1.
Action Items
Who? Assign
only to a Team
Member
Date What? present When Done? Where?
2.
1. Action items. Action items are to-do's assigned to attendees at the meeting
How? Decision or Open?
2. Decisions. All decisions that may affect future choices of the group should
3. Open issues. New issues raised at the meeting but not resolved there shou
3.
d to attendees at the meeting. Record the task, the person responsible and the date agreed upon to complete the task.
choices of the group should be recorded.
g but not resolved there should be recorded so they can be carried over to a future meeting.
5.
DEFINE OPPORTUNITIES
What is Important?
revised 8/28Charter Worksheet
Team 6 Sigma Terms
Team Charter Worksheet
Define Phase Cost Savings Site
Team Charter Title:
Problem
Statement Major Milestones for this project Result/Date due
Business Case for Action (Current Situation) Vision (Opportunities)
Why have you selected this project over others? Opportunities -
Processes Affected: What pain are you currently experiencing
Value-added Proposition -
AS-IS Process - Stretch Goals and Key Benefits -
TO-BE Process -
High Level Business Requirements -
How Vision supports Business, Customers, Actors -
Consequences of No Change -
Competitive Analysis - How Vision supports SM Stategy and Scorecard -
How much is this project worth (estimate)? Scope -
Goal Statement Project Scope
11:42:1111/27/2008SIXSigmaToolstemplate-122781483509-phpapp03.xls
6.
Smart Goals Lower acceptable limit
Specific (What?)
What authority does team have?
Measurable (Where are targets?)
Attainable (How?)
Upper acceptable limit
Relevant (Why?)
Time Bound (When?)
ProjectTime-Line - Month 1 Month 1 Month 1 Month 1 Month 1 Month 1 Month 1
Actors (Alignment of Key People) Mechanisms Metrics
Team Selection
11:42:1111/27/2008SIXSigmaToolstemplate-122781483509-phpapp03.xls
7.
Stakeholders- Trigger Points Link to SM Scorecard-
Sponsor - Affect on Cash Flow and Margin-
Champion-
Key Business Partners - System Resources- ROI - Benefit - Investment
Team Leader (Owner) - What can we measure, how often, and by whom -
Training needed-
Team Members (cross functional Deliverables-
experts)-
Quick Wins-
Finance Expert-
Key Process Indicators affected -
IT Resources -
Other Resources Need Measureable Results -
Best Communication Methods -
Best Training Methods -
DMAIC deals mostly with root causes
DMADV what design features (opportunities) we have under our control that we can work on that will impact the ccrs
DMADDD Design Digitize Drawdown
11:42:1111/27/2008SIXSigmaToolstemplate-122781483509-phpapp03.xls
10.
1) Why are you working on this project rather than others? Explain quantitatively.
BUSINESS CASE & OPPORTUNITY
2) Show from schematic how vital (result)? to big Y; Analyze numbers. BUSINESS CASE
3) What are you trying to achieve X relates GOAL
STATEMENT
4) What are the critical Customer Requirements and how were they determined?
5) What are the risks of this project and is there a plan to minimize them?
6) What is our communication plan going forward (who is setting up meetings and
communicating)?
11:42:1211/27/2008SIXSigmaToolstemplate-122781483509-phpapp03.xls
13.
DEFINE OPPORTUNITIES
What is Important?
Voice of the Customer
VOB CBR's CTQ's
Voice of the Business Critical to Quality
Critical Business
Price/Unit
Requirements
Delivery time
What do comustomers
Spec Reqs
want? Quality? Cost?
Service level
Delivery? Service &
Safety?
Business Issues
Who Receives an
Output to the
Process?
External Customers -
Who pays money for
your product?
Vendors
End Customers
14.
Internal - Depts
who receive
outputs from your
process
Buyers
Commodity Mgrs
18.
E OPPORTUNITIES What complaints do you get from your
at is Important? customers about the outputs of your process?
ce of the Customer Write them in the Voice of the Business
Identify them as CBR's CTQ's or CTP's
From these identify CCR's
Identify how to measure the current status of the C
If possible, Dollarize the Cost Currently Incurred
CTP's CCR's VOC
Critical to Process Critical Customer Voice of the Customer Key measures
Cost/Unit Requirements How does this effect the
Productivity
Must have - Must be ultimate customer? What and How
Regulation Compliance
Key issues Would the Customer Pay
Rework
for it?
Safety
Training hours
19.
ints do you get from your
out the outputs of your process?
the Voice of the Business
s CBR's CTQ's or CTP's
entify CCR's
measure the current status of the CCR's
llarize the Cost Currently Incurred
Opportunity Dollarized
Sort
1
1
1
1
1
1
1
1
1
1
1
1
1
26.
DEFINE OPPORTUNITIES
What's Important?
Supplier Input Using a Survey
12 Steps to creating a Survey
Step 1 - Objective : Describe the Objective of your survey in a few bullet points.
Avoid accumulating objectives. 2 or 3 main points are a limit to
a good survey. Although it may sound restrictive, it is very
unlikely that your interviewees will be able to stomach more
than this (with the exception of highly specialized business to
business surveys focussed on professionals)
Failing to describe your objective will make you lose sight of the
overall purpose of the survey and will have serious
consequences on the length, the accuracy and the sequencing
of your questions.
Step 2 - Population : Define the total population concerned with your survey
Match this definition with the objective stated in Step 1, i.e.
neither too wide or too narrow
Step 3 - Pre-study : A Pre-study (caution, a pre-study is NOT a pre-test) should
never be overlooked
Carry out the study on a few individuals concerned with your
study
Avoid prejudices (yours or more likely, your customer's !)
which could make you jump to conclusions even before the
beginning of the survey
Step 4 - Assumptions : State clearly the points (in accordance with Step 1), that you
wish to clarify or verify, or even contradict
Do not filter these assumptions according to your own
prejudices, i.e. avoid attitudes such as quot;I don't need to check
that point because I feel that ... quot; unless you already have
evidence of the phenomenon in the first place.
Step 5 - Writing the Questions : Check the wording of your questions so that they are ...
1. Understandable
2. Unbiased
3. Non suggestive
4. Not built in a way that interviewees react defensively
27.
5. Not long-winded and repetitive so as to avoid weariness
factor
Step 6 - Contact Methods : Pre test your questionnaire on a representative sample of the
given population
If cards have to be shown to the interviewees, then add to pre
test
Choose Contact mode
1. Mail Questionnaire
2. Personal interviewing (arranged or quot;interceptquot;)
3. Phone interviewing
(New methods may include fax, Email, or even Html forms, but
one may wish to stress the difficulty in using these new media.
In a sense, they bear a strong resemblance with the phone in so
far as the interviewee is invisible and difficult to identify. These
media can only be used for short questions aimed at checking
precise assumptions. As for the population involved, it goes
without saying that Web usage is not pervasive enough - even
in the USA - in order to use this medium to survey wide
populations of interviewees. At the moment, the web is best
suited for Internet survey, but things might change in the near
future)
Modify questions in accordance with Pre test results
Modify sequencing of questions in accordance with Pre test
results
Suppress redundant questions (this is often a sore point)
Insert missing questions
If cards have to be shown to the interviewees, ensure that
contents match questionnaire.
Step 7 - Sampling : Define the representative sample
Give a sufficient size to the sample (methods of appraisal of
error margins can be used)
Step 8 - Administration : Administer Questionnaire in a neutral fashion (i.e. preventing
interviewers from introducing personal biases while asking the
questions. This may imply that these interviewers must be
either trained or skilled professionals, and that a clear, thorough
and comprehensive debriefing session takes place).
Step 9 - Non responses : Response rate is an important factor. Do not just overlook
them. They are a good indicator the quality of your
questionnaire (amongst other things, namely if a gift is sent to
all respondents)
Step 10 - Interpretation : Interpretation should be distinct from the opinion of the collector
of the data
Step 11 - Analysis : Analyse ALL the responses (avoid partial analysis)
28.
Step 11 - Analysis :
Do not extend results that are valid for the given sample to the
entire population without taking the necessary precautions
Note: A famous example of a bad survey analysis that is given
by the focus groups that led Coca Cola to change their flagship
product at the end of the 1980's. The analysts of the focus
groups were adamant that traditional Coke had to be changed,
but the assumption was wrong, the methodology too, and
eventually, the decision that arose from these groups almost led
the Atlanta giant to a disaster.
Step 12 - Report : Write a report that will describe the results comprehensively
Avoid biased conclusions
Minimise prejudices
Avoid quot;politically correctquot; conclusions
Mention numbers and percentages (both are necessary)
30.
DEFINE OPPORTUNITIES
What's Important?
revised 4/8
Supplier Input Process Output to Customers (SI
Starting Date
Ending Date
Suppliers Suppliers Inputs to Process
External Inputs Requirement supplied to SM
Internal Inputs Requirement supplied to SM
31.
DEFINE OPPORTUNITIES
What's Important? revised uploaded
upplier Input Process Output to Customers (SIPOC)
SIPOC Diagram
AS-IS PROCESS FLOW Outputs from Process Customers
(see Functional Deployment Process Map) Results of Process External to SM
Results of Process Internal to SM
32.
SIPOC stands for suppliers, inputs, process, output, and
customers. You obtain inputs from suppliers, add value
through your process, and provide an output that meets or
exceeds your customer's requirements.
Supplier-Input-Process-Output-Customer: Method that
helps you not to forget something when mapping
help processes.
33.
DEFINE OPPORTUNITIES
What's Important?
Supplier Input Process Output to Customers
Financially Valuating Key Meas
(Total Investment + Cost of Capital) / Benefit per month= Paybac
Key measures Hard Dollar Soft Dollar Operating Non Capital Financing
Benefits Benefits Activities Investing Activities
Costs (qual) Activities
Costs
Example $1,000,000.00 $50,000.00 $1,000.00 $-
37.
Total 5 year How long Savings this Where can Estimated
Benefit will it take to year Benefit be Parallel
achieve applied Benefit
Benefit? elsewhere?
$4,934,800.00 6mo ###
$-
$-
$-
$-
$-
$-
$-
$-
$-
$-
$-
$-
$-
$-
$-
$-
$-
$-
$-
$-
$-
$-
$-
$-
$-
$-
$-
0
0
0
0
0
0
0
0
0
0
39.
DEFINE OPPORTUNITIES
What's Important?
Process Mapping
PURCHASE ORDER PROCESS
CONTROLS
Rules, Regulations, Standards,
Polices, Procedures, Work Instructions, Goals,
Metrics, & People (Process Owner)
QPL/QSL: Vendor Master; Material Master;
SOP 8-4; SIC 3.2
314W checklist GVM Profile
↓
SUPPLIER INPUT (Requisitioner, PROCUREMENT PROCESS
Customer, Planner, Commodity
Team)
Materials or Information consumed Work done (by the Buyer) to the Inputs to
or transformed by the Process to Transform Inputs into Output (PO)
produce output
Master Data
Vendor, Info Record, Source List, QPL
Requisition/Demand Create and Transmit PO from Inputs.
(Schedule Sharing)
Review & Approval
MECHANISM
People, Equipment, Tools, Software that perform the Process
SAP
Buyer
Commodity Team
WWCM
PO Type
TIGERS
40.
ROCESS
OUTPUT TO CUSTOMER (Receiving, AP,
Requisitioner or Planner
and the Supplier)
Information or Product created by the
Process
Good PO that can be performed to by the
Supplier, received, and paid.
41.
DEFINE OPPORTUNITIES
What is Important?
revised 4/8
Functional Deployment Process Map
AS_IS Process Flow
Process Mapping help
Department
Sub-Process
Start boundary
42.
ss Map
What costs are
associatied with
each step
Qualitative Review (Value Added Analysis)
Would anyone pay for this?
Low
Hanging
Customer? Operations? Other fruit
43.
DEFINE
OPPORTUNITIES
What's Important?
Supplier Input Process
Output to Customers
(SIPOC)
Using Science to Influence to Reduce Uncertain
Compliance
force, have to, coersion, rules and regulations,
Conformity
do it cuz you want to, ought to, peer pressure, ethics
Internalization
Willing total and deliberate, do it cuz you want to,
Contrast Phenomenon
Contrast something or some other process with yours as a competing activity
Illustration
how it will be used-illustrate aspects of use
Context established before making a requrest
Selling something as something it is not - Humbie - Military/status symbol
Pet Rock, Chia Pet
Concensus Jim wants it
Amplifier - number of people behaving a certain way Stock goes up
Bandwagon effect = everyone is doing it Management supports
Product endorsements IBM and Intels secret too
Similar groups of people do it 6 sigma is above the ben
Like people do like things
Uncertainty
Consistancy
Phone service ads that mention once you can get someone to take a stand on something…the bigger a commitment they will b
Moto phones need to say more Value system appeal -
Get commitment to become active - even a one word commitment
Have people write down your own ideas…then share with the team
Make their commitments public - issue minutes after meeting, have instrumental member issu
Make goals public
Want voluntary commitments - easist done when you know their values then to talk someone i
Ask your friends for a commitment - people play golf together - build relationships
44.
Reciprocity - doing someone a favor and getting a return-
Scarcity
People perceive things to be more valuable if they think it is scarce or in limited accessability
Phrasing scarcity in terms of a loss is 2 - 4 times more effective than possitive
Make it Unique
Competition for something scarce
Exclusive Information is more persuasive - Limited number - only supplier
Limited run - Collectables market
Limited edition cell phone - combines several different features
Have a one of a kind cell phone - with unique personalized addons
Have a cell phone contest that cell phones have a unique phone number in them to call and may win vaca
What is the market segment? Who should we target?
Why should we target them? What's the benefit to the customer?
What are the features
45.
duce Uncertainty
Jim wants it
Stock goes up
Management supports
IBM and Intels secret tool revealed
6 sigma is above the bench
r a commitment they will begin to make
nstrumental member issue minutes, presentation sharing
es then to talk someone into something they don’t believe in
relationships
46.
r in limited accessability
to call and may win vacation to ?
47.
OPPORTUNITIES
What is Important?
Kansei
Emotional/Subjective Aspects of the Customer Requirements
Measuring Perceptions
Touch and Feel
Stylish Functional
Simple Complex
Powerful Weak
Easy to Use Hard to Use
Usability
Stylish Functional
Simple Complex
Powerful Weak
Visual Stylish Functional
Simple Complex
Powerful Weak
Stylish Functional
Simple Complex
Powerful Weak
Stylish Functional
Simple Complex
Powerful Weak
48.
Kansei
Definition
Kansei Engineering
means psychological feeling or image of a product. Kansei
engineering refers to the translation of consumers' psychological
feeling about a product into perceptual design elements. Kansei
engineering is also sometimes referred to as quot;sensory engineeringquot; or
quot;emotional usability.quot;
Random Sample of Market Segment
Know audience so words have meaning
Use a Factor analysis to compute 2-5 major customer drivers
Hard to Use
source
more...
49.
DEFINE OPPORTUN
What is Importan
Quic
Goals:
Elilminate
Change
Rearrange
Combine
Simplify
Imagine
Potential
Quick Win Easy to Implement Fast to
Opportunity (ü) Implement (ü)
Kill a Defect WEFEC
defect 1
Within sphere of influence
Easy to implement
Fast to implement
Easily reversible
Cheap to implement
50.
DEFINE OPPORTUNITIES
What is Important?
Quick Win Diagram
Benefits will be
gained
Within the
Implement Implemented by:
Cheap to Team’s
Implement (ü) Control (ü) (Yes/No)
(ü)
Kill a Defect WEFEC
defect2 defect3 defect4
52.
DEFINE OPPORTUNITIES
What is Important?
QFD = House of Quality
QFD
QFD is a good tool to employ in all phases
1. Derive top-level product requirements or technical characteristics from customer needs (Product Planning Matrix
2. Develop product concepts to satisfy these requirements.
3. Evaluate product concepts to select most optimum (Concept Selection Matrix).
4. Partition system concept or architecture into subsystems or assemblies and flow-down higher- level requirement
5. Derive lower-level product requirements (assembly or part characteristics) and specifications from subsystem/as
6. For critical assemblies or parts, flow-down lower-level product requirements (assembly or part characteristics) to
7. Determine manufacturing process steps to meet these assembly or part characteristics.
8. Based in these process steps, determine set-up requirements, process controls and quality controls to assure ac
53.
PPORTUNITIES
is Important?
ouse of Quality
http://www.qualisoft.com/
tool to employ in all phases
om customer needs (Product Planning Matrix).
lies and flow-down higher- level requirements or technical characteristics to these subsystems or assemblies.
ristics) and specifications from subsystem/assembly requirements (Assembly/Part Deployment Matrix).
irements (assembly or part characteristics) to process planning.
part characteristics.
ess controls and quality controls to assure achievement of these critical assembly or part characteristics.
54.
DEFINE OPPORTUNITIES
What is Important?
revised 4/8
Documentation Related to this Process
Doc. Number Title Creation date Last Revised
Related Specifications
Policies
Procedures
Work Instructions
Guidelines
Forms
Websites
Training Materials
Key Terms Term Definition
55.
RTUNITIES
ortant?
d to this Process
Req to Pay Reference Guide
Approvers URL Audit Results Changes needed
Definition
56.
y=f(x1..x2..x3…)
What knobs when turned will improve y CTQ Flowdown -variance allocation
Capability Flowup
Transfer functions - predict the Y and X influence
y= y=
x=
x knobs (inputs) x knobs
what drives the Y? what drives the Y?
y= y=
x=
x knobs x knobs
what drives the Y? what drives the Y?
57.
nd X influence
x=
what drives the Y?
x=
what drives the Y?
58.
Average high - Average low = effect
Plot Y data
Capability analysis
IMR chart
one way anova
59.
revised 4/8
What can we meas
Represen
MiniTab
MEASUREMENT WO
6 Sigma Terms
Search Minitab Website X Factors
Quantitative Verifiable Predictive Process
Predictive Input Indicators Measurement Indicators
Scrap
Rework
Cycle Time
Random Selection will eliminate bias
Surveys are often biased, must sample the Non-Resp
60.
Is population finite or infinate?
Risks in Decisions Defendant's True Status
Inocent
Not Guilty Correct Decision
Guilty Alph Error
Patientt's True Status
Inocent
Healthy Correct Decision
Ill Alph Error
Test Statistics Drive the P Value
Observed value of Population Parameter
Subtract the standard value
Divided by measure of dispersion
Assuming that Null Hypothesis is true
Risk and Circumstance always go in the oposite direction
construct a reference distribution for the test statistic
how reliable is the sample in representing the populat
61.
Determine what to MEASURE
How are we doing?
What can we measure that shows our current status?
Representative Samples are a must
Top Level quot;Yquot; is observable
at the Business Unit level
and often verified with the Strong
MEASUREMENT WORKSHEET End Customer Relationship
Y Factors
Output Performance
Quantiative Verifiable Indicators Quantitative Verifiable
Measurement CTQ's CTP's Measurement Priority
ten biased, must sample the Non-Responders
62.
nite or infinate?
Defendant's True Status
Guilty
Beta Error
Correct Decsion 1 compared to a sample
1 compared to 1
x Multiple
Patientt's True Status
Guilty
Beta Error
Correct Decsion
Don’t have to worry about normality
Drive the P Value
e of Population Parameter
andard value
sure of dispersion expected variability in the y
Null Hypothesis is true
oposite direction
rence distribution for the test statistic
the sample in representing the population?
63.
Medium Week No
Relationship Relationship Relationship
Sampling Plan
function of the risk of being
wrong; variability of the
population; difference to be
Factor Factor Factor detected
64.
1 compared to a sample 1 sample 1
1 compared to 1 F test
Bartlett or Lavine
Don’t have to worry about normality if you are working with averages - Central Limits Theorum
65.
Determine what to MEASURE
How are we doing?
revised 4/8
What can we measure that shows our current status?
Search Minitab Website From SIPOC Gather Input and Output Indicators
X FACTORS Y FACTOR
Input Indicators Process Indicators Steps Output Indicators
What measures evaluate the degree to What measures evaluate dimen
which the inputs to a process provided by What are the steps and the output - may focus on the
suppliers are consistent with what the What measure evaluates the activities used to convert performance of the business as
process needs to effectively and efficiently effectiveness, efficiency and quality of the the inputs into customer that associated with the deliver
convert into customer satisfying outputs transforation processes satisfying outputs? services and products to custom
Ex. Number of Receipts Ex. Number of defective Receipts Ex. Backup Receipt Ex. Payment issues
66.
ent status?
MiniTab
Y FACTOR Most
Output Indicators important
What measures evaluate dimensions of
the output - may focus on the
performance of the business as well as
that associated with the delivery of the
services and products to customers
Ex. Payment issues
67.
Determine what to MEASUR
How are we doing?
revised 4/8
Data Collection
Search Minitab Website
What can we measure that shows our curre
Performance Data Source that Strongly affects
Measurement Operational Definition Output Display
Describe Defect Continuous Discrete Analysis Tool
Or Metric Ordinal Nominal
How will data be used?
ID Largest Contributors
Is Data Normally Distributed?
Sigma Level and Variation
Root Cause Analysis?
Correlation Analysis?
Is Comparision of 1 sample or two or many
What is NULL Hypothesis= 0= no relationship= Start with this assumption is TRUE
Want to accept the Alternative Hypothesis
Sometimes we do not want to look for an alternative
68.
ne what to MEASURE
w are we doing?
ta Collection MiniTab
sure that shows our current status? Charts
Sample Size When will How will Other Data
Who will collect Data be Data be collected at
Cost? collected collected same time
Practical? Frequency? Random?
How will Data be Displayed?
Pareto Chart
Histogram
Control Chart
Scatter Diagram
69.
Determine wha
How are w
What can we measu
Measurement Sy
MSA help
Search Minitab Website Where are sources of Variation in
Process Procedure Transaction Source of Data Report Used
Measurement system analysis (MSA) is an experimental and mathematical method of determining
There are five parameters to investigate in an MSA: bias, linearity, stability, repeatability and repro
According to AIAG (2002), a general rule of thumb for measurement system acceptability is:
Under 10 percent error is acceptable.
70.
Determine what to MEASURE
How are we doing?
What can we measure that shows our current status?
Measurement Systems Analysis
Definitions
re sources of Variation in Measurement System?
Physical Verification Bias to Standard Repeatable? Accurate? Precise?
al method of determining how much the variation within the measurement process contributes to overall process variability.
ity, repeatability and reproducibility.
tem acceptability is:
71.
How can Variation be eliminated
overall process variability.
72.
Determine what to MEASURE
How are we doing?
revised 4/8
What can we measure that shows our current status?
Search Minitab Website
Display and Evaluate
Understanding Variation, Stability, and Capablity
Use MiniTab
Evaluate Measurement System
How much error could be in the tool being used to gather da
73.
ASURE
g?
ur current status? MiniTab
and Capablity
Normality Test
Look at Pvalue if lower than .05 Null must go
Compare line with dots - same shows Normal
If not normal use Box-Cox
Control Chart
IM chart with reference data
Gage R&R
Nested if did not use the same thing to test
R&R by Operator - Precision
Total Gage R&R must be less than 10 - 30%
Capability
Cpk potential capability
g used to gather data?
74.
Determine what to MEASURE
How are we doing?
revised 4/14
What can we measure that shows our current status?
Determine Process Performance
What is the Cost of Poor Quality
COPQ help
AS-IS Process
Items from A
Item Item
Prevention MTD YTD MTD
Quality Management
Preventative Maintenance
Factory Training
Supply Chain Process Improvement
Appraisal
Inspection
Material Inspection
Engineering Maintenance
Internal Failure
Scrap
Excess Inventory
Obsolete Inventory
Rework
External Failure
Warranty Costs
Call Centers
Totals 0 0 0
TO-BE Process
Items from Cause
Item Item
Prevention MTD YTD MTD
Quality Management
Preventative Maintenance
Factory Training
Supply Chain Process Improvement
76.
t to MEASURE
e doing?
hows our current status?
ss Performance Help
of Poor Quality Characterize Costs in AS-IS Process
Estimate costs fitting the 4 Categories
In TO-BE Process Try to eliminate costs of Appraisal, Internal Failure and External Failure.
Items from AS-IS Process
Item Item Item Item
YTD MTD YTD MTD YTD MTD YTD
COPQ – Suppliers
Cost of Poor Quality from Supplier
Suppliers can generally affect our c
a) Producing defective material.
b) Damaging material during delive
Our COPQ will generally cover the
1) Cost of labor to fix the problem.
2) Cost of extra material used.
3) Cost of extra utilities .
4) Cost of lost opportunity
a) Loss of sales/revenue (profit ma
b) Potential loss of market share
c) Lower service level to customers
0 0 0 0 0 0 0
Items from Cause and Effect Diagram
Item Item Item Item
YTD MTD YTD MTD YTD MTD YTD
78.
rnal Failure and External Failure.
COPQ – Suppliers
Cost of Poor Quality from Suppliers
Suppliers can generally affect our cost due to:
a) Producing defective material.
b) Damaging material during delivery.
Our COPQ will generally cover the followings:
1) Cost of labor to fix the problem.
2) Cost of extra material used.
3) Cost of extra utilities .
4) Cost of lost opportunity
a) Loss of sales/revenue (profit margin)
b) Potential loss of market share
c) Lower service level to customers/consumers
79.
ANALYZE THE OPPORTUNITY
What is Wrong?
revised 4/8
IDENTIFY ROOT CAUSE
Help
PROBLEM STATEMENT
What is the REAL Problem that needs to be solved?
What is the root cause that is resulting in the effect?
5 WHY'S
Ask WHY 5 TIMES to arrive at root cause
Problem:
Why?
Why?
Why?
Why?
Why?
REAL PROBLEM:
80.
PPORTUNITY
rong?
OT CAUSE
SOURCES OF VARIATION OR DEFECTS:
INPUT DEFECTS:
what inputs were defective?
t cause PROCESS DEFECTS:
look at fdpm and find process defects
DOCUMENTATION/MATERIALS
what documents need to be changed
DMADV
Identify, prioritize, and quantify impact of key factors affecting
product process performance
Derive transfer functions which capture key relationships
Select high level desing alternatives
paradigm busting most often occurs here
Track CTQ Flow Down, assess attainment of goals
document assumptions
Revise business case if necessary
KISS or KILL
81.
ANALYZE THE OPPORTUNITY
What is Wrong?
Transfer Functions - Finding
Relationships in Data
Transfer Function = Regression Model
Y= the function (x1,x2,x3…) indicator functions 0= no affect; n for affect
Pancakes
Taste =
Assembly - bottlenecks make this difficult
Productivity = F(Layout, WIP, capacities, changeover times
Use Process simulation instead
Relationship based on scientific principles
physics
etchrate
thermodynamics
Empirical data
ANOVA, Multiple Regressions
Designed experimentation or existing data
Financial relationships
Income COGS
Direct Costs
Indirect Costs
Business Models
Simulation as transfer functions
Simulation software
Physical simulations (experiment)
What if some of the Xs are things outside of my control
Measure
Interaction between these and other variables
Calculate the if the affect on the Big Y
82.
Transfer function
Fitted Line Plot Is there a relationship between X and Y?
acities, changeover times)
tion or existing data
Red Band is 95% confidence of existing residuals
Blue Band is 95% predictive of future residuals
Process Model
Alph Risk reject the null lhypothesis when it is true
Beta Risk accept the null hypothesis when it is false
83.
ANALYZE THE OPPORTUNITY
What is Wrong?
IDENTIFY ROOT CAUSE
How does the variablity in X drive the Y
Hierarchical Transfer Fuctions and how Variability in the X's
At Risk Program is being looked at by one team
Quantifying how capability of Y drives limits on X's
Monte Carlo Simutation
Low cost test
Insurance policy for system performance
Random number generation
Transfer Functions use Random numbers as Inputs
Select Input Values Transfer Function Calculated Output values
Research time + Review Time = Total Cycle Time
Indicator variable split worksheet
85.
ANALYZE THE OPPORTUNITY
What is Wrong?
revised 4/8
FISHBONE
86.
Help
PROCESS TITLE
The 4 M's
Methods, Machines, Materials, Manpower
The 4 P’s
Place, Procedure, People, Policies
The 4 S’s
Surroundings, Suppliers, Systems, Skills
88.
ANALYZE THE OPPORTUNITY
What is Wrong?
revised 4/8
Pareto
Stratification
USE MINITAB
Data
16
14
12
10
Data
8
6
4
2
0
Column E Column F Column G Column H Column I
Data 15 10 9 5 3
90.
ANALYZ
W
revised 4/8 Process steps
1 List process step and input
Process/Product 2 List potential failure modes Failure
3 List potential effects
4 Assign severity rating
5 List potential causes
6 Assign occurrence Rating
7 List current controls
8 Assign detection rating
9 Calculate Risk Priority number 1-10
10 Use RPNs to help decide on high priority failure modes
11 Plan to reduce or eliminate the risk associated with high priority failure modes
12 Re-computing RPN to reflect impact of action taken on failure mode
Process
Potential
Item Potential Potential Cause(s)
Process Process Failure Effects of of the Current Risk
Steps inputs Mode the Failure Severity Failure Occurance Controls Detection Priority
Failure Modes & Effects Analysis
91.
Process/Product:
FMEA Team:
Black Belt:
Process
Item Process Steps
Potential Effects of Failure
Occurrence
inputs
Severity
Potential Failure Mode
Potential Cause(s) of Failure
Current Controls
Text Text Text Number Text Number Text
5 Correct 7 Stock checked twice
Stock Stock in Unable to locate
location is a year
inventory wrong stock
full
location
Damage Insufficient
d product
92.
ANALYZE THE OPPORTUNITY
What is Wrong?
Help FMEA Meeting schedule
Failure Mode Effects Analysis 1 Review/create process flow diagr
RISK ANALYSIS 2 Review information/begin FMEA
3 - 4 Finish FMEA/assign actions
ailure modes
How can the process go wrong?
Actions Results
Target
Completion
Recommended Action Responsibility Date Action Taken Severity Occurance
93.
10
Detection
Number
Risk Priority Number
Calculation
0
0
0
0
350
Recommended Action
Text
Actions
Responsibility and Target Completion Date
Text
Page:
(Revised)
FMEA Date: (original)
94.
Review/create process flow diagram and review FMEA procedures
Review information/begin FMEA
Finish FMEA/assign actions
s
Risk
Detection Priority
95.
Responsibility and Target Completion Date
Text
Action Taken
Text
Severity
Number
of
Results
Occurrence
Number
Detection
n
Risk Priority Number
Number Calculatio
0
0
0
0
0
96.
ANALYZE THE OPPORTUNITY
What is Wrong?
revised 4/8
Sources of Variation
Show Correlation between Defect and
Problem
When preparing to numerically analyze sources of variation in Minitab, ALWAYS do a tree diagram and multivari chart
to assess the data
Process Variation
Source 1 Where in the process?
Measurement Variation
Source 2 Where in the Measurement Tool?
Total Variation
σ 2
Process
Variation
=
σ2
Factor is an input to your process Time
Crossed or Nested
Highest level of sampling T1 T2
Crossed are controlable factors
Temporature, flow rate
Sub level of sampling P1,P2,P3 P4,P5,P6
Nested factors are parts, locations
98.
SOV
Behavior of the process is observed in production
mode.
Data is collected of a long enough period of time to
capture a high percentage of the historical
variation
Graphs are used to illustrate and identify major
causes of total variation
multivari chart Statistical methods are used
Minitab can be used to model data between
Multiple X factors and Y with General Linear Model
MultiVari Study- graphical tool to assess
factors
Construct Tree Diagram
Box Plot
Understand the data:
Nested - Random
Crossed - Fixed
ANOVA
+ Measurement
system
variation
σ2
T3
P7,P8,P9
100.
ANALYZE THE OPPORTUNITY
What is Wrong?
Analysis of Variance
Use Averages Analysis of Variation
One Way ANOVA - one factor
Multi Way ANOVA -
Two way ANOVA - two fixed variables MiniTab
Analysis of Variance Table
Degress of Freedom
Sum of the Squares
Mean of Sigma
P-Value
101.
ANOVA
Is Sample size, sample rate and sample structure good?
Tree Diagram
Input a MODEL into MiniTab
Model = representation of a relationship of items
MiniTab
Response= factor1+factor2+factor3
Response= Mean+factor1+factor2+factor3
Expect factors to be 0 variation
Balanced ANOVA
Nested ANOVA
General Linear ANOVA (mix fixed and random)
Measurement System
%R to R, % P to T
Excellent if MSA is 10%
Random effect puts a variance on the X - tells you how big the proble is
Fixed descrete effects estimates the differences or contrast between the categories
Fixed continuous effects give you an equation describing the effect
If you treat a variable (X) as random, estimating the effect on Y
If you treat a variable (X) as continuous, you will attach an equation on it
mean square error is the estimate of the unexplained residual variability
contrasts are generally measured from the mean
102.
ANALYZE THE OPPORTUNITY
What is Wrong?
Regression - Curve Fitting
Find what is important
by estimating the relationship of X and Y
Matrix Plot - identifies where there is no data in your test - wrong decisions may be made introducing bias
103.
Fit is the relationship between X and Y
Minimize the Residual Variance
Sigma is the estimate of residual values
Red and Blue lines are bands estimated events
be made introducing bias
Appliying regression techniques to passive data extablish Correlation not Causation
Use simple linear regression when you only have one continuous Y against one continuous X
Graphical tool for evaluationg correlation is a scatter plot or a matrix plot
One of the dangers in using Multiple Regression Analysis is confounded data
To compare coefficients in the model, code the data on a -1 and +1 then all are on an equal scaled and can be comp
On a fitted line plot a prediction interval tells us the interval around the fitted line where the next point will occur
104.
scaled and can be compared
ext point will occur
105.
ANALYZE THE OPPORTUNITY
What is Wrong?
Residuals - What is Left Over
Residual = Observed Valu - Predicted Value
Assumptions Tested
Residuals Equal Independe
Plot Variance Normality nce Random Lack of Fit
Vs Run Order
Vs. X
Vs. Predicted Y
Normal Prob.
OK not enough data
straight line normal distribution
OFAT= one factor at a time
106.
you can see a lot by looking yogi
Collect Data from the Process Passively
Confounded Data - Lack of interdependance of data
Are we seeing a variable change due to another variable affecting it
Care should be taken to draw conclusions - ie. Infomercial claims
Outliers
Find FIT and Residuals
time series Assumptions: residuals are independent look for trends
nomality plot residuals are normally distriubed
control chart From a stable population
Equal population variance
Is there some systematic reaction going on?
Are there Outliers - can seriously affect the results
R sq is the amount of variation that is explained
R sq over 100% indicates that data is not dependent
Slop is the residual value on the Regression Plot
for every unit of response, slop up or down value of unit
Control Chart slop = 13.1387
In Control
Prediction (Fit) against Residuals
scatter
107.
ANALYZE THE OPPORTUNITY
What is Wrong?
Regression - Curve Fitting
Find what is important
by estimating the relationship of X and Y
How can we evaluate a response due to multiple factors
Have I caught the important causes?
108.
R sqd adjusted begins to be significant
want R sq and R sq adjusted to close to one another
Slopes are in different units
No simple graph in 2 dimensions
Correlation between X factors can dramactically affect results
Graph
Time Series on
Matrix Plot
Dotplot
Correlation coefficient
measure of the strenghth of correlation
P value shows if hypothese is good
SE Standard Error Coefficient
Remaining error variation
T = Signal to Noise ratio
Model Lack of Fit
Approximation
Replication error - If you run the same test several times, you wont get the same measurement error
Input error
error of observation
Bucketize Error
Tot Error=Model error, lack of fit error (missing variations), observation error, Measurement (X's) error (p v
Care most about: Lack of Fit error (all the right X's with measurements)
Measurement error depends on how big it is
Observation error is important to know how much is affecting LOF
110.
ANALYZE THE OPPORTUNITY
What is Wrong?
Theory of Solving Problems Inventively
Classify solutions that come out of invention
Most problems have been solved in other fields
State problem as contradiction
There are 40 Inovative Principles which can be viewed here
Segmentation
Divide an object or system into independent parts
Make an object easy to disassemble
Separation
Separate the only necessary part (or property) or an interfering part of property from an object or system
Symmetry Change
The Collaborative Innovation (CI) Process
Additional Resources:
Inventive Problem Solving (IPS) -- A powerful method for eliminating technological roadblocks related to the developm
Anticipatory Failure Determination (AFD) -- an efficient and effective method for analyzing, predicting and eliminating
Directed Evolution (DE) -- A method for developing a comprehensive set of scenarios describing future generations o
111.
http://www.ideationtriz.com/
http://inp.nsk.su/~dolgash/triz
http://ideaconnections.com
http://triz-journal.com
I-TRIZ began evolving in the mid-1980s
when TRIZ, in its classical form, ceased
development. The research and
development of I-TRIZ has continued
unabated since its inception. As a result, I-
TRIZ not only contains enhanced versions of
classical TRIZ tools, but includes an
expanded knowledge base, new tools for
applying this knowledge and for analyzing
problems more effectively, and the following
comprehensive applications:
rom an object or system
ks related to the development and use of products and processes. Applicable to all engineering disciplines, IPS can be used to solve problem
redicting and eliminating failures in systems, products, and processes.
ibing future generations of a system. DE is based on an extensive set of patterns that reveal the evolutionary tendencies of technological sys
112.
IMPROVE PERFORMANCE
What needs to be done?
revised 4/7
GENERATE SOLUTIONS
PROBLEM STATEMENT:
USE BRAINSTORM TOOLS TO IDENTIFY POSSIBLE SOLUTIONS
FROM ROOT CAUSE ANALYSIS
FROM PARETO CHARTS
FROM FMEA
FROM 2B PROCESS FLOW
FROM AFFINITY DIAGRAM
FROM 6 HATS
FROM SOLUTION MAP
113.
LE SOLUTIONS
Then
Remove showstoppers
Remove items that do not fit the
organization
Narrow list to best 4 - 7 using
CDAM: combine, delete, add or
modify
Solution Matrix based on Sigma
impact, Timeing, Cost and
Benefits
Weight ideas by performance
factor
Prioritize Solutions based on data
114.
IMPROVE PERFORMANCE
What needs to be done?
revised 4/8
AFFINITY DIAGRAM
PROBLEM STATEMENT:
GROUP LIKE (AFFINITY) SOLUTIONS TOGETHER
BRAINSTORM SOLUTIONS AFFINITY SETS
115.
HELP
1. Rapidly group ideas that seem to belong
together.
2. It isn't important to define why they belong
together.
3. Clarify any ideas in question.
IONS TOGETHER 4. Copy an idea into in more than one affinity
set if appropriate.
5. Look for small sets. Should they belong in
a larger group?
6. Do large sets need to be broken down
more precisely? the ideas have been sorted,
7. When most of
you can start to enter titles for each affinity
set
116.
IMPROVE PERFORMANCE
What needs to be done?
revised 4/8
revised uploaded
Nominal Group
Technique
List Team Members
List Causes
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
117.
To find the priority of a list of causes according to the team:
1 List Team and Causes
2 Count the number of Causes (n)
3 Team Members individually assign a value to each cause.
The highest priority should be noted as n.
The least important cause should be noted as 1
4 Each team member continues to choose the next most important and the next least important until all cau
5 Record each members scoring
3 Total all scores for each cause.
4 Sort total scores decending
5 Pareto the results.
Priority
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
118.
ast important until all causes are assigned a value.
119.
IMPROVE PERFORMANCE
What needs to be done?
revised 4/8
Solution Map
CORE PREMISE:
120.
Start with core premise (goal you
want to achieve), root cause, or
problem statement. Brainstorm
related clusters of potential
solutions in the boxes.
Brainstorm solutions from around
the core premise
When one person offers and idea,
have other team mebers try to
expand it or turn it into another
idea.
121.
revised 4/8
IMPROVE
PERFORMANCE
What needs to be done?
FORCE FIELD ANALYSIS
SOLUTION:
DRIVING FORCES RESTRAINING FORCES
122.
IMPROVE PERFORMANCE
What needs to be done?
revised 4/8
Functional Deployment Process Map
TO - BE Process Flow
Process Steps
Department
Sub-Process
Start boundary
123.
DMADDD
Design - If this is a new
ss Map
process the 2B will be
the new Design which
will be Verified by the
Pilot
Qualitative Review (Value Added Analysis)
Would anyone pay for this?
Low Hanging
Customer? Operations? Other fruit
124.
IMPROVE PERFORMANCE
What needs to be done?
revised 4/8
Evaluate your new Process
Process Steps
Department
What is
Sub-Process
What is the Cost
Fits Impact on the Time Benefit Other
Show Stopper? Organization? Sigma? Impact? Impact? Impacts?
Start boundary
125.
cess
Can this
step be
Combined Don't need Need to This Step
with this step - Add this has been
another? Delete Step Modified Qualitative Review (Value Added Analysis)
Would anyone pay for this?
Low
Hanging
Customer? Operations? Other fruit
126.
revised 4/8
IMPROVE PERFORMANCE
What needs to be done?
PILOT THE PROJECT
Implementation checklist
Pilot Team Detailed Implementation
Training Plan Communication Plan Event
Select Small Group
Design
DOE
RSM Validated/Refined Model
Identified sigma x's
Variance Allocation Optimized design parameters
Reduced variation
sensitivity Analysis
Monte Carlo Simulation
Product
Requirements
Design
Features
1
How well are
Customer wants
Expectations satisfied?
Product
Requirements 3
Measurements
Measurements
128.
Take part of a process (where you have the
RMANCE least confidence, and pilot or simulate the
e done? process
Controls Measures Verify Results
Verify
Capability Flow-up
Set Xs according to design parameters
Flow-up to CCRs thruough transfer function
Determine whether Ys hit targets
Mfg
Processes
Process
Operation
Design
features 3
Mfg
Processes 4
130.
IMPROVE PERFORMANCE
What needs to be done?
revised 4/8
Verify Results
End Product:
Customer:
Process Tested:
Desired Results:
Process Steps tested:
131.
ERFORMANCE
s to be done?
Pilot Observations:
GAP Analysis/Root
Cause:
Follow-up Actions:
Questions:
132.
Control Performance
revised 4/8 How do we guarantee performance?
Implementation Plan
Potential Problems (RISK) of Recommended Internal
implementing Solution (FMEA) Controls quot;Hardwired Controlsquot;
What Data is to be
Process Control System collected Who will collect this Data
Documented Procedures Documented Guidelines
Training Plan Changed Changed
Method of
Communication Plan Leadership support Communication
Business Case from
Budget and Benefits Case quot;Definequot; Solution Benefits
Detailed Implementation Event Objectives Milestones
133.
trol Performance
e guarantee performance?
Implementation Plan
Check Lists
How will this Data be When will it be What is the Out of
Shown Regularly Reviewed? Control Plan?
Documented
Documented Forms Training Materials
Changed Changed Audience
When? Who is responsible
Benefits to Other Non-Financial
Cost to Implement Customers Benefits
Roles and Dates/Time/Room/M
Respnsibilities Resource Planning aterials
134.
Control Performance
revised 4/8 How do we guarantee performance?
Documentation Related to this New
Process
Review Documentation and
Change to reflect New Process
Doc. Number Title Creation date Last Revised
Related Specifications
Policies
Procedures
Work Instructions
Forms
Websites
Training Materials
Key Terms Term Definition
135.
rmance
performance?
ted to this New
s
Approvers URL Audit Results Changes needed
Definition
136.
Control Performance
revised 4/14 How do we guarantee performance?
Poka-Yoke Mistake Proofing
Example: You leave your Gas Cap with your
keys on top of your hood to avoid forgetting
to put it back on.
Review 2Be Process. Where
can possible Errors be made
Where does Error Occur that if undectected, will cause a defect that your custom
that will cause a Defect?
What can be done to order the process so that the Error is Self Evident before it
What can be done to quot;hardcodequot; the Process so that the Error will not be passe
What can be done using a Checklist to identify the Error before it is pass on?
What Does Digitization Add to Six Sigma ?….
E-Training - Ability to Train & Test Thousands of people in Mont
E-Processes – Digitized Improvement Actions Stay in Control
E-Tools – e-Surveys, Simulators, and Modeling Tools Improve
phases of Six Sigma Projects
E-Tracking - Ability to Monitor All Vital Xs and the improvement
E-Visibility - Ability to Monitor All Business Ys and The Sectors
the Results (good and bad)
137.
DMADDD
rformance
Digitize the Process
ntee performance?
by using Hard
Coding to take the
cost out of the
process by
automating certain
tasks
ur Gas Cap with your
od to avoid forgetting
cause a defect that your customer will notice.
e Error is Self Evident before it is passed on?
that the Error will not be passed on?
e Error before it is pass on?
gma ?….
ousands of people in Months not Years
nt Actions Stay in Control
nd Modeling Tools Improve the Analyze and Improve
al Xs and the improvement projects attached to them
siness Ys and The Sectors / Functions Driving
138.
Control Performance
How do we guarantee performance?
Control Tollgate Review Questions
Now is a good time to reflect on your experiences during the Control phase. Here are some questions to consider.
Methodology
1. Describe the implementation plan. How will the plan be monitored to ensure its success? Who is accountable?
2. What are the potential problems with the plan? What are the contingency plans?
3. What controls are in place to ensure that the problem does not reoccur?
4. How has the scorecard been integrated into the management review process?
5. Who is the process owner? How will the responsibility for continued review be transferred from the improvement team to the
process owner? How frequent are the reviews?
6. What is being measured? What evidence do you have that would indicate the process is in control? How well and consisten
the process performing? Is a response plan in place for when the process experiences out-of-control occurrences?
7. How has the process been standardized? How have you documented the process changes?
8. What is the new process sigma?
Building Organizational Support
9. How has the training plan been revised from the Improve phase? How has training been conducted to ensure understanding
the process changes? How effective was this training? What continuing issues does your team need to address in the area of
training?
10. What is the communication plan for implementation? How will your team use communication to manage this change, minim
resistance, and mobilize stakeholders?
11. Based on your implementation and communication with key stakeholders, what are the barriers to successful change? How
you plan to address them? How are your actions to overcome barriers reflected in your implementation plan?
139.
Control Performance
revised 4/8 How do we guarantee performance?
Process Control System
List Output Indicators associated with What UPSTREAM events best predict this What Check will be put in place to i
CCR's Output before it is passed on to the Custom
140.
k will be put in place to identify this Error Contingency Plan or
passed on to the Customer? OCAP
141.
Control Performance
revised 4/8 How do we guarantee performance?
IMPLEMENT Statistical Process Control to
Monitor the Process
Design a Detection
System
How will you know if your new process has gone out of control?
List Key Measures from VOC, Calculate Out of Control Points
SIPOC Data Operational Definition (MiniTab)
142.
Design for Manufacturability (DFM)
Design for Service (DFS)
Design for Assembly (DFA)
Verify Functionality
What must be done when New
Process is Out of Control
OCAP
143.
Control Performance
revised 4/14 How do we guarantee performance?
IMPLEMENT Statistical Process Control to
Monitor the Process
List Lessons Learned using the DMAIC What Other Similar Processes Could Also
Process Be Improved
Pilot
Site Level
World Wide
Level
144.
rformance DMADDD
ntee performance? Draw Down - Find
where this projects
results can be
al Process Control to directly applied to
e Process other processes
rocesses Could Also Collaboration with other Process Owners
with Similar Processes
145.
What needs to be d
Experiment = Understand the nature of a system by manipulating inputs
Screening
What are the variables or factors that affect the response and their interaction?
Separate the vital few from the trivial many
Optimization
Optimize the performance of some process or product
Comparison
Compare two or more things: vendors, processes, tools, designs etc
Robust design
Is the performance or product or process insensitive to the environment in which they are used?
System improves as the factors get more fundamental - Vendor - On time delivery
PURCHASE ORDER PROCES
SUPPLIER INPUT
(Requisitioner, Customer,
Planner, Commodity Team)
146.
Materials or Information
consumed or transformed by
the Process to produce output
Master Data
Vendor, Info Record, Source List, QPL
Requisition/Demand
(Schedule Sharing)
Review & Approval
147.
Design
How do we guarantee performance?
What needs to be done?
Design of Experiments
ating inputs
nse and their interaction?
ls, designs etc
o the environment in which they are used?
On time delivery
PURCHASE ORDER PROCESS
What factors are uncontrolled?
Categoriacal
CONTROLS
Rules, Regulations, Standards,
Polices, Procedures, Work
Instructions, Goals, Metrics, &
People (Process Owner)
QPL/QSL: Vendor Master;
Material Master;
SOP 8-4; SIC 3.2
314W checklist GVM Profile
↓
PROCUREMENT PROCESS OUTPUT TO CUSTOMER
(Receiving, AP,
Requisitioner or Planner
148.
Work done (by the Buyer) to the and the Supplier)
Inputs to Transform Inputs
into Output (PO)
Information or Product created by
the Process
Create and Transmit PO from Good PO that can be performed
Inputs. to by the Supplier, received, and
paid.
MECHANISM
People, Behaviors, Equipment, Tools, Software
that perform the Process
SAP
Buyer
Commodity Team
WWCM
PO Type
TIGERS
What Factors are
Uncontrollable?
Continuous
149.
Design
How do we guarantee performance?
What needs to be done?
ANOVA = one factor at a time
Understand how the data is structured
Draw Tree diagram Cycle Time = Y
Day DOW
Location No. of Employee
Lead Rep nexted (different people)
Loan/Lease Amount
Application used
Approval Time
Wait for Info
OBSR_NO. Cycle TimeDAY Lead Rep Location No. Empl DOW Application UsedLoan/Lease
model = Discrete X's
General Linear Model
overview how to examples data see also
Stat > ANOVA > General Linear Model
Use General Linear Model (GLM) to perform univariate analysis of variance with balanced and unbalanced design
Calculations are done using a regression approach. A quot;full rankquot; design matrix is formed from the factors and cova
Factors may be crossed or nested, fixed or random. Covariates may be crossed with each other or with factors, or
Dialog box items
Responses: Select the column(s) containing the response variable(s).
Model: Specify the terms to be included in the model. See Specifying a Model for more information.
Random factors: Specify any columns containing random factors. Do not include model terms that involve other fa
<Covariates>
<Options>
<Comparisons>
151.
Analysis of Variance Table
Degress of Freedom
Sum of the Squares Total Variation = sum of(y-averagey)squared
Mean of Sigma
P-Value
residual error = not explained by the model - unexplained variation
R squared value = explained variation divided by the total variation times 100
Covarient is continuous data
Use Discrete data for interation plot
Factor Type Levels Values
DAY fixed 20 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
20
Location fixed 3 New York Paris Sydney
Analysis of Variance for Cycle Ti, using Adjusted SS for Tests
Source DF Seq SS Adj SS Adj MS F P
DAY 19 5333.6 966.8 50.9 6.24 0.000
Wait for Info Location 2 23916.6 23916.6 11958.3 1466.91 0.000
Wait - Discrete Error 1998 16287.8 16287.8 8.2
Total 2019 45538.1
Approved/Dis
Amount Approval Time for Info
Wait Wait - Discrete Time Discrete
Cycle
d and unbalanced designs, analysis of covariance, and regression, for each response variable.
from the factors and covariates and each response variable is regressed on the columns of the design matrix.
h other or with factors, or nested within factors. You can analyze up to 50 response variables with up to 31 factors and 50 covariates at one t
terms that involve other factors.
153.
9 STEP ANALYSIS PROCESS - FACTORIAL DESIGNS
STEP MINITAB COMMANDS
1 Graph…Dotplot
view data
Graph…Plot
Graph…Matrix Plot
2 and 3 Stat…DOE…Factorial…Analyze
create/fit model
4 Stat…Regression…Residual Plots
evaluate residuals
5 Calculator - create natural log or square root
consider transform
6 go back to steps 2/3
take out terms
7 Stat…DOE…Factorial…Analyze
store equation
8 Stat…DOE…Factorial…Factorial Plots
optimize
Stat…DOE…Factorial…Contour Plots
Stat…DOE…Factorial…Response Optimizer
9 Stat…Regression…Regression
prediction interval
154.
ORIAL DESIGNS
OPTIONS COMMENTS
Y
symbol - group - CenterPt Y versus Run Order
connect - graph
Y versus all X factors
Terms - 2nd order, deselect center points in model
Graphs - Normal, Pareto, Residuals
Storage - Fits, Residuals store in order to plot residuals under
Stat/Regr/Residual Plots
Fits versus Residuals else use automated plots
only if needed-residual problems
Storage - coefficients
Main Effects - Setup
Interaction - Setup
Contour Plot - Setup
Setup - choose optimization criteria (I.e Max)
Options-input optimum conditions and check enter optimum conditions in same
prediction and confidence limits order as predictors box; make
sure there is a column of data
for each effect in final model.
155.
9 STEP ANALYSIS PROCESS - RSM DESIGNS
STEP MINITAB COMMANDS
1 Graph…Dotplot
view data
Graph…Plot
Graph…Matrix Plot
2 and 3 Stat…DOE…Response Surface…Analyze
create/fit model
4 Stat…Regression…Residual Plots
evaluate residuals
5 Calculator - create natural log or square root
consider transform
6 go back to steps 2/3
take out terms
7 Stat…DOE…Response Surface…Analyze
store equation
8 Stat…DOE…Response Surface…Contour Plots
optimize
Stat…DOE…Response Surface…Response Optimizer
9 Stat…Regression…Regression
prediction interval
156.
DESIGNS
OPTIONS COMMENTS
Y
symbol - group - CenterPt Y versus Run Order
connect - graph
Y versus all X factors
Select Coded Units
Graphs - appropriate residual plots
Storage - Fits, Residuals store in order to plot residuals under
Stat/Regr/Residual Plots
Fits versus Residuals else use automated plots
only if needed-residual problems
Storage - coefficients
Contour Plot - Setup choose uncoded; make sure data
file is in uncoded units to match
Setup - choose optimization criteria (I.e Max)
Options-input optimum conditions and check enter optimum conditions in same
prediction and confidence limits order as predictors box; make
sure there is a column of data
for each effect in final model.
157.
Design
How do we guarantee performance?
Design of Experiments
A Crossed Effect Method
Use data found in Analyze Phase to develop a theory to test
Building a statistical model between experimental responses Y and factor inputs X
Experimental Sequence Two Factor Two Level M
Fraction Factorial ( chosen X factors) B lo
Subset of the Factors identify leading indicators B hi
Screening Experiments 5-7 X's To calculate the Main E
State the problem and objectives of experiment
Planning - Design the experiment and plan for data collection
Write Operational Definitions
Operating Range
Experimental Range as a subset of Operating Range (nominal value +-
Select a Nominal value of the factors
Fixed Effect Model -
Conduct the experiment
Analyze the results - continuous - Miltivari regression - Discrete - ANOVA
Interpret the results
View the data
Create the model
Fit the Model
Perform Residual Diagnostices
Check for possible transformations
Remove non-significant terms/refit reduced model
Choose improved model
Interpret Chosen Model
Continue Experimentation/Make confirmation runs
Full Factorial (all known X's) 2k experiment
All factors of significance used in a 2k Frational
2k = 2 times the number of factors Look for Interactions x1 and x2
Randomization to reduce bias
Run Order must be randomized
Randomize to minimize Uncontrolled Factors
Independent trials
Blocking- use with uncontrolled variation
Limit the nuisance (noise) factor
Block what you can, and randomize what you cannot block
Replication - repeat the experiment over again
Repetition - remeasure over again
Run all or some of the experiments twice
Ensures independent replicates
Look for variablity in the controlled factors
Center points are replicate runs where all continuous factors are at their center lev
Estimate the true experimental error
Determine if a drift or innstability occurred during the experiment
158.
Evaluation of the curvature in the response
Experimental Error- unexplained variation
pure error - where you repeat yourself in error
LOF - lack of fit - model error
Error that occurs when the estimated model does not correctly predict the respons
Error that is the noise caused by uncontrolled and unknown nuisance factors
Experimental Unit
Material to which a particular experiment run or factor-level combination is applied
Orthogonal Coding - reduce controllable input factors as -1 , 0 or +1
Remove any interactions
effect of one factor is dependent on the other
Determine Region of Optimum Path of Assent
Response Surface Methodology
Model Optimal Region
Plackett- Burman
Confirm Model Predictions
Confirmation Runs - Pilot testing
159.
center points
replicates = how many times you are running the experiment
blocks =
analysis
cube plot 3d plot of 3 variables
160.
4 Objectives for doing a DOE
Screening Don’t look at just CORRELATION, but look at CAUSATION
Optimization
Comparison
Robustness
ses Y and factor inputs X
Two Factor Two Level Matrix
A lo A hi
To calculate the Main Effect of a Factor from a DOE….average high minus the average low
Range (nominal value +- 10 to 20% of Nominal value
Two reasons for not doing an OFAT type design
cannot identify interactions
cannot define the true optimal
Uncontrolled Categorical Factors
Process inputs Process Process outputs
Uncontrollable Continuous Factors
Designs should be:
Balanced - 4 hi levels and 4 lo levels
Orthogonal - right angles to each others - allows you to independently assess the amount
of each of the X's explain
+1's and -1's reduce X's to common value
independent value of the effects
Center points can be used for curvature - curvature checks your assumption p value of curvature tells you th
2 to the 4 plus centerpoints = number of runs
Orthogonal check in Minitab
tors are at their center level midway between low and high values
ing the experiment
161.
rectly predict the response
wn nuisance factors
el combination is applied
162.
switch between Orthogonal and uncoded correlation checks for confoundness in the X's
163.
t CAUSATION
p value of curvature tells you the percentage you will be wrong
165.
confoundness in the X's all zeros indicate that there is no correlation between the X's
represents a balanced design
166.
Robust Design
resliency to internal design internal customers
variability in training,
resiliency to external factors external customers environment
usage environment factors
CTQ flowdown
transfer functions can capture the depenence of both design and environmental factors
design of experiments helps quantify relationships and derive the transfer functions
Monte Carlo helps validate results and help make choices among different alternatives
Design product to perfom well without variables that are not in our control effecting it
Design variables are under our control
Environmental variables are not under our control
How does your design perform under different conditions external and internal?
167.
Objective
Experimental Unit
Treatment
Screening which X's are important FractionalFactorial
5 - 7 X variable Plackett-Burman
Comparision which X is better Full Factorial
AB AA BA
Optimization want to get a picture of the process Response Surface (RSM)
2 - 4 X variables
Robust Design Not sensative to certain variables Response Surface (RSM)
Determine Important Variables Resolve any interactions Determine Region of Opt
Cause and Effect diagrams Full Factorials Path of Ascent
All combinations
A with three settings (levels)
B with 2 settings (levels)
Number of factoral combinations = A levels * B levels
Simplest Full Factorial is a 2 factor 2 level
Hi
2k Full Factoral Designes
k= number of factors Y=B B
2= number of levels A=X
Lo A
Look for interactions
Average HI - Average Lo is the Main Effect
Effect divided by 2 = Slope
Slope = change is y over the change in x
Do an ANOVA>>Interactions Plot
Planning Step Task
1. State the Problem and
Objective Outline the specific continuous improvement objective
Collect available background information
Summarize data applicable to this problem area
2. Design the experiment and
plan for data collection Hold a meeting of all concerned individuals
Develop a preliminary experimenatal design program
Review the design with all concerned parties
168.
3. Conduct the experiment Develop methods and acquire necessary materails and equipment
Conduct the experiment. Be sure to follow the protocol exactly
Record any unusual events.
4. Analyze the results Record results as the analysis progresses
Interpret the experimental results
Consider all observed data
Confine conclusions to strict deductions from the experimental evidence
Test any questions that are suggested by the results by independent experiments
Make conclusions regarding the technical meaing of results as well as their statistic
State results clearly in understandable terms
5. Interpret the results Describe work clearly. Provide the background, pertinence of the problems, and m
Use graphs and tables. Present the data in a usable form
Supply sufficient information to enable the reader to verify results and draw conclu
Limit conclusions to an objective summary of evidence so that the work recommen
169.
WHICH
HOW
Surface (RSM) PICTURE
Surface (RSM) SPECIAL TYPE
OF SCREENING
Determine Region of Optimum Model Optimal Region
Path of Ascent Response of Surface Methodology
Process
Hi
170.
ental evidence
ndependent experiments
lts as well as their statistical significance
e of the problems, and meaning of the results
y results and draw conclusions
that the work recommends itself for prompt considerations and decisive actions
171.
Y=Temperature
X Factors Low High User Input
Argon 30 70 45 -0.25
Oxygen 1 9 5 0
Nitrogen 200 500 350 0
Ion Rate 5 10 7.5 0
Supplier 1 2 1 -1
ReactionTime 10 20 15 0
1 Run
Equation (coded units) = 500 - 132*IonRate + 45*Supplier*Oxygen + 25*ReactionTime
Need Specs For now assume 600 to 700?
sd(noise)= Stdev=20? Verify if any factors should affect sd?
172.
1 Number:
Normal Random: 1
Temperature(Y) = 512.19
Noise: 8.77
Temperature (Exact) 500
Temperature + Noise: 508.77
25*ReactionTime
50
4
350
7.5
1
15
176.
Fractional Factorials
Determine Important Variables Resolve any interactions Determine Region of Optimum
Cause and Effect diagrams Full Factorials Path of Ascent
All combinations
Confirm Model Predictions
Confirmation Runs
Sparcity of Effects - not everything is important I=abcd
center on the top 4 factors a(i)=a(abcd
sacrafice the highest order interactions a=bcd
use the 1st 8 of the 16 1/2 fraction select all + or all - of the abcd bd=ac
177.
Region of Optimum Model Optimal Region
Response of Surface Methodology
a(i)=a(abcd
Resolution
Finger rule
Resolution III is not recommended
Resolution IV is ok..but need to watch out for confounding. If there are interactions present we will see them, but we w
Resolution V is good
178.
we will see them, but we will not be able to tell what they are without further experimentation
179.
Blocking is used to protect against known nuisance factors
Use randomization to protect agains unknown nuisance factors
Average high - Average low = effect
180.
Significant Block effect
Take blocking out when significant
181.
Capability Flowup
CTQ Flowdown CTQ Flowdown – A very
Variance Allocation
You want to do your final
Defining and Partitioning Tolerances
How much tolerance is tolerable
sigma squared of the total = sig sq'd1 + sig sq'd2…..
Sensitivity Analysis
degree of variability in the output of a transfer function (Y) which results in variability in the inputs (X's)
Input variability can cause unacceptable changes in output performance
Monte Carlo- good for Capability Flowup but not CTQ Flowdown
estimate how variability in the input variables will cause poor output performance
Simulates - but only by trial and error
Calculating Variances (estimate)
quadradic transfer function k
y
k=slope
x
sigma of y = k sigma x
y= square the variances and add them together
if sigma x's are know plug them into the calculation (capability analysis)
yield = f(time, temp)
time= 325
temp= 480
dYield
CTQ Flowdown - what is the tolarable combined variance in the x's
182.
CTQ Flowdown – A very rigorous methodology for allocating requirements and assessing capabilities of the most critical segments of a prod
You want to do your final CTQ flowdown into CTPs (critical to processes) at the beginning of the design phase. You need to know what is cri
ility in the inputs (X's)
http://www.umoncton.ca/cie/Conferences/29thconf/29thICCIE/papers/paper004.PD
183.
ritical segments of a product prior to M1.
need to know what is critical to the process before you can design your new detailed processes
CIE/papers/paper004.PDF
185.
Use Predictive Tools to Demonstrate Design Fulfillment of CCRs
Design
DOE
RSM Validated/Refined Model
Identified sigma x's
Variance Allocation Optimized design parameters
Reduced variation
Product
Requirements
Design
Features
1
Customer How well are
Expectations wants satisfied?
Product
Requirements 3
Measurements
Measurements
Capability Flow-UP
186.
nt of CCRs
Verify
Capability Flow-up
Set Xs according to design parameters
Flow-up to CCRs thruough transfer function
Determine whether Ys hit targets
Mfg Processes
Process
Operation
Design
3
features
Mfg
Processes 4
Measurements
Measurements