the dmaic lean six sigma project and team tools approach measure phase 1

The DMAIC Lean Six Sigma Project and Team Tools Approach

Measure Phase

1

Lean Six Sigma Combo/Black Belt Training! Agenda – Measure Phase

Welcome Back, Brief Review Process Thinking, Mapping, and 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

Measure Objectives (pg. 8-11)

• Identify the Project Y

• Define the performance standards for Y, and its baseline (current state) performance

• Clarify understanding of specification limits as well as defect and opportunity definitions

• Validate the measurement system (MSA)

• Collect the data as needed

• Characterize the data using basic tools and capability

• Begin funneling the X’s that affect the Y

• Measure…what is the current state/performance level and potential causes

3

Why spend so much time in the Measure phase?

“When you can measure what you are speaking about, and express it in numbers, you know something about it; but when you cannot measure it, when you cannot express it in numbers, your knowledge is of a meager and unsatisfactory kind…” Lord Kelvin

“If you can’t measure it, you can’t manage it.”

Peter Drucker4

Why Do We Measure?• To thoroughly understand the current state of our

process and collect reliable data on process inputs that you will use to expose the underlying causes of problems

• To know “where you are” – the extent of the problem

• To understand and quantify the critical inputs (xs) that we believe (theories) are contributing to our problem (Ys)

5

Lean Six SigmaDMAIC Phase Objectives

• Define… what needs to be improved and why


• Analyze…collect data and test to determine significant contributing causes

• Improve…identify and implement improvements for the significant causes

• Control…hold the gains of the improved process and monitor

6

LSS PROJECT FOCUS

Process 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)

7

Measure Phase:

Process Mapping

8

The Basic Philosophy of Lean Six Sigma

• All processes have variation and waste• All variation and waste has causes • Typically only a few causes are significant• To the degree that those causes can be understood

they can be controlled• Designs must be robust to the effects of the remaining

process variation• This is true for products, processes, information

transfer, transactions, everything• Uncontrolled variation and waste is the enemy

9

Remember - What is Six Sigma…

•A high performance measure of excellence•A metric for quality

•A business philosophy to improve customer satisfaction•Focuses on processes and customers•Delivers results that matter for all key stakeholders

•A tool for eliminating process variation•Structured methodology to reduce defects

•Enables cultural change, it is transformational

10

Why Process Thinking?

Allows criticism without blaming people

Allows shared understanding of how things work

Helps manage complexity

Provides focus within context

Helps to manage scope of project

Identification of team members

Understand inputs / outputs - leads to measurement

11

High Level Process Map - SIPOC

Process Name

Supplier-Inputs-Process-Outputs-Customer ….………………………………………..…….….……………………………………………...….……………………………………………...….……………………………………………...………………………………………………………………………………………………………………………………………………………

12

High Level 1 Box Examples

Outputs

Time to quoteNumber of contactsQuote accuracy

Inputs

Customer NameCustomer IDBill toShip toCredit status

Quoting Job

13

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

14

Lean Six Sigma Project and Team Basic Tools

Process Flow Chart (pg. 33-44)

A visual display of the key steps and flow of a process, also called a process map. Usually standard symbols are used to construct process flow charts. These include boxes (or rectangles) for specific steps, diamonds for decision points, ovals for defined starting and stopping points, and arrows to indicate flow.

Processes can include providing a service, making or delivering products, information sharing, design, etc. – Should represent the current as-is state of the process!

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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

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

25

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

http://www.oregon.gov27

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

33


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)

34


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

35


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)

36


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

37


Conflicting objectives

– Unclear alignment from one group to another working in the same process

– Direction from leadership and metrics


38


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


– The “Hidden Factory”

The Hidden Factory

All of the work that is performed that is above and beyond what is required to deliver good products and services to the customer; work that is not necessarily tracked (cost, productivity, etc.).

Work-arounds or “built-in” Rework

39

40

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.

41

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

Measure Phase:

Measurement System Analysis (MSA)Can the variation in the parts (output) be detected over and

above the variation caused by the measurement system?

43

Baseline Data Questions

• What is the current process capability? (Where are we now in terms of consistently meeting the customer’s needs?)

• Is the process stable?

• How much improvement do you need to meet your goal, to make a meaningful impact?

• What data are currently available?

• How will you know whether there has been an improvement?

• How does the current state compare to the CTQs?

44

Measurement System Analysis (MSA)(pgs. 87 – 103)

Is it the right data to answer the question at hand?

or

Is it the best question the existing data can answer?

45

Look Carefully

46

Measurement System Analysis (MSA)(pg. 87 – 103)

A measurement system analysis is performed to determine if the measurement system can generate true reliable data, and to assure the variation observed is due to the actual performance of the process being studied, and not due to excessive variation in the measurement system itself.

47

Measurement System Analysis (MSA)

“In any program of control we must start with observed data; yet data may be either good, bad, or indifferent. Of what value is theory of control if the observed data going into that theory are bad? This is the question raised again and again by the practical man (woman).” - Walter Shewhart

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Reliable Data ?

49

Separate what we think is happening from what is really happening!

50

Data Integrity?

• What assumptions were made?

• Is the data representative of the process ?

• Who generated the data?

• How was it measured?

• What is the noise in the measurement?

• If required, does it pass an audit?

• Can we trust the data and the measurement system used to generate the data to properly investigate the process?

51

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.

52

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?

54

Consistency (Stability) Issue

• Does the measurement system error remain stable or predictable over time, across equipment, across operators, across all shifts, across all facilities, etc…?

• Will we get reliable measurements from the process even if the measurements are taken on the weekends, during night shifts, by different employees, etc.?

55

Measurement Systems

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?

56

Repeatability / Precision

• The variation in measurements obtained when one operator uses the same measuring process for measuring the identical characteristic of the same parts or items ( part dimension, blood pressure cuff, chemistry analyzer, etc.).

• Can the variation in the parts be detected over and above the variation caused by the measurement system?

• How closely will successive measurements of the same part or process by the same person using the same instrument repeat themselves?

57

Reproducibility• The variation in the average of measurements made by

different operators using the same measuring process when measuring identical characteristics of the same items (two abstractors reviewing same chart).

• Reproducibility is very similar to repeatability. The primary difference is that instead of looking at the consistency of one person, we are looking at the consistency between people.

• Are the average measurements for each part reproducible across different operators, gages, machines, locations, etc…?

58

Linearity

• Is the measurement system consistent across the entire range of the measurement scale?

• Are measurements reliable even at the extremes?

59

Accuracy

• Are the measurements truly representative of the output of the process being studied?

• On average, do I get the “true data” from the output of the process?

60

Accuracy vs. PrecisionNot Accurate, Not Precise

Accurate but not precise

Precise but not accurate

Accurate and Precise

..

.

.

.

.

.

..

..

... ...

.

. .. ....

..... .. .

61

Key Questions for a MSA?(Your Project’s Measurement System)

• Is my measurement system repeatable - will I get the same results if I take the measurement more than once?

• Is my measurement system reproducible - will someone else be able to complete the same measurement and get the same results?

• Is my measurement system accurate - will the results from my study match the actual value, or expert data?

63

MSA Recap

ADEQUATE 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

64

Why do we conduct MSA?(Your Project’s Measurement System)

• While many statistical tools may be very powerful, they can also provide misleading results if there is too much measurement error.

• We conduct MSA to gain an understanding of the quality, or trustworthiness, of data being collected to drive decisions about improving your process(es).

• Some part of the total observed variation inherent to a process is, in fact, caused by the measurement system itself. – How much variation can we tolerate?

• A good measurement system is vital for your baseline data as well as your investigations of possible Xs.

65

Measure Phase:

Calculating Sigma Levels

and

Baseline Data and Metrics

66

Why are Baseline Measures so Important?

“If we could first learn where we are and where we are going, we would be better able to judge what to do and how to do it.” Abraham Lincoln

67

Calculating the Approximate Sigma Level

1. Define your opportunities

2. Define your defects

3. Measure your opportunities and

defects

4. Calculate your yield

5. Look up process Sigma

68

Calculating the Approximate Sigma 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.

69


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%

70


• Look up process Sigma

A 85.24% yield is a process Sigma of 2.5 to 2.6

Discussion: What is your estimate of your process Sigma

71

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

72

73

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.

74

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?

Baseline Data Questions• What is the current process capability?

• Is the process stable?

• How much improvement do you need to meet your goal?

• What data are currently available? How many samples do I need to collect (pg. 85-86)

• How will you know whether there has been an improvement?

• How does the current state compare to the CTQs?

75

Types of Data

Two major types of data (pg 70)

– Continuous (or “variable”)• Measurement along a continuum, length, height,

age, time, dollars, etc.

– Discrete (or “attribute”)• Categories, yes/no, names, labels, counts, etc.

76

Types of Data

Continuous– Any variable that can be measured on a continuum or

scale that can be infinitely divided– There are more powerful statistical tools for interpreting

data continuous data, so it is generally preferred over discrete/attribute data

– Examples: height, weight, age, respiration rate, etc.

77

Types of 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

78

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

79

Baseline Capability

• A baseline capability study basically answers how well the current “as is” process meets the needs (specifications) of the customer. It can be tracked over time via run chart, control chart, etc.

• Process Capability compares the output of a process to the needs of the customer for a given key measure.

80

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

81

Process Capability: Variation

The New Goalpost Scoring

The New Business Reality

3 Points

2 Points

1 Point

82

Characteristic of the Performance Gap… (Problem)Accuracy and/or Precision

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

83

Process Capability:Short Term and Long Term

|

-5

|

-4

|

-3

|

-2

|

-1

|

0

|

5

|

4

|

3

|

2

|

1

Short Term

Long Term

84


• Processes experience more variation over a longer term than in the short term.

• Capability can vary depending on whether you are collecting data over a short term or a long term.

• The equations and basic concepts for calculating capability are identical for short term and long term except for how standard deviation is calculated to account for the increased variation over the long term.

85

Is a 3 process a capable process?

Perfect World – Accurate & Consistent

Consistent, but not always accurate

Time

Long-term Capability

Short-term Capability

LSLUSL

86


• Short Term (Cp and Cpk calculations)– Gathered over a limited number of cycles or intervals– Gathered over a limited number of shifts &

associates

• Long Term (Pp and Ppk calculations)– Gathered over many cycles, intervals, equipment, &

operators– May be attribute or variable– Assumes the data has “seen” at least 80% of the

total variation the process will experience

87


(pgs. 135 – 140)

• Cp (short term) and Pp (long term) calculations compare the amount of variation in the process output to the total range of variation allowed (customer specifications)

88

A Problem With Cp and Pp

Which is the better process?

What is the difference in Cp between the two processes?

What can be done to make Cp more effective as a process capability statistic?

|

-5

|

-4

|

-3

|

-2

|

-1

|

0

|

5

|

4

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3

|

2

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1

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-4

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-3

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-2

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-1

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0

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5

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4

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3

|

2

|

1

89


(pgs. 135 – 140)

• Cpk (short term) and Ppk (long term) compares the amount of variation and the location of the mean from the process output to the total range of variation allowed (customer specifications)

90

Meet Ppk / 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?

|

-4

|

-3

|

-2

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-1

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0

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5

|

4

|

3

|

2

|

1

3

3

},min{

USLC

LSLC

CCC

pu

pl

puplpk

91

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

92

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


Cpk = 1.67


Cpk = 2


Cpk and Process Sigma

93

94

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

95

Data Analysis / Statistical Software: Minitab

Brief Overview

Improving how we Improve! (Through Data Analysis and Minitab)

Minitab is a tool consisting of many tools and techniques for thorough data analysis.

1.Do not think of Minitab as “giving you the answer.”

2.If you do not have reliable data, and/or you are not asking the proper analysis questions, Minitab will be of little value – if any!

96

97

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

98

Minitab

• Typical desktop icon for Minitab

99

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

100

Minitab Overview

101

Text column Date column Numeric data column

Data Analysis and Minitab Remember the triple C’s for Data in Minitab

1.Organize data into Columns

2.Record/Input data Chronologically as appropriate

3.Data must be Clean (no commas, dollar signs, etc.)

102

103

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

104

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

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

105

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Capability Analysis

• Stat>Quality Tools>Capability Analysis

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

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

108

Measure Phase:

Pareto Charting and Analysis

(The 80/20 Rule)

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110

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.


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:


115

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


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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121

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Other Fishbone categories

• 6 Ms– Method, Material, Manpower, Machinery,

Measurement, Mother Nature

• 4 Ps– Policies, Procedures, Personnel, Place

123

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

124

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

125

Xs in the Worksheet

126

Xs typed in as constants

127

Sub-branches

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?

133

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

136

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

137

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

138

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

139

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)

141

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 )

142

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

Lean Six SigmaDMAIC Phase Objectives

• Define… what needs to be improved and why


• 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


Identify Vital Few Root Causes of Variation Sources & Improvement Opportunities

Define Performance Objective(s) for Key Xs

Quantify potential $ Benefit


Generate Solutions Prioritize Solutions Assess Risks Test Solutions Cost Benefit

Analysis Develop &

Implement Execution Plan

Formal Champion Approval


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

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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the dmaic lean six sigma project and team tools approach measure phase 1

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