeting the departure schedule as put forward by
airports. But before the organization can start it needs an operational definition
of when the airplane departs.
• B. Customer Complaints Reduction. A fast food restaurant wants to reduce the
number of complaints it receives. It needs an operational definition of complaints
before it can start to measure it.
• Staff. A customer service organization wants to be able to
assess how knowledgeable its staff is at meeting the customer needs. It needs
an operational definition to help establish a baseline.
• Process.
Measurement Plan
Questions to Answer
What precise data will be collected?
Performance measurement?
Causes of process deficiencies?
Do we analyze all relevant data or a
sample?
What is the right sample size?
What is the right frequency?
What will be the sample selection
method?
What tools are necessary?
What formats will be used?
What logs will be kept?
Do we need a computer?
• Step 2: Measurement Plan
• Determining current process performance usually requires the collection
of data. When developing a measurement plan ensure that:
The data collected is meaningful
The data collected is valid
All relevant data is collected concurrently
What logistical issues are relevant?
Who will collect data?
Where is the data located?
When will it be collected?
What additional assistance is required?
What you want to do with the data?
Used daily, weekly,etc.
Identify trends in the process data
Identify deficiencies in the process
Demonstrate current process
performance
Identify variation is a process
Identify a cause and effect relationship
Develop a Measurement Plan-
Types of Data
• Before data collections starts, classify the data into different types:continuous or is
important because it will:
– Provide a choice of data display and analysis tools
– Dictate sample size calculation
– Provide performance or cause information
– Determine the appropriate control chart to use
– Determine the appropriate method for calculation of 6s
Continuous
Measured on a continuum
Objective
•Time
•Money
•Weight
•Length
Subjective
•Satisfaction
•Agreement
•Extent
•Type of error
Discrete
Count or categories
Objective
•Count defects
•# approved
•# of errors
•Type of document
Subjective
•Yes / No
•Categories
•Service performance
rating(good,poor)
•Satisfaction
•Agreement
Two Basics Types of Data
• Continuous or variable data-measured on a continuum or scale.
Usually continuous measures can be divided into parts and still
make sense. For example:
– Time can be divided into days, hours, minutes, or seconds (cycle
time)
– Money can logically be divided or specified in increments (sales,
costs, losses)
– Satisfaction if measured with a continuous scale,(
dissatisfied, dissatisfied, neither satisfied nor dissatisfied, satisfied,
very satisfied)can logically be calculated and expressed in an
average level of satisfaction on a scale.
• Discrete, categorical or attribute data-measured by
example:
– Defects(yes/no,approved/disapproved,pass/fail,met customer
requirement/did not meet customer requirement)
– Categories(days of the weed, locations, type of customer, type of
product, risk-low/medium/high)
– Satisfaction(poor/fair/good/excellent or dissatisfied/satisfied)
Cause Data
Performance Data
• Descriptive
• Focus on Results
• Helps establish a baseline
• Measures performance of a
process
• Should be collected first
Cause Data
• Focuses on why process performs the way it
does
• Helps identify potential root causes
• Collect this type of data to explain
performance problems
• Cause data, on the other hand, focuses on why the process performs as it does. Cause
data supports problem solving by helping to isolate root causes of problems.
• Don’t assume, however, that you shouldn’t gather cause data and performance data at
the same time. Remember, resourcefulness is one of the keys to effective data collection.
Sometimes, you’ll know enough about potential causes to measure performance and
isolate potential causes at the same time.
• Most of the time, however, you won’t know enough about potential causes until you’ve
determined your processes current performance level. Be prepared to document current
performance first, then brainstorm potential causes and collect additional data related
to those causes at a later date.
Performance and Cause Data
Step 2: Develop a Measurement Plan
Each Six Sigma improvement team should complete a measurement plan
that contains the following information:
Example:Cycle time for loan application processing
How will data be used? How will data be displayed?
•Identification of the Largest Contributors
•Identifying of Data is Normally Distributed
•Identifying Sigma Level and Variation
•Root Cause Analysis
•Correlation Analysis
•Pareto Chart
•Histrogram
•Control Chart
•Scatter Diagrams
Performa
nce
measure
operationa
l
Definition
Data
Source and
Location
Sample
Size
Who Will
Collect the
Data
When Will
Data be
Collected
How Will
Data be
Collected
Other Data
that should
be
Collected
at the same
time
Time to
process a
loan
applicatio
n
Fax
date,time
Decision
fax date,
time
Loan
applications
Representat
ive fax
center
289 Tim Smith
Dave Mann
During the
first weed of
the month,
10/1/99 to
10/7/99
Randomly
selected
from
September
‘99
Type of loan
Amount of
loan Dealer
Time of day
Day of week
Step 2: Develop Data Measurement Plan
• Example: Cycle time for loan application processing
Performa
nce
measure
operational
Definition
Data
Source and
Location
Sample
Size
Who Will
Collect the
Data
When Will
Data be
Collected
How Will
Data be
Collected
Other Data
that should
be
Collected at
the same
time
Time to
process a
loan
applicatio
n
Fax
date,time
Decision fax
date, time
Loan
applications
Representati
ve fax center
289 Tim Smith
Dave Mann
During the
first weed of
the month,
10/1/99 to
10/7/99
Randomly
selected
from
September
‘99
Type of loan
Amount of
loan Dealer
Time of day
Day of week
Considerations for other data that should be collected at
the same time:
How will you display the data?
What do you want to do with the data after it is collected?
How do you want to stratify the data?
What data might you need to identify and verify root cause?
Data collection is a balance between time money and accuracy (getting the data you need).
Step 3: Collect Data
• Follow the plan—note any deviations from
the plan
• Consistency—avoid bias
• Observe data collection
Discussion on Data Collection Experience
Obtaining the Measurements
• The data collected will only be as good as the collection system itself. In
order to assure timely and accurate data, the collection method should be
simple to use and understand. There are several most
common are:
– Checksheet –a simple log of “tick marks” representing the volume and type of
work
– Time stamps- a recording of the time that each activity begins and ends.
Example: Checksheet
Applications Returned for Missing Data
• All data can be collected manually(writing in the log, recording the time,
etc.)or automatically. Automatic data collection assures accurate and timely
data, and removes the burden of collection from the operator of the process.
But, it can be very expensive to set up. It usually involves computer
programming and/or hardware. For most initial efforts, a paper log is the
most cost effective form of data collection.
Reason Missing Incorre
ct
Social Security Number ///// //
Street Address /// /////
Phone Number /////
////
Employment
Information
/// /
Identify Tools to Help You Collect Data
• Hint: Identify types of data you need to collect before you design the form
Checksheets
Simple data collection form which
helps determine how often
something occurs.
Concentration Diagrams
Pictorial checksheet which helps you
mark where something occurs or the
type of problem.
Wasteful Energy Habits Week
1
Week
2
Week 3 Total
Long showers /// / // 6
Lights left on //// /// //// 11
Windows left open // / 3
AC set below 72° / // // 5
Door left open ///// ///// /// 13
Total 15 12 11 38
Name
Address ///
Telephone//
Type of Loan Needed/////
Income Level///// /////
Other Loan Information/////
Banking Information///// //
Process Distribution Checksheet
.4
6
.4
7
.4
8
.4
9
.5
0
.5
1
.5
2
.5
3
.5
4
.5
5.
.5
6
.5
7
.5
8
.5
9
.6
0
.6
1
.6
2
//
//
/
//
//
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//
//
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//
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//
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/
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//
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// //
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//
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2 3 5 8 10 18 14 22 15 13 10 9 5 4 3 1
Totals
One-Factor Attribute Checksheet
Sampling
Using a sample of data you draw conclusions about the entire
population of data. This is known as “statistical inference.” Sampling
saves costs and time. Sampling provides a good alternative to
collecting all the data. Identifying a specific confidence level allows
us to make reasonable business decisions.
Entire population
of data sample
Statistical
inference
parameters：
M，S
Statixtics:
X，S，etc.
Sampling from a population
analysis
Sampling Situations
Different situations which dictate
sampling techniques:
• To analyze and control a
process
• To describe a large
population(., types of
customers and buying
behavior)
XXX
sample
Average cycle time(Xbar）
No. of defects
Proportion defedtive
Standard deviation（S）
XXXX
sample
X X X
X
X X
X
Systematic
Process
sampling
Typical
Descriptive
statistics:
Random
sampling
from a
population
Sampling
TypesProcess-subgroup sampling
XXX
sample
X XX
Day 1 Day 2 Day 3
Sample from a particular step in the process each day(hour, week, month)
Population-stratified random sample
Randomly sampling within a logical category(location,shift,product,ect)
A
A B
B
C
C D
D
A
A
B
B
C
C
D
D
sample
Sampling Considerations
• Where
– Location in the process where process steps directly affect outputs (strong
relationship)
– Maximize opportunity for problem identification(cause data)
• Frequency
– Dependent on volume of transactions and/or activity
– Unstable process—more frequently (use systematic or subgroup sampling)
– Stable process—less frequently(use sample size formula)
– Dependent on how precise the measurement must be to make a meaningful
business decision
• considerations
– Is the sample representative of the process or population?
– Is the process stable?
– Is the sample random?
– Is there an equal probability of selecting any data point?
– The answer to each of these questions must be yes before we can draw
statistically valid conclusions
Determining Minimum Sample Size
Minimum sampling size from a population or a stable process can be estimated
from the following formulas:
Continuous Data Sample Size
For continuous data:
N=minimum sample size required
S=estimate of standard deviation of the population or
process data
D=level of precision desired from the sample in the
same units as the “s” measurement
=constant representing a 95%confidence interval
where
Discrete Data Sample Size
For discrete or proportion data:：
where
n=minimum sample size
p=estimate of the proportion of the population of process
which is defective
D=level of precision desired from the sample in units of
proportion
=constant representing 95%confidence interval
the highest value of p(1-p) or p=.5
Benefits of Continuous Data
Usually requires a smaller sample
More information for stratification and root cause analysis
Determining Minimum Sample Size
Formula for Small Populations
• Making adjustments in the minimum sample size required for small
populations:
• Sample size formula assumes:
– a 95% confidence interval
– A small sample size(n) compared to the entire population size(N)
– If n/N is greater than , the sample size should be adjusted to
The proportion formula should only be used when nP≥5
Formula for Small Populations
Example: Processing loan applications
Given:
• The sample size formula shows that you need a minimum sample size
of 289.
• You have only processed 200 units.
Solution: The correct minimum sample size would be:
or 119-minimum sample size required
Minimum Sample Size
Example:Sample Size Calculation—Continuous
A sigma team samples a loan process to determine the average
processing time,and wishes to estimate the average time within one day.
Based on previous sampling, the team has estimated the standard
deviation of the current loan processing time as 4days.
What is the minimum sample size required to be able to estimate the
average with the required precision?
Minimum Sample Size
Example: Sample Size Calculation—Discrete
Another sigma team determines the minimum sample size required
for the proportion of service contracts that require rework at the
client approval meeting. From interviews, the team has concluded
that approximately 25% of the contracts contain errors and require
rework. They wish to determine the % requiring rework within 5%.
n=（)(.1875)=289 contracts
Example: Sample Size
Objective:
– Determine the appropriate sample size.(10 minutes)
Instructions:
– Use the room service breakfast example. Breakfast is
scheduled for the time the customer requests delivery.
• The customer requirement is +/-10 minutes from the
scheduled delivery time.
• Estimated s= and D=2 minutes
• Estimated number of defects is 30%（P=；D=5%）
– Determine the minimum sample size for both
continuous and discrete data.
Answer: Sample Size(continued)
Continuous
Discrete
Example: Sample Size Formula
• Objective:
– Determine the appropriate sample size formula to use.(30 minutes)
• instructions：
– At your tables determine the right formula(proportion/discrete or
continuous)to use and calculate the sample size for each situation.
the average cycle time within 2 hours. The estimated standard
deviation is 8 hours. What is the minimum number to sample?
team collected 100 observations to determine the proportion defective.
They found 20% to be defective. How accurately can they estimate the
proportion defective?
have a customer survey with 2 categorical questions and 8 interval
statements. You estimate that at least one option of a categorical question
will be answered by approximately 50% of the respondents and you want to
be able to detect a difference within±5%。For the continuous statements
you want to be able to detect a difference of at least ½ a point. The highest
estimated standard deviation for any of the statements is expect the
response rate to be 25%.how many surveys do you have to send out and
how many completed surveys do you need returned?
Answers to Sampling Exercise
3Discrete Calculation
Continuous
must send out 4*minimum sample or 4*385=
1,550
Step 4: Display and Evaluate Data
Display data:look for
data errors and
outliers.
Evaluate the data
collection
methods:determine
if the methods used
to collect data have
provided consistent
and representative
data.
Scatter
HistogramRun
Pareto
Evaluate Data
• Has your data collection method:
– Given you dependable data?
– Provided consistent information throughout the data collection period?
– Provided a reliable set of data?
– Given representative data?
• If you repeat the data collection will you get similar results?
• Does the data collected provide the information you need?
Review and Transition
• In we learned:
– The different types of data and how each type can provide the team with
different insights and knowledge of a process.
– How to develop operational definitions and data collection plans that build
validity and consistency in the data which the team gathers.
• In , understand Variation, we will learn:
– The concept of variation and how a process can be evaluated by assessing
its variation
– How to plot and calculate the variation of the team’s business process
– The use of the business statistical software package with hands-on
experience
Determine What
to Measure
Manage
Measurement
Understand
Variation
Determine
Sigma
Performance
Excel Team
Performance
Operational Definitions
Data Collection Formats and Plans
Measure Performance
Understand Variation
Understanding Variation
Objective
To develop an understanding of the importance of variation in
managing processes and how to measure variation.
Key Topics
•What is Variation?
•Charting Variation?
Determine What
to Measure
Manage
Measurement
Understand
Variation
Determine
Sigma
Performance
Excel Team
Performance
Baseline Performance
Variation
• Variation means that a process does not produce exactly the same result
every time the product or service is delivered.
• Variation exists in all processes.
• Measuring and understanding variation in our business processes helps
identify specifically what the current level of performance is and what
needs to change in order to reduce the variability and therefore reduce
the defects delivered to customers.
Data Variation
What Causes Variation?
Suppliers Process Inputs Business Process Process Outputs
Critical
Customer
Requirements
Variation in the
output of processes
causes defects
Defects
Root cause analysis
of variation leads
to permanent defect
reduction
What is Variation?
Delivery Time
Critical Customer
Requirement = 10 days
Defects:Service
unacceptable to
customer
F
r
e
q
u
e
n
c
y
o
f
D
e
l
i
v
e
r
y
T
i
m
e
s
s = Variation or data spread
x = days
1 2 3 4 5 6 7 8 9 10 11 12
Variation Reduction
• If we reduce variation, then fewer observations will
fall above the customer requirement of 10 days.
Delivery Time
Critical Customer
Requirement = 10 days
Defects: Service
unacceptable to
customer
F
r
e
q
u
e
n
c
y
o
f
D
e
l
i
v
e
r
y
T
i
m
e
s
s =Variation or data spread
x = days
1 2 3 4 5 6 7 8 9 10 11 12
Variation and Mean Reduction
• If we reduce both the average delivery time and the variation in
delivery time, we can further reduce those times that do not
meet customer requirements.
Delivery Time
Critical Customer
Requirement = 10 days
Defects:Service
unacceptable to
customer
F
r
e
q
u
e
n
c
y
o
f
D
e
l
i
v
e
r
y
T
i
m
e
s
x = 6 days
1 2 3 4 5 6 7 8 9 10 11 12
What Does Variation Mean to
Sigma?
• Measuring variation means that we can clearly define how well
we are meeting customer requirements.
• By observing or measuring the process over time you can
determine the mean and standard deviation, and therefore the
performance of the process against customer requirements.
• Sigma requires that we measure two elements:
– Process performance
– Customer requirements
• The goals of Sigma Business Improvement are to center the
process well within customer requirements through reducing
variation,first by eliminating special causes of variation,and then
the common causes that are necessary in order to center the
process outputs fully within customer requirements.
Charting Variation
Histograms
A histogram is a bar graph that displays the results for a
sample of performance data (daily commuting time, for
example) in picture form. This picture is sometimes
called a frequency distribution because it shows clearly
how frequently each separate value appears in the data.
Charting Variation-Normal Distribution
single peak equal to average
symmetrical sides continuously declining
on both sides
X
Charting Variation-Standard Deviation
The standard deviation noted as σ -for the population
s-for the sample
A normal distribution is completely described when we know the x and s of the
data.
Normal Distribution
X Xi
S
Charting Variation-Standard Deviation
• The standard deviation noted as -for the
sample
is an efficient way of expressing the average spread
or variation of a set of data.
Standard deviation for a sample is calculated as:
Where x=the average of all the data
xi=the individual data point value
n=the number of data points
A normal distribution is completely described when we
know the x and s of the data.
Standard Variation: Yield &The Normal Curve
• The normal curve can also be partitioned as shown below and
because of its perfect symmetry,the following rules apply:
Number of standard
deviations on either side of the
team
% of data between
these limits
1
2
3
4
5
6
X
Number of Standard Deviations form the Mean
-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6
Exercise:Histogram Interpretation
Objective
To practice interpreting
histograms.(15 min.)
Instructions
Analyze the following
Histograms and answer
the corresponding questions
below.
What type of distribution is this?
What may this represent?
What type of distribution is this?
What may this represent?
10 23 36 49 62 75 88 101 114 127
3 6 9 12 15
Charting Variation-Run Charts
Three Different Run Charts with the Same Distribution
26
25
24
23
22
21
20
19
18
17
16
15
14
26
25
24
23
22
21
20
19
18
17
16
15
14
26
25
24
23
22
21
20
19
18
17
16
15
14
16 17 18 19 20 21 22 23 24
X X X X X X XXX
X X X X X
X X X
X
Charting Variation-Control Charts
26
25
24
23
22
21
20
19
18
17
16
15
14
UCL
CL
LCL
M T W TH F M T W TH F M TH F M T W TH F M T W TH F M
Days
C
om
m
ut
in
g
T
im
e(
m
in
s)
Control charts-Basics
Control charts
• Help manage variation
• Help monitor the process
• Provide an easy to understand visual indicator of
process performance
• Help sigma improvement teams understand the root
cause of the variation in a process
Control charts-Basics
• Steps to building an appropriate control chart
• the type of data
• data consistently with control charting in
mind
• the appropriate control chart
• the control chart
• process performance
• corrective action
Control Chart Theory
• The control chart, invented by is a
common sense way to determine if a process is
exhibiting common cause or special cause variation.
• This is another way of asking, is the process “in
control”or “out of control?”
• Common Cause Variation:Variation that is random
and inherent in the system.
• Special Cause Variation:Variation that is
unpredictable, intermittent and usually related to only
one element of the process.
Control Chart Theory
• Another definition used in manufacturing is that common cause
variation is due to all the following categories or elements of a
process collectively.
While special cause variation is that caused by only one of these
categories.
Service and sales versions of these categories may include the
following:
Service/Sales Categories
measureme
nt
Methods machines
Material Mother Nature People
People Marketing Location
Products Distribution Mother Nature
Procedures Policies Service
Rationale for the Three Sigma Limits
• Dr. Shewhrt determined that selecting the three sigma
limits for a control chart would help people improve the
process profitably… That is plus or minus 3 sigma from
the average was a good balance between the two types
of statistical errors.
1）Type I, Finding a special cause when there is not one
and,
2）TypeⅡ, not finding a special cause when there is one
Selecting the Appropriate Control Chart
*Proportion Defective:The entire unit is either good or bad. A proportion
can be calculated-binomial assumptions apply.
**Count of Defects:There is no limit to the number of defects that can be
counted. It is not possible to count the non-defects. Poisson
assumptions apply.
Type of Data
Discrete Continuous
C Chart
Constant sample size
U Chart
Variable sample size
NP Chart
Constant sample size
P Chart
Variable sample size
Individuals, Moving Range & EWMA
Sample size=1
Sample size<6
X-bar and R
Sample size>=6
X-bar and S
Count of defects** Proportion of defective*
The Central Limit Theorem
The central limit theorem states:
“The shape of the sampling distribution of X-Bar will be a
normal curve, no matter what the shape of the
population distribution.” Koosis
The conclusion is that X-Bar Charts will almost always
reflect a normal distribution regardless of the
distribution of the individual data points.
*Proportion Defective:The entire unit is either good or bad. A
proportion can be calculated-binomial assumptions apply.
**Count of Defects:There is no limit to the number of defects that can be
counted. It is not possible to count the non-defects. Poisson
assumptions apply.
Type of Data
Discrete Continuous
C Chart
Constant sample size
U Chart
Variable sample size
NP Chart
Constant sample size
P Chart
Variable sample size
Individuals, Moving Range & EWMA
Sample size=1
Sample size<6
X-bar and R
Sample size>=6
X-bar and S
Count of defects** Proportion of defective*
The Binomial Assumptions are as Follows:
1）The count of discrete data comes from a distinct subgroup N.
This subgroup is usually more than 50 items.
2）There are two possibilities of result:conformance of non-
conformance
3）Each result is independent of the other.
The assumption is that both the total number of units and the total
number of non-conforming units are this case the unit or
item is either conforming or non-conforming,and the proper control
chart is either the NP-Chart if the sample size is constant, or the P
Chart if the sample size is not.
Binomial Measurements Have Two Possible
Outcomes
Defective,Not Defective
Yes, No
Conforming,Not Conforming
Satisfied, Not Satisfied
Example: Loan Approval
Process
Note:
Not all percentage are
discreet or count
.%yield. If both the
numerator and the
denominator are determined
by measuring, the % is
considered continuous data
file C:\6Sigma\Control Charts\
STAT>CONTROL CHART>P
variable to be charted <Loans Approved>
column number containing the subgroup size in
“Subgroups in”<Total Loans>
P Chart for Loans Not Approved
*Proportion Defective:The entire unit is either good or bad. A
proportion can be calculated-binomial assumptions apply.
**Count of Defects:There is no limit to the number of defects that can be
counted. It is not possible to count the non-defects. Poisson
assumptions apply.
Type of Data
Discrete Continuous
C Chart
Constant sample size
U Chart
Variable sample size
NP Chart
Constant sample size
P Chart
Variable sample size
Individuals, Moving Range & EWMA
Sample size=1
Sample size<6
X-bar and R
Sample size>=6
X-bar and S
Count of defects** Proportion of defective*
The Poisson Assumptions are as Follows:
1）The chance of an error or incident is small (less than
five percent)
2）Events occur independently of one another
3）The area of opportunity for the error or event is constant
for each subgroup.
Think about this as counting the number of errors where
the opportunity for error is unlimited. For instance there
would be no limit to the number of blemishes that could
be found on an automobile,or the number of accidents
that could occur in a population of workers.
Poisson Distributions are Characterized by the
Following:
Probability of defect is low
Opportunity for defect is high
You cannot count the non-defects
Data are characterized by the number of occurrences in
a sample
Example:Automobile Warranty Claim Process
file C:\6Sigma\Control Charts\u-
STAT>CONTROL CHART>U
variable to be plotted and,
denote sample size by
selecting.“Subgroups in” cars delivered
15201501December 8
16275601November 7
19270770October 6
22600870September 5
15770595August 4
18420776July 3
246751000June 2
16020630May1
1ST6MONTHS
No OF CARS
DELIVERED
No OF
CLAIMS
Month
U Chart for No. Of Claims
C Chart for Errors in 100 Applications
Exponentially Weighted Moving Average
Control Charts
• When the process average changes slowly over time, a potentially
more sensitive control chart is the exponentially weighted average
chart. This control chart is sensitive to small shifts in data and is
useful when early detection of a process shift is critical to the
business process owners.
Example
EWMA Calculation
Historical Mean=100
()+.2(81)817
()+.2(71)716
()+.2(82)825
()+.2(102)1024
(1000)+.2(120)1203
()+2(89)892
()+2(114
)
1141
EWMA
(WEIGHT=)
CALCULATIONINDIVIDUA
L DATA
SEQUENCE
Control Chart Maintenance
• Control charts should be based on common cause this
reason special causes should be investigated and removed from the
data. This maximizes the possibility of detecting special cause
variation.
• The minimum number of data points to construct a control chart
should be 20-30.
• Question:Under what circumstances should the limits on a control
chart be changed?
Guidelines for Recalculating
Control Limits
Do the data display a pattern clearly different than the
past?
Is the reason for the change known?
Do we expect the new process results to continue?
OK to calculate limits from new data.
ⅠChart for Cycle Av for Chart M1
ⅠChart for Cycle Av for Chart M2
I Chart for Cycle Average
Should customer specifications be placed
on the control chart?
Relating “Voice of Customer” to
“Voice of Process”
State
of Process
Control
Within Spec Outside Spec
In control
O
ut of control
Introduction to Statistical Comparison
• Hypothesis tests answer the question “Is Group 1significantly
different from Group 2”. Groups 1&2 could be the cycle time of a
process before and after a process improvement, or defects at one
location 1 and location 2.
• Hypothesis tests include continuous and discrete data and could
include more than two groups. Test included are:
– t-Tests
– ANOVA tests
– Correlation
– Regression
– Chi-squared tests
Introduction to Statistical Comparison-Is
Sample A different from Sample B?
Entire
Population
of data
Sampl
e
Entire
Population
of data
Sampl
e
Parameters:
μσ
Parameter：
μσ
Sample A
X and S
Sample B
X and S
Introduction to Statistical Comparison
• We are hypothesizing that there is no difference between
the populations being tested. We are testing the null
hypothesis that the groups being compared are not
different.
– A p-value is reported for each hypothesis test.
– The p-value has a range between and .
– The closer the p-value is to the more likely the groups being
are different.
– A p-value less than is considered significant since we are
testing to prove that there is a difference.
– The p-value is the probability that the observed difference could
be caused by sampling variation alone.
Introduction to Statistical Comparison
• We are hypothesizing that there is no difference between
the populations being tested. We are testing the null
hypothesis that the groups being compared are not
different.
– A p-value is reported for each hypothesis test.
– The p-value has a range between and .
– The closer the p-value is to the more likely the groups
being are different.
– A p-value less than is considered significant since we
are testing to prove that there is a difference.
– The p-value is the probability that the observed difference
could be caused by sampling variation alone.
Hypothesis Tests Assumptions
• Sufficient samples must have been drawn
randomly from a population
– Statistical independence assumption
• That the data are normally distributed
Hypothesis Testing
• Suppose a six sigma team wished to compare the average salaries
of employees from two different departments. First they collect a
random sample of people from the population of each department.
Next they plotted the histograms from each department.。
1
3
6
5
1
33
2
1
XCS=Average Salary=
3
2
3
4 4
3 3
1
2
Xm= Average Salary =
Marketing
F
re
qu
en
cy
F
re
qu
en
cy
Customer Service
Hypothesis Test Interpretation
Our hypothesis test,often,will be to “disprove the null hypothesis.”
To accomplish this,we will return to the concept of p-value stated earlier.
This time we will define P as follows:
If p≤,we declare a significant difference exists.
“the p-value is the probability that the observed difference between Xcs
and Xm is caused by sampling variation.”
Another way to state this is:
“The p-value is the probability that these two samples came from the same
population.”
The conclusion here is that we will not declare a statistically significant
difference exists unless there is less than a 5% chance we have made a
mistake.
0 P
Not Different
Types of Error
Hypothesis Test Result
Not Different Different
OK
OK
TypeⅠerror
α=probability of
TypeⅡerror
β=probability ofDo not take
action or make
decision when
should have.
Take action or
make decision
when should not.
D
if
fe
re
nt
N
ot
D
if
fe
re
nt
Actual
Hypothesis Tests
Discrete Continuous
Chi Square
Regression
T-Test
ANOVA
DOE
Logstic
Regression
Y
X
C
on
ti
nu
ou
s
D
is
cr
et
e
Summary of Statistical Tools 1
Tool What Type of Data When to Use How to apply this
Tool in my work
Histogram Visual display of one variable
showing data center,
spread,shape and outliers.
Continuous or
discrete
large amounts of
data
get a ‘feel for the data’
compare actual description to
customer specs
Multivari
Charts
Bar chart comparison of sub-
groups on one variable.
Continuous or
discrete
To visually compare sub-groups
by individual data points and the
mean. In MINITAB 12 only. To
identify major source of variation.
Box Plots Visual display of the summary
of Y data grouped by category
of X.
Y=continuous
X=discrete or
categorical
Summary display to visualize
differences in data center, spread
and shape across categories.
Run Charts Plots observation is time
sequence
Y=continuous or
discrete
To view process performance over
time for trends,sifts or cycles.
Control
Charts
Plots observations in time
sequence against a mean and
control limits.
Y=continuous or
discrete
To monitor the process in order to
control and improve process
performance over time for trends,
shifts or cycles. To identify special
causes.
Scatter
Diagram(Plot)
Plots a response Y versus a
predictor X.
Y=continuous
X=continuous
To understand the possible
relationships between two
variables. To identify possible root
causes which are related to Y. Do
not use with special
Summary of Statistical Tools 2
Tool What Type of Data When to Use How to apply this
Tool in my work
Behaviorally
Anchored
Scales
A response scale on which
specific points are named or
described to clarify the
differences between points.
Discrete/Categorical:
Nominal (name)
Ordinal (order)
To translate subjective or qualitative
issues into data (discrete or
continuous).To reduce variation in the
response measured.
t-Test Determine if there is a
difference between two
groups.
Y=continuous
X=discrete or
categorical
if sample average=specified value
if 2 sample means are equal
t: to reduce variation when
comparing two sample averages
Analysis of
Variance
Determine if there is a
difference among many
groups.
Y=continuous
X=discrete or
categorical
（2 or more Xs)
Determine of there is a statistically
significant difference among the groups.
Discrete
Data:
Chi Square
Determine if there is a
difference for observed
frequencies of 2 discrete
variables.
Y= discrete or
categorical
X= discrete or
categorical
Determine if there are relationships
between two discrete variables.
Regression(Lin
ear & Multiple)
Summarizes, describes,
predicts and quantifies
relationships.
Y=continuous
X=continuous or
discrete
if there is evidence of a
relationship between Xs and Ys.
data to develop a mathematical
equation to quantify the relationship.
root causes.
predictions using the model.
Logistic
Regression
Summarizes, describes,
predicts and quantifies
relationships.
Y=discrete
X=continuous or
discrete
if there is evidence of a
relationship between Xs and Ys.
data to develop a mathematical
equation to quantify the relationship.
root causes.
predictions using the model.
Design of
Experiments（
DOE）
Systematic and efficient
proactive approach to testing
relationships.
Y=continuous or
discrete
X=continuous or
discrete
To establish cause and effect
relationship between Ys and Xs. To
identify ‘vital few’ Xs.
Review and Transition
In Understanding Variation, we learned:
– The concept of variation and how a process can be evaluated by assessing its
variation.
– How to plot and calculate the variation of the team’s business process.
– How to use a business statistical software package.
• In Determine Sigma Performance, we will learn:
– The various calculations associated with determining process sigma.
– How to calculate the sigma performance of the team’s process.
Determine What
to Measure
Manage
Measureme
nt
Understan
d
Variation
Determine
sigma
Performan
ce
Excel
Team
Performance
Baseline Performance
Measure Performance
Determine Sigma Performance
Determine Sigma Performance
Objective
To determine the right method for calculating Sigma performance.
Calculate process sigma performance using the appropriate method.
Key Topic
– Calculating Sigma Defined
– Continuous Data Calculations
– Discrete Data Calculations
– Rolled-up Sigma Calculations
Determine What
to Measure
Manage
Measuremen
t
Understand
Variation
Determine
sigma
Performanc
e
Excel
Team
Performance
Process Performance Based
on Customer Requirements
Steps for Calculating Sigma
• The method to be used for calculating sigma
performance depends on the type of process
performance data used(continuous or discrete).
• Steps in Calculating Sigma Performance
• Critical Customer Requirements (CCRs)
• types of variable and output measures to be
collected
• nature of data collected (short term/long
term)
• Sigma Performance
Calculating Sigma Defined
Critical Customer Requirements (CCRs)
– Critical customer requirements must be determined by/from the
customer and defined as key output variables or CTQs
– The level of performance required on these key output
measures define the critical customer requirement that the
Sigma Calculation is based on. For example, cycle time<5 days
Determine sigma calculation method required. Identify and
collect data on each, based on Critical Customer
Requirements.
the type of data for each performance
measure to be used to calculate sigma.
– Continuous
– Discrete
Collect Data
•Identify the specific measures required
•Develop an operational definition
•Determine minimum sample size
•Collect data
The output measure are derived from critical customer requirements as
described in section . Often more than one output measure is
important to customers. For example, time of delivery (cycle time)and
quality of the product or service may both be important to customers.
You could have different Sigma performance levels for each of these
measures.(See Sigma Roll-up calculations.)
of Data
Short-Term and Long-Term Data
Output measures tend to vary over time due to many internal and
external example, supplier quality may vary, affecting the
quality of information required for your process. New competitors may
emerge affecting the marketplace and customer expectations. It is
important to separate short term performance from long term in order to
determine the potential for the current process. Classify the nature of
the data measured as either short term or long term.
•Short term
– 30-50 data points minimum
– no special causes or shifts in performance
•Long term
– 100-200 data points minimum
– includes special causes or possible shifts in performance
Short-term versus Long-term Data
A
B
C
D
E
A+B+C+D+E
Long-term Data
includes the effects of
special cause variation
Time
Short-term Data
generally dose not
include special cause
variation
Sigma Performance
Following are the basic methods for calculating Sigma
• All methods require the minimum sample size for 95%
confidence.
• Data should be randomly selected to represent the population.
Method Type of Data Comments
Ⅰ.Z-Score Continuous The data must be roughly
normally distributed. Calculate
the Z-score and yield of the
process.
Ⅱ.DPMO (Defects per Million
Opportunities)
Discrete N>1000 Must have at least 5 defects or 5
non-defects.
Ⅲ.Discrete(High Volume) Discrete N>1000 Less than 5 defects
Ⅳ.Discrete (Low Volume) Discrete N≤1000 Less than 5 defects
Ⅴ.Sigma Roll-up Discrete or Continuous Combine yields from a business
point of view.
Sigma Performance
Select the appropriate method
Calculating process Sigma
Ⅰ
Continuous
Ⅱ
DPMO Method
Discrete
Ⅴ
Sigma Roll-up
Ⅲ
High Volume
Ⅳ
Low Volume
The concept of Six Sigma
Area Under the Standard Normal Curve
Defects
-3 -2 -1 0 1 2 3
0
10
20
30
40
50
60
70
80
Process Yield
.6794
Z or sigma=
Z Process
Calculating Sigma for Continuous Data
-Example
Example:
• A customer service counter processes applications for credit
cards at the store location. The average time for processing
application or ‘Xbar’ is minutes; the standard deviation of ‘s’
is minutes; and critical customer requirement or CCR is 10
minutes. CCR
Cumulative Probability or Yield
S=
Credit Card Application Processing Time
10minutes
Z10=
X-Xbar
S
=
Z10=
0
Sigma Calculation Table
*note:
This table does not include a
shift. If you enter this
table with long-term data
then you calculate long-term
Sigma and if you enter with
short-term data then you
calculate short-term Sigma
Both Motorola and GE Capital
assume that you enter their A
bridged Sigma Table with long
-term data and exit with a
short-term Sigma. In order to
do this,they assume a shift
of Sigma between short-
and long-term Sigma.
Therefore,if long-term yield
equates to a standard normal
area of Sigma(or
DPMO)then they assume a
shift and report short-term
Sigma of .
*
*
Upper and Lower CCRs
Defects
Defects
0 10-10
Delivery Time
Combine into a single yield
Calculations
Defects greater than USL=(%)
Area from zero to USL=1-
=((%)
Defects less than LSL=(%)
Area from zero to LSL=(%)
Yield==(70%)
Look up Yield of in Sigma Table
Z=(approximately)
-3 -2 -1 0 1 2 3
0
10
20
30
40
50
60
70
80 USLUSLUSL
.013
.70 or
yield=70%
Standard Normal Values of Delivery Time
Sigma Performance
I
Continuous
II
DPMO Method
Discrete
V
Sigma Roll-up
III
High Volume
IV
Low Volume
Calculating Process sigma
Select the
appropriate
method
Breakfast Example
• Now let’s review an example of how process
outputs, critical customer requirements,and the
standard normal distribution are combined to
determine the process sigma. A hotel provides
room service meals to its guests,and from
numerous guest surveys and research, has
designed a service that guarantees a breakfast
meal delivery within 10 minutes of the time
requested by its guest. They have determined
an early delivery will inconvenience the guest
as well as a late delivery, especially in the
morning.
• Data from a one week operation has been
gathered and is shown here. What is the
process sigma of the breakfast delivery?
50
-10 0 10 20 30
100
0
Fr
eq
ue
nc
y
Delivery Time Deviation
Low
Customer
Limit
Upper
Customer
Limit
Target
Defects
Calculating Sigma with Discrete Data
• Taking another look at the “Breakfast Delivery” data we’ve been
using, let’s examine a very direct way to calculate process yield.
• By examining the raw data, we can count the number of delivery
times that do not meet customer requirements and translate that
directly into a defect calculation referred to as Defects Per Million
Opportunities, or DPMO。
DPMO Defined
• DPMO=Defects Per Million Opportunities
• =1M x
where：D*=total number of defects counted in the sample: a defect
defined as failure to meet a CCR or Critical Customer
Requirement
• N=number of units of product or service
• O=number of opportunities per unit of product or service for a
customer defect to occur
• M=million
*There must be at least 5 defects and 5 non-defects to use the
DPMO formula.
DPMO Example
Using the previous example, let’s calculate the DPMO and the process sigma
Using this method from the data set on breakfast delivery times:
D=205
N=725
O=1（There is only one opportunity for a defect per breakfast delivery. Either the
delivery is within the customer limits or not.）
DPMO=
Using the Sigma Calculation table, enter the DPMO column and look up the process
sigma directly.
The relationship between DPMO and process sigma is as follows:
Process Yield=1-DPU
Where DPU=Defects/unit
In our example,
Process Yield == or % sigma=.058
Sigma Calculation for Breakfast Example
Objective:
– To practice calculating Sigma
Information Provided:
– Mean=5minutes
– Standard=10minutes
– USL=+10minutes
– LSL= -10minutes
the Sigma for continuous data
Review and Transition
• In Determine Sigma Performance, we
learned:
– The various calculations associated with determining process
sigma.
– How to calculate the sigma performance of the team’s process.
• In , Excel Team Performance, we will learn:
– The role of high-performance work teams in process
improvement.
– How to use team diagnostics and assessments to evaluate the
team strengths and opportunities for improving its own
performance.
Determine What
to Measure
Manage
Measurement
Understand
Variation
Determine
Sigma
Performance
Excel Team
Performance
Process Performance
Based on Customer Requirement
Measure Performance
Excel Team Performance
Excel Team Performance
Objective
To enable the team to reach a high level of performance fully
utilizing team member skills, knowledge, and experience
working collaboratively.
Key Topics
• Norming Stage
• Teaming Techniques
• Performing Stage
Determine What
to Measure
Manage
Measurement
Understand
Variation
Determine
Sigma
Performance
Excel Team
Performance
Productive Team Atmosphere
Summary of “Measure Performance”
• Determine What to Measure
– Understand the role that data plays in process improvement
– Understand the cause and effect relationships that occur inside the team’s process
– Determine the indicators needed to evaluate current process performance
• Measurement
– Understand different types of data and how each type can provide the team with different insights and
knowledge of a process
– Develop operational definitions and data collection plans that build validity and consistency in the data
which the team gathers
• Variation
– Understand the concept of variation and how a process can be evaluated by assessing its variation
over time
– Plot and calculate the variation of the team’s business process
– Gain hands on experience with the use of the statistical software package MINITABTM
• Sigma Performance
– Understand the various calculations associated with determining process sigma
– Calculate the sigma performance of the team’s process
– Calculate the rolled-up Sigma for the business
• Team Performance
– Understand the role of high-performance work teams in process improvement
– Use team diagnostics and assessments to evaluate the team strengths and opportunities for
improving its own performance
$$
CCRsCCRs
Teams
Focus
What is the
process？
Q
ui
ck
W
in
O
pp
or
tu
ni
ty
？
What Part of the Gap Does My
Customer Care About Most？
Current Performance
Gap
Gap
Desired Sigma Level
Customer
Suppliers
Strategy
Technology
Regulation
Competitors
Validated Area
of Focus
What Indicators do We Need to
Evaluate the Current Process？
Input Process Output CCR
Input Indicator
Output Indicator
Process Indicator
CCR’s
VOC
Define Opportunities & Measure Performance
Cartoon Storyboard
Process Indicator
g
g
