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Transcript of The DMAIC Lean Six Sigma Project and Team Tools Approach Measure Phase 1.
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The DMAIC Lean Six Sigma Project and Team Tools Approach
Measure Phase
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Lean Six Sigma Combo/Black Belt Training! Agenda – Measure Phase
Welcome Back, Brief Review Process Thinking, Mapping, and AnalysisMeasurement System AnalysisSigma Level, Baseline Metrics, Types of Data Capability Analysis Introduction to MinitabPareto AnalysisTheories of Xs and Cause and EffectData Collection Plan and SamplingLessons Learned / Measure Phase Conclusions Wrap-Up / Teach-Coach Practice / Quiz 2
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Measure Objectives (pg. 8-11)
• Identify the Project Y
• Define the performance standards for Y, and its baseline (current state) performance
• Clarify understanding of specification limits as well as defect and opportunity definitions
• Validate the measurement system (MSA)
• Collect the data as needed
• Characterize the data using basic tools and capability
• Begin funneling the X’s that affect the Y
• Measure…what is the current state/performance level and potential causes
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Why spend so much time in the Measure phase?
“When you can measure what you are speaking about, and express it in numbers, you know something about it; but when you cannot measure it, when you cannot express it in numbers, your knowledge is of a meager and unsatisfactory kind…” Lord Kelvin
“If you can’t measure it, you can’t manage it.”
Peter Drucker4
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Why Do We Measure?• To thoroughly understand the current state of our
process and collect reliable data on process inputs that you will use to expose the underlying causes of problems
• To know “where you are” – the extent of the problem
• To understand and quantify the critical inputs (xs) that we believe (theories) are contributing to our problem (Ys)
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Lean Six SigmaDMAIC Phase Objectives
• Define… what needs to be improved and why
• Measure…what is the current state/performance level and potential causes
• Analyze…collect data and test to determine significant contributing causes
• Improve…identify and implement improvements for the significant causes
• Control…hold the gains of the improved process and monitor
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LSS PROJECT FOCUS
Process Problems and
Symptoms Process outputs Response variable, Y
Independent variables, Xi
Process inputs The Vital Few determinants Causes Mathematical relationship
Y
X’s
Measure
Analyze
Improve
Control
Pro
cess
Ch
ara
cter
izat
ion
Pro
cess
O
ptim
iza
tion
Goal: Y = f ( x )
Define The right project(s), the right team(s)
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Measure Phase:
Process Mapping
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The Basic Philosophy of Lean Six Sigma
• All processes have variation and waste• All variation and waste has causes • Typically only a few causes are significant• To the degree that those causes can be understood
they can be controlled• Designs must be robust to the effects of the remaining
process variation• This is true for products, processes, information
transfer, transactions, everything• Uncontrolled variation and waste is the enemy
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Remember - What is Six Sigma…
•A high performance measure of excellence•A metric for quality
•A business philosophy to improve customer satisfaction•Focuses on processes and customers•Delivers results that matter for all key stakeholders
•A tool for eliminating process variation•Structured methodology to reduce defects
•Enables cultural change, it is transformational
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Why Process Thinking?
Allows criticism without blaming people
Allows shared understanding of how things work
Helps manage complexity
Provides focus within context
Helps to manage scope of project
Identification of team members
Understand inputs / outputs - leads to measurement
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High Level Process Map - SIPOC
Process Name
Supplier-Inputs-Process-Outputs-Customer ….………………………………………..…….….……………………………………………...….……………………………………………...….……………………………………………...………………………………………………………………………………………………………………………………………………………
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High Level 1 Box Examples
Outputs
Time to quoteNumber of contactsQuote accuracy
Inputs
Customer NameCustomer IDBill toShip toCredit status
Quoting Job
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High Level Process FlowINPUTS PROCESS OUTPUTS
Specialty availableChart availablePatient assessment
MD orders consult Order in chart—completeReason for consultOrder flaggedOrder placed in correct area
Legible orderComputer system working
Unit Sec enters
consult
Consult stamp on chartConsult documented in CERNER
Contact informationCall schedulesAssigned vs. Group call schedule
Unit Sec calls consult Specific MD notifiedAnswering service notifiedMD on-call notified
24 hour chart check RN reviews chart for completeness
Consult not met Failure to meet consult is noted by RN24 hr chart check signature
RN realizes need to reconsult RN informs Unit Sec to reconsult
Unit Sec attempts to reconsult
Contact informationCall schedulesAssigned vs. Group call schedule
Unit Sec/RN verifies with exchange / office
Office or exchange notifies physician
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Lean Six Sigma Project and Team Basic Tools
Process Flow Chart (pg. 33-44)
A visual display of the key steps and flow of a process, also called a process map. Usually standard symbols are used to construct process flow charts. These include boxes (or rectangles) for specific steps, diamonds for decision points, ovals for defined starting and stopping points, and arrows to indicate flow.
Processes can include providing a service, making or delivering products, information sharing, design, etc. – Should represent the current as-is state of the process!
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Process Mapping (pg. 33-44)
• A process is a sequence of steps or activities using inputs to produce an output (accomplish a given task).
• A process map is a visual tool that documents and illustrates a process.
• Several styles and varying levels of detail are used in Process Mapping. Most common and useful styles are SIPOC, Flow Diagrams, Box Step, and Value Stream Maps.
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Process Mapping• The team should start with the observed,
current, as-is process.
• Start high-level, and work to the level of detail necessary for your project (key inputs).
• As inconsistencies are discovered, the team can develop a future state or should-be process map to improve the key xs and the overall output (Y) of the process.
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Levels of Process Mapping
Level 1: Core Business Processes
Level 2: Processes
Level 3: Subprocesses
Level 4: Activities/Steps
Level 5:Task
How Low Can You Go?
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Patient Care Core Business Process
Admissions Treatment & Invervention
Discharge Billing
Medication administrationPhysical therapyDiagnostic and therapeutic imaging interventionLab testingCardiology treatment interventionPulmonary treatment interventionSurgical interventionIV therapy treatmentNutritional supportDischarge teachingPhysiological monitoringImplementation of treatmentsCommunicationPain management 19
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How Low Can Should You Go?
• Decompose the process until it becomes unnecessary to go any farther– Accountability is identified
– Responsibility falls outside the process boundaries
– Root cause becomes evident
– The time required to measure the process exceeds the time required to perform it
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Flow Diagrams - Concept(For Complete List, see: PowerPoint - Shapes - Flowchart)
Activity / Step
Decision
Flow lines
Terminal / End
Connector
Database
Document
Off-page Connector
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Process Flow - Symbols
Follow the standard symbols; don’t make up your own.
People who follow your process flow should be able to understand your work and documents.
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You poor dummy!
Don’t mess with it.
You big dummy!
Does thethingwork?
NO PROBLEM
Did youmess
with it?
Doesanyoneknow?Hide it!
Can youblame
someoneelse?
Will youget in
trouble?
Toss it!
YES
NO
YES
NO
NO
YES
NO
YES NO
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Suspected Bleeding Disorder
H & P
Screening Tests:CBCPTPTTPFAThrombin TimeOther testing as indicatedby Patient or Family Hx
PositiveScreening Test
Result?
Release or workupfor other Dx
Focused Testing(see list b)
Focused Testing(see list a)
PositiveTest
Result?
PositiveTest
Result?
Review ScreeningTest Results
yes
no
no
no
yes
DefinitiveFamily
History?
Symptomatic
Patient or FamilyHistory?
yes
no
no
END
FurtherTesting
Required?
Confirmed Dx
yes
yes
yes
no
Sample Process Map
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http://www.qualitymag.com
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Draft Problem Statement
Define Potential
Project
Identify theMetrics
Determine theOutputs (Y)
Quantify theOpportunity
CalculateBenefits
Reconsider Project
Redefine Project Scope
MeetsSix SigmaCriteria?
Two Or FewerOutputs?
Charter and Launch Project
No
YesNo
Yes
A Gap Exists
Process Flow Chart Lean Six Sigma Project Selection
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http://www.oregon.gov27
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A Flow Chart of Process Mapping
Define the Process Scope
Assemble the Team
CreateProcess
Flow Diagram
IdentifyVA/NVASteps
Find theHiddenFactory
Macro Map?
Observe and
Verify
Build a Detailed
Map
IdentifyX’s and
Y’s
Identify the
Specs.
List Process
Capability
Revise and
Update
Draft a MacroMap
No
Yes
Start
Tools: PowerPoint, Excel, Visio, Process Model
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Additional Process Mapping Techniques
• Swim lanes (pgs. 43-44)
• Value stream mapping (pgs. 45-51)
• Time Value Map (pgs. 52-53)
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Process Mapping AnalysisDetailed Analysis of Process Delays or Errors:
Identifying process delays or potential errors is an important analyze phase activity. Going into greater detail in identifying the type and source of delay or error will help to more clearly define the root cause and thereby produce a more robust solution and overall improvement.
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Process Mapping AnalysisTypes of Process Delays or Errors:
• Gaps• Redundancies• Implicit or unclear requirements• Bottlenecks• Hand-offs• Conflicting objectives• Common problem areas
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Process Mapping Analysis
Gaps
– Responsibilities for certain process steps are unclear, not understood, easy to “skip”
– Process seems “unfocused,” goes off track in delivering what the customer needs
– Excessive variation
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Process Mapping Analysis
Redundancies
– Actions or steps are duplicated
– Different groups repeat actions that are done somewhere else, and they are not aware of the repeat actions occurring
– Excessive checking (non-value adding)
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Process Mapping Analysis
Implicit or unclear requirements
– “Word of mouth” instructions, not formally documented; assumptions
– Operational definitions are not noted; different groups interpret definitions and instructions differently
– Unclear measurement system
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Process Mapping Analysis
Bottlenecks
– A “slow down” of work flow
– Multiple inputs may feed into a process step, which is then delayed
– Output of entire process may be “controlled” by the output rate of the bottleneck step(s)
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Process Mapping Analysis
Hand-offs
– Unclear if a process step has received needed inputs from an “upstream” step
– Misunderstanding of who is responsible, or who has done what
– Communication problems
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Process Mapping Analysis
Conflicting objectives
– Unclear alignment from one group to another working in the same process
– Direction from leadership and metrics
– Communication problems
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Process Mapping Analysis
Common problem areas
– Overall weaknesses seen throughout a process, common failure modes
– Repeated steps or checks in a variety of places throughout the process flow
– Communication problems
– The “Hidden Factory”
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The Hidden Factory
All of the work that is performed that is above and beyond what is required to deliver good products and services to the customer; work that is not necessarily tracked (cost, productivity, etc.).
Work-arounds or “built-in” Rework
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Process Mapping, Measurement and Analysis
Study your key processes and note any of the aforementioned potential process delays or errors directly on your process map. Go to the source to verify with data. Many key xs are identified through careful and deliberate process measurement and analysis.
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Start
Change in patient’s physical status
Ongoing assessment and monitoring of patients vital
signs and status
Appropriate care delivered
Did werecognizechange?
Continueddeterioration
Did weact
quickly?
Was theaction
appropriate?
Patientmedicalrecord Cerner data
Best possibleoutcome
Potentially badclinical outcome
Can Cernerflag critical
VS changes?
Frequencyof VS
checks?
Nursingskill to
recognize shock?
Use ofMRTs?
Handoffissues?
Are weeffectively
communicating
vital info?
Does afull ICU
mean delays?
YES
NO
NO
NO
YES
YES
Process Map Analysis
Kaizen bursts identify hand-offs or transactions that have the potential to create defects
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Measure Phase:
Measurement System Analysis (MSA)Can the variation in the parts (output) be detected over and
above the variation caused by the measurement system?
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Baseline Data Questions
• What is the current process capability? (Where are we now in terms of consistently meeting the customer’s needs?)
• Is the process stable?
• How much improvement do you need to meet your goal, to make a meaningful impact?
• What data are currently available?
• How will you know whether there has been an improvement?
• How does the current state compare to the CTQs?
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Measurement System Analysis (MSA)(pgs. 87 – 103)
Is it the right data to answer the question at hand?
or
Is it the best question the existing data can answer?
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Look Carefully
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Measurement System Analysis (MSA)(pg. 87 – 103)
A measurement system analysis is performed to determine if the measurement system can generate true reliable data, and to assure the variation observed is due to the actual performance of the process being studied, and not due to excessive variation in the measurement system itself.
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Measurement System Analysis (MSA)
“In any program of control we must start with observed data; yet data may be either good, bad, or indifferent. Of what value is theory of control if the observed data going into that theory are bad? This is the question raised again and again by the practical man (woman).” - Walter Shewhart
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Reliable Data ?
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Separate what we think is happening from what is really happening!
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Data Integrity?
• What assumptions were made?
• Is the data representative of the process ?
• Who generated the data?
• How was it measured?
• What is the noise in the measurement?
• If required, does it pass an audit?
• Can we trust the data and the measurement system used to generate the data to properly investigate the process?
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Exercise: You have 60 seconds to document the number of times the 6th letter of the alphabet appears in the following text:
Inspection
The Necessity of Training Farm Hands for First Class Farms in the Fatherly Handling of Farm Live Stock is Foremost in the Eyes of Farm Owners. Since the Forefathers of the Farm Owners Trained the Farm Hands for First Class Farms in the Fatherly Handling of Farm Live Stock, the Farm Owners Feel they should carry on with the Family Tradition of Training Farm Hands of First Class Farmers in the Fatherly Handling of Farm Live Stock Because they Believe it is the Basis of Good Fundamental Farm Management.
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6 Items To Look For In A Good Measurement System
ResolutionConsistencyRepeatabilityReproducibilityLinearityAccuracy
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Resolution• Is the measuring base unit small enough to adequately
evaluate the variation in the process? • Can we “see” differences in what the process is producing?• Must monitor the process frequently enough to catch it
varying, or going from good to bad.• As a general rule, we should use units of measure that are at
least 10 subdivisions of the range of measurement being investigated. “Ten bucket rule”
Examples of issues with resolution in your projects?
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Consistency (Stability) Issue
• Does the measurement system error remain stable or predictable over time, across equipment, across operators, across all shifts, across all facilities, etc…?
• Will we get reliable measurements from the process even if the measurements are taken on the weekends, during night shifts, by different employees, etc.?
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Measurement Systems
Measurement Systems must be Repeatable & Reproducible if we
are to draw adequate conclusions
Would it be OK if the time clock your employees get paid by is off by:
1 hour every day? 1 hour a week? 1 hour per month? 1 hour per year?
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Repeatability / Precision
• The variation in measurements obtained when one operator uses the same measuring process for measuring the identical characteristic of the same parts or items ( part dimension, blood pressure cuff, chemistry analyzer, etc.).
• Can the variation in the parts be detected over and above the variation caused by the measurement system?
• How closely will successive measurements of the same part or process by the same person using the same instrument repeat themselves?
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Reproducibility• The variation in the average of measurements made by
different operators using the same measuring process when measuring identical characteristics of the same items (two abstractors reviewing same chart).
• Reproducibility is very similar to repeatability. The primary difference is that instead of looking at the consistency of one person, we are looking at the consistency between people.
• Are the average measurements for each part reproducible across different operators, gages, machines, locations, etc…?
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Linearity
• Is the measurement system consistent across the entire range of the measurement scale?
• Are measurements reliable even at the extremes?
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Accuracy
• Are the measurements truly representative of the output of the process being studied?
• On average, do I get the “true data” from the output of the process?
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Accuracy vs. PrecisionNot Accurate, Not Precise
Accurate but not precise
Precise but not accurate
Accurate and Precise
..
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Key Questions for a MSA?(Your Project’s Measurement System)
• Is my measurement system repeatable - will I get the same results if I take the measurement more than once?
• Is my measurement system reproducible - will someone else be able to complete the same measurement and get the same results?
• Is my measurement system accurate - will the results from my study match the actual value, or expert data?
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MSA Recap
ADEQUATE INADEQUATE
Most of the variation is accounted for by physical or actual differences in the process or components.
Variation in how the measurements are taken is high.
- You can’t tell if differences between units or process observations are due to the way they were measured, or are true differences
- You can’t trust your data and therefore shouldn’t react to perceived patterns, special causes, etc.—they may be false signals
- All sources of measurement variation will be small
- You can have higher confidence that actions you take in response to the data are based on reality
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Why do we conduct MSA?(Your Project’s Measurement System)
• While many statistical tools may be very powerful, they can also provide misleading results if there is too much measurement error.
• We conduct MSA to gain an understanding of the quality, or trustworthiness, of data being collected to drive decisions about improving your process(es).
• Some part of the total observed variation inherent to a process is, in fact, caused by the measurement system itself. – How much variation can we tolerate?
• A good measurement system is vital for your baseline data as well as your investigations of possible Xs.
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Measure Phase:
Calculating Sigma Levels
and
Baseline Data and Metrics
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Why are Baseline Measures so Important?
“If we could first learn where we are and where we are going, we would be better able to judge what to do and how to do it.” Abraham Lincoln
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Calculating the Approximate Sigma Level
1. Define your opportunities
2. Define your defects
3. Measure your opportunities and
defects
4. Calculate your yield
5. Look up process Sigma
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Calculating the Approximate Sigma LevelDefine your opportunities and defects
• An opportunity is any area within a product, process, service, or other system where a defect could be produced or where you fail to achieve the ideal product or service in the eyes of the customer .
• A defect is any type of undesired result. The defect threshold may be as superficial as whether or not the product works. But it may be more subtle.
– This may be the difference between “Does the car run?” and “Does the car have a flawless paintjob, the tires I want, the brand of CD changer I want, etc, etc…”
– It’s usually not enough just to ask whether the product “meets expectations”… the expectations need to be defined.
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Calculating the Approximate Sigma Level
Measure your opportunities and defects and calculate your yield – the percent without defects.
Opportunities - Defects
Opportunitiesx 100
Total number of widgets
Total number of widgets minus widgets with defects
x 100
156
183x 100
85.24%
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Calculating the Approximate Sigma Level
• Look up process Sigma
A 85.24% yield is a process Sigma of 2.5 to 2.6
Discussion: What is your estimate of your process Sigma
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ActivityWorking individually1. Define an opportunity in your process. What’s a ballpark estimate of the number of opportunities in your process?
2. Define the defects in your process. What’s a ballpark estimate of the number of defects in your process?
3. Calculate your process yield
4. Find your Sigma level
(10 minutes to complete)
Opportunities - Defects
Opportunitiesx 100
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Balancing Measures• Balancing measures are often identified to prevent
important process, input, or output factors from being sacrificed at the expense of achieving a narrow goal.
• Prevent “tunnel-vision”
• Be alert for unintended consequences
• “Need to know” versus “nice to know”
• Balancing measures are those things we don’t want to lose sight of as we drive toward meeting our goal.
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Introductory Statapult Activity!• Working in teams,
– Try to hit a target distance (specification) with a projectile of your choice and your assigned statapult
– Collect the distance for each shot by team member in sequential order (6 total shots for each team member)
– In addition to the actual distance shot, also record if the shot is “in spec”, or “out of spec”
– Collect and record the total time it takes each team member to complete their respective 6 shots
– List potential xs that explain variation in the distance the projectile travels (Y) (If you have any variation?)
– List any waste that occurred in your statapult process
How well did your team perform? What is your team’s sigma level?Are you individually a good statapultician?
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Baseline Data Questions• What is the current process capability?
• Is the process stable?
• How much improvement do you need to meet your goal?
• What data are currently available? How many samples do I need to collect (pg. 85-86)
• How will you know whether there has been an improvement?
• How does the current state compare to the CTQs?
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Types of Data
Two major types of data (pg 70)
– Continuous (or “variable”)• Measurement along a continuum, length, height,
age, time, dollars, etc.
– Discrete (or “attribute”)• Categories, yes/no, names, labels, counts, etc.
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Types of Data
Continuous– Any variable that can be measured on a continuum or
scale that can be infinitely divided– There are more powerful statistical tools for interpreting
data continuous data, so it is generally preferred over discrete/attribute data
– Examples: height, weight, age, respiration rate, etc.
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Types of DataDiscrete Data Type Definition Example
Count How many? Count of errors; How many patients got evidence-based care? How many specimens were tested?
Binary Data that can have only one of two values
Was delivery on-time? Was the product defect-free? Alive/dead; Male/female; Yes/No
Nominal The data are names or labels with no intrinsic order or relative quantitative value
Colors; dog breeds; diagnoses; brands of products; nursing units; facility
Ordinal The names or labels represent some value inherent in the object or item (there is an obvious order to the items)
Product performance: excellent, very good, good, fair, poor; Severity: mild, moderate, severe, critical
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Types of Data
Example:
• Product meets design specifications
• Heart rate
• Distribution managers
• Gasoline grades (regular, plus, premium)
Type of data:
• Discrete – Binary
• Continuous
• Discrete – Nominal
• Discrete - Ordinal
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Baseline Capability
• A baseline capability study basically answers how well the current “as is” process meets the needs (specifications) of the customer. It can be tracked over time via run chart, control chart, etc.
• Process Capability compares the output of a process to the needs of the customer for a given key measure.
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Any deviation from the target causes
losses to the business
Taguchi Philosophy
LSL USL
Anything outside the specification limits
represents quality losses
Traditional Philosophy
“goalpost mentality”LSL USL
Process CapabilityUncontrolled Variation is Evil
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Process Capability: Variation
The New Goalpost Scoring
The New Business Reality
3 Points
2 Points
1 Point
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Characteristic of the Performance Gap… (Problem)Accuracy and/or Precision
LSLLSLUSLUSL USLUSLLSLLSL
Off-Target Variation
On-Target
CenterProcess
Reduce Spread
The statistical approach to problem solving
The statistical approach to problem solving
USLUSLLSLLSLLSL = Lower spec limit
USL = Upper spec limit
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Process Capability:Short Term and Long Term
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Short Term
Long Term
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Process Capability:Short Term and Long Term
• Processes experience more variation over a longer term than in the short term.
• Capability can vary depending on whether you are collecting data over a short term or a long term.
• The equations and basic concepts for calculating capability are identical for short term and long term except for how standard deviation is calculated to account for the increased variation over the long term.
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Is a 3 process a capable process?
Perfect World – Accurate & Consistent
Consistent, but not always accurate
Time
Long-term Capability
Short-term Capability
LSLUSL
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Process Capability:Short Term and Long Term
• Short Term (Cp and Cpk calculations)– Gathered over a limited number of cycles or intervals– Gathered over a limited number of shifts &
associates
• Long Term (Pp and Ppk calculations)– Gathered over many cycles, intervals, equipment, &
operators– May be attribute or variable– Assumes the data has “seen” at least 80% of the
total variation the process will experience
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Process Capability:Short Term and Long Term
(pgs. 135 – 140)
• Cp (short term) and Pp (long term) calculations compare the amount of variation in the process output to the total range of variation allowed (customer specifications)
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A Problem With Cp and Pp
Which is the better process?
What is the difference in Cp between the two processes?
What can be done to make Cp more effective as a process capability statistic?
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Process Capability:Short Term and Long Term
(pgs. 135 – 140)
• Cpk (short term) and Ppk (long term) compares the amount of variation and the location of the mean from the process output to the total range of variation allowed (customer specifications)
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Meet Ppk / CpkProcess Performance
Example:A process mean is 355,
standard deviation is 15,upper spec. limit is 380, and lower spec. limit is 270
What is the Cpk?What is the Cp?
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},min{
USLC
LSLC
CCC
pu
pl
puplpk
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Capability – Cpk’s
mUSLLSL
m USLLSL m USLLSL
Centered Process
Shifted Process Shifted Process
Cp = USL – LSL
6s
Cp = same
Cp = same
Cpk = USL-Mean
3s
OR
Cpk = Mean – LSL
3s
Cpk = less
Cpk = less
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0 +1 +4+2 +6+3 +5-1-2-3-5-6 -4
LSL USL
LSL USL
0 +1 +4+2 +6+3 +5-1-2-3-5-6 -4
0 +1 +4+2 +6+3 +5-1-2-3-5-6 -4
LSL USL
0+1 +4+2 +6+3 +5-1-2-3-5-6 -4
LSL USL
Cpk = 1
+/- 3σ within spec limits
Cpk = 1.33
+/- 4σ within spec limits
Cpk = 1.67
+/- 5σ within spec limits
Cpk = 2
+/- 6σ within spec limits
Cpk and Process Sigma
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Run ChartsThe Importance of Data Over Time
Graphical display: Run charts (also calledTime-series charts)
Con
tinuo
us Y
(e.
g.Le
ngth
of
Sta
y)
Discrete X (e.g. Month)
average
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Data Analysis / Statistical Software: Minitab
Brief Overview
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Improving how we Improve! (Through Data Analysis and Minitab)
Minitab is a tool consisting of many tools and techniques for thorough data analysis.
1.Do not think of Minitab as “giving you the answer.”
2.If you do not have reliable data, and/or you are not asking the proper analysis questions, Minitab will be of little value – if any!
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Improve: Data-Driven Approach
Is there a difference between Data and Information?
Data – factual information used as a basis for reasoningInformation – the communication or reception of knowledge obtained from investigation, study, or instruction
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Minitab
• Typical desktop icon for Minitab
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Minitab Overview
Toolbar
Session WindowTest results and messages will appear as running text. The text in this window can be modified, copied, and pasted
WorksheetYou can have multiple worksheets with your data arranged in columns. The grey line is where you put your column labels
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Minitab Overview
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Text column Date column Numeric data column
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Data Analysis and Minitab Remember the triple C’s for Data in Minitab
1.Organize data into Columns
2.Record/Input data Chronologically as appropriate
3.Data must be Clean (no commas, dollar signs, etc.)
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Descriptive Statistics
• Using the data collected in the statapult exercise, look at the descriptive stats– Stat>Basic Statistics>Display Descriptive Statistic– Stat>Basic Statistics>Graphical Summary
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Descriptive StatsDescriptive Statistics: Distance
Variable N N* Mean SE Mean StDev Minimum Q1 Median Q3Distance 75 0 78.880 0.549 4.756 55.000 77.000 79.000 81.000
Variable MaximumDistance 87.000
9085807570656055
30
25
20
15
10
5
0
Distance
Frequency
Mean 78.88StDev 4.756N 75
Histogram (with Normal Curve) of Distance
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Graphical Summary
85807570656055
Median
Mean
80.079.579.078.578.0
1st Quartile 77.000Median 79.0003rd Quartile 81.000Maximum 87.000
77.786 79.974
78.286 80.000
4.098 5.668
A-Squared 1.66P-Value < 0.005
Mean 78.880StDev 4.756Variance 22.621Skewness -1.83016Kurtosis 7.84318N 75
Minimum 55.000
Anderson-Darling Normality Test
95% Confidence Interval for Mean
95% Confidence Interval for Median
95% Confidence Interval for StDev95% Confidence I ntervals
Summary for Distance
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Capability Analysis
• Stat>Quality Tools>Capability Analysis
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Short Term Variation - Example
• Use Minitab to estimate short term variation:– Stat > Quality Tools > Capability Analysis
(Normal)
126.0125.4124.8124.2123.6
LSL USL
Process Data
Sample N 35StDev(Within) 0.49662StDev(Overall) 0.48982
LSL 123.50000Target *USL 126.50000Sample Mean 124.60711
Potential (Within) Capability
CCpk 1.01
Overall Capability
Pp 1.02PPL 0.75PPU 1.29Ppk
Cp
0.75Cpm *
1.01CPL 0.74CPU 1.27Cpk 0.74
Observed PerformancePPM < LSL 28571.43PPM > USL 0.00PPM Total 28571.43
Exp. Within PerformancePPM < LSL 12897.49PPM > USL 69.05PPM Total 12966.54
Exp. Overall PerformancePPM < LSL 11902.71PPM > USL 55.66PPM Total 11958.37
WithinOverall
Process Capability of ChambTemp
Project: Untitled; 9/9/2004
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Capability Six-Pack
71645750433629221581
90
75
60
Indiv
idual V
alu
e
_X=78.88
UCL=90.58
LCL=67.18
71645750433629221581
20
10
0
Movin
g R
ange
__MR=4.40
UCL=14.37
LCL=0
7570656055
80
70
60
Observation
Valu
es
9085807570656055
LSL USL
LSL 75USL 85
Specifications
90807060
Within
Overall
Specs
StDev 3.89951Cp 0.43Cpk 0.33
WithinStDev 4.75611Pp 0.35Ppk 0.27Cpm *
Overall
1
1
11
1
Process Capability Sixpack of DistanceI Chart
Moving Range Chart
Last 25 Observations
Capability Histogram
Normal Prob PlotAD: 1.656, P: < 0.005
Capability Plot
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Measure Phase:
Pareto Charting and Analysis
(The 80/20 Rule)
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Pareto chart
• A Pareto chart is a special type of bar graph where the categories are arranged from largest to smallest with a line indicating the cumulative percent
Vilfredo Pareto observed that 80% of the land in Italy was owned by 20% of the population.
Later, Joseph Juran called this “80-20 rule” the Pareto principle.
80% of the effects come from 20% of the causes.
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Lean Six Sigma Project and Team Basic Tools
Pareto Analysis (pg. 142-144)
A Pareto chart is simply a bar graph with the bars arranged typically in descending order from highest to lowest frequency by discrete category. It graphically displays the 80/20 rule. Approximately 80% of the quantifiable results (frequency), will be attributed to 20% of the causal categories.
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Create the Pareto Chart
• Go to Stat>Quality Tools>Pareto Chart• Select “Chart Defects Table”• Defects or attribute data in: Colors• Frequencies in: Counts
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Create the Pareto Chart
• Click on Options
• Label the X axis “M&M Color”
• Label the Y axis “Count”
• Give your chart a title
• Click on OK
• Click on OK again
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Your Pareto Chart
…should look something like this:
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Measure Phase:
Cause and Effect Analysis
(Collecting the “theories” of x’s)
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Statapult Activity Follow-up
• Working with your team– Discuss the effect (Y results) of your statapult process
(the head of your fishbone diagram)?– How satisfied are you with the measurement system for
your process output– List some potential xs (theories) that affect your process
outcome (Y).– Construct a fishbone diagram of the potential x’s– Discuss how we might determine the most significant x’s– List some categories of waste experienced by your team– Prepare a mini-presentation (5 mins) to share with class
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Lean Six Sigma Project and Team Basic Tools
Cause and Effect Diagrams (pg. 146-149)
A C&E diagram (also called a fishbone diagram), is a pictorial display of the potential or likely causes of a given effect. The causes are grouped and arranged in meaningful categories, sometimes called branches. There are numerous ways to name the grouped branches. The most common names include: Material, Method, Manpower, Machinery, Measurement, and Mother Nature (Environment).
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Other Fishbone categories
• 6 Ms– Method, Material, Manpower, Machinery,
Measurement, Mother Nature
• 4 Ps– Policies, Procedures, Personnel, Place
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Cause & Effect Matrix Form
1 2 3
Tim
e to
quote
# o
f conta
cts
Quote
acc
ura
cy
Total
Process Step Process Input1 Create Customer Header Cust ID 9 9 9 1982 Identify Products Parametric design 9 9 9 1983 Identify Products Supercedes ref 9 9 9 1984 Generate price SCR200 9 3 9 1745 Generate price SPA file 9 3 9 1746 Generate price Price sheet 9 1 9 1667 Generate price Marketing approval 9 1 9 1668 Customer creates header Credit status 3 9 9 1509 Identify Products Tech rep exp 9 9 3 13810 Identify Products Spec features 3 3 9 12611 Identify Products SCR8000 Xref 3 3 9 12612 Issue quote Marketing approval 3 1 9 11813 Identify Products Cust prod ID 9 3 3 114
Cause & Effect MatrixRating of Importance to Customer
8 4 10
Natural break, Sanity Natural break, Sanity checkcheck
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Cause and Effect Chart
• Stat>Quality Tools>Cause-and-Effect• In Minitab, you can build your C&E Chart from
lists of potential Xs in the workbook or by keying them into the dialogue box
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Xs in the Worksheet
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Xs typed in as constants
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Sub-branches
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Measure Phase:
Data Collection Plan
and Preparation for Analysis(Data Collecting for the “theories” of x’s)
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Data Collection Plan (pgs. 72 – 81)
• Data are the documentation of an observation or measurement. Data are facts, but you may need information – data which provide the answers to questions you have.
• A good data collection plan helps ensure data will be useful (measuring the right things) and statistically valid (measuring things right).
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Data Collection Plan (pgs. 72 – 74)
1. Decide what to collect
2. Decide on stratification factors as needed
3. Develop operational definitions
4. Determine the appropriate/needed sample size
5. Identify the source/location of data
6. Develop data collection forms/check sheets
7. Decide who will collect the data
8. Train data collectors
9. Do ground work for analysis
10. Execute your data collection plan
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Data Collection Plan1. Formulate the question or theory: What is the question
we are trying to answer?
2. Decide how data will be communicated and analyzed.
3. Decide how to measure: population or sample?
4. Collect data with a minimum of bias.
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Patient Month
Time to ABX administration in
minutes
Were ABX administered
within 4 hours?A January 109 YB January 205 YC January 256 ND February 245 NE February 250 NF February 264 NG February 157 YH March 125 YI March 223 YJ March 215 YK March 315 NL April 125 YM April 267 NN April 207 YO May 185 YP May 162 YQ May 243 NR June 239 YS June 235 YT June 225 YU June 237 Y
Data Collection PlanAsking the Right/Best Question
Time to ABX in Minutes is captured using a continuous measure: “How many minutes did it take?”
It can be converted into a
discrete measure: “Was
it done within four hours?”
What kind of data will you be collecting?
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Data CollectionAsking the Right Question
Is the measure you are using a good one?
• Understandable
• Provides information for decision making
• Applies broadly
• Is conducive to uniform interpretation
• Is economical to apply
• Is compatible with existing design of sensors
• Is measurable even in the face of abstractions
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Data Collection PlanCommunicating the Results
• Although you may not know what the data reveals – and it may seem odd to be thinking about how your team will analyze and display the data -- having some idea about the sort of analysis and display you will use will help you make decisions about the data you collect.
• If you wait until after the data are collected to think about analysis, you may find that the data do not support the kind of analysis you want to conduct.
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SamplingQualities of a Good Sample
• Free from bias– Bias is the presence of some undue influence on the sample
selection process that causes the population to appear different than it actually is
• Representative– The data should accurately reflect a population. Representative
sampling helps avoid biases specific to segments of the population
• Random– The data are collected in no predetermined order and each
element has an equal chance of being selected
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Sampling
• Random Sampling – each element has an equal chance of being selected– Simple random (no pattern)– Systematic random (every Nth value)
• Stratified Random Sampling – the population is grouped into levels or “strata” according to some characteristic and proportional samples are drawn randomly from each stratum
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Random Sampling
X
X
X
X
X
X
X
X
X
X X
X
XX
X
X
X
X
XX
Each element has an equal chance of being chosen
Population
Sample
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Stratified Random Sampling
• Randomly sampled from each stratified category or group
• Sample sizes for each stratum are generally proportional to the size of the group within the population
X XXX X
Y YY YY
YYYY Y YY
ZZ ZZZ
ZZZ ZZ
Population
Sample
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Sampling
• Fixed percentage sampling – leads to undersampling from small populations and oversampling from large populations
• Judgment sampling – using judgment to select x number of “representative” samples - guess
• Chunk or convenience sampling – selecting sample simply because the items are conveniently grouped
The following are NOT appropriate ways to get a valid random sample:
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Sampling (pgs. 85-86)
Sample size calculation for continuous data
n = 1.96sΔ
2
n Minimum sample size
1.96 Constant representing a confidence interval of 95% (valid when sample size is 30 or more)
s Estimate of standard deviation of data
Δ The level of precision desired from the sample you are trying to detect (same units as s)
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Sampling
Sample size calculation for discrete data
n Minimum sample size
1.96 Constant representing a confidence interval of 95% (valid when sample size is 30 or more)
s Estimate of standard deviation of data
P Estimate of the proportion defective
Δ The level of precision desired from the sample you are trying to detect (same units as s)
n = 1.96sΔ
2
P (1-P )
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Ask the Right Question
Right/Appropriate Data
Proper Analysis
Correct Interpretation
Correct Audience
Appropriate Action
- Bias the question with existing belief system
- No easy access to data systems- Substitute what is needed with what is available- Missing and incomplete data- Data values are incorrect
- Insufficient statistical skill- Inadequate statistical software- Analysis paralysis
- Unwilling to take action- Analysis paralysis
- Decision errors from false positives / false negatives- Refusal to accept the facts- Bias the interpretation with existing belief system- Intellectual dishonesty
- Unable to take action
Effective Data Driven PracticePotential Failure ModesSteps to Effective
Data Driven Practice
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Lean Six SigmaDMAIC Phase Objectives
• Define… what needs to be improved and why
• Measure…what is the current state/performance level and potential causes
• Analyze…collect data and test to determine significant contributing causes
• Improve…identify and implement improvements for the significant causes
• Control…hold the gains of the improved process and monitor
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Start Date: Enter Date End Date: Enter Date
Benchmark Analysis Project Charter Formal Champion
Approval of Charter (signed)
SIPOC - High Level Process Map
Customer CTQs Initial Team meeting
(kickoff)
Start Date: Enter DateEnd Date: Enter Date
Identify Project Y(s) Identify Possible Xs
(possible cause and effect relationships)
Develop & Execute Data Collection Plan
Measurement System Analysis
Establish Baseline Performance
Start Date: Enter DateEnd Date: Enter Date
Identify Vital Few Root Causes of Variation Sources & Improvement Opportunities
Define Performance Objective(s) for Key Xs
Quantify potential $ Benefit
Start Date: Enter DateEnd Date: Enter Date
Generate Solutions Prioritize Solutions Assess Risks Test Solutions Cost Benefit
Analysis Develop &
Implement Execution Plan
Formal Champion Approval
Start Date: Enter DateEnd Date: Enter Date
Implement Sustainable Process Controls – Validate:
Control System Monitoring Plan Response Plan System Integration
Plan $ Benefits Validated Formal Champion
Approval and Report Out
Author: Enter NameDate: April 19, 2023
Project Name:Problem Statement:Mislabeled example
Project Scope:Enter scope description
Champion: NameProcess Owner: NameBlack Belt: NameGreen Belts:Names
Customer(s):CTQ(s):Defect(s):Beginning DPMO:Target DPMO:Estimated Benefits:Actual Benefits:
Not Complete Complete Not Applicable
MeasureMeasureDefineDefine
Directions:•Replace All Of The Italicized, Black Text With Your Project’s Information•Change the blank box into a check mark by clicking on Format>Bullets and•Numbering and changing the bullet.
AnalyzeAnalyze ImproveImprove ControlControl
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Going Forward with your Project and Analysis
“What’s different in me is that I still pose to myself the questions that people quit making when they were five years old.” Albert Einstein
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