Principles of Management and Total Quality Management Unit I – Planning.
Quality management and quality planning
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Transcript of Quality management and quality planning
Quality Management and
Quality PlanningBy
Amartya Talukdar
Cost of Quality – 4 Categories
Early detection/prevention is less costly (Maybe by a factor of 10)
Ways of Improving Quality Plan-Do-Study-Act Cycle (PDSA)
Also called the Deming Wheel after originator
Circular, never ending problem solving process
Seven Tools of Quality Control Tools typically taught to problem solving
teams Quality Function Deployment
Used to translate customer preferences to design
PDSA Details Plan
Evaluate current process Collect procedures, data, identify problems Develop an improvement plan, performance
objectives Do
Implement the plan – trial basis Study
Collect data and evaluate against objectives Act
Communicate the results from trial If successful, implement new process
Seven Tools of Quality Control1. Cause-and-Effect Diagrams2. Flowcharts3. Checklists4. Control Charts5. Scatter Diagrams6. Pareto Analysis7. Histograms
Cause-and-Effect Diagrams Called Fishbone Diagram Focused on solving identified quality problem
Flowcharts Used to document the detailed steps in
a process Often the first step in Process Re-
Engineering
Control Charts Important tool used in Statistical
Process Control The UCL and LCL are calculated limits
used to show when process is in or out of control
Scatter Diagrams A graph that shows how two variables
are related to one another Data can be used in a regression analysis
to establish equation for the relationship
Pareto Analysis Technique that displays the degree of importance for
each element Named after the 19th century Italian economist; often
called the 80-20 Rule Principle is that quality problems are the result of only a
few problems e.g. 80% of the problems caused by 20% of causes
Histograms A chart that shows the frequency distribution
of observed values of a variable like service time
at a bank drive-up window Displays whether the distribution is
symmetrical (normal) or skewed
Product Design - Quality Function Deployment
Critical to ensure product design meets customer expectations
Useful tool for translating customer specifications into technical requirements is Quality Function Deployment (QFD)
QFD encompasses Customer requirements Competitive evaluation Product characteristics Relationship matrix Trade-off matrix Setting Targets
Quality Function Deployment(QFD) Details
Process used to ensure that the product meets customer specifications
Voice of theengineer
Voice of the
customer
Customer-basedbenchmarks
QFD - House of Quality
Adding trade-offs, targets & developing product specifications
Trade-offs
Targets
TechnicalBenchmarks
Reliability – critical to quality
Reliability is the probability that the product, service or part will function as expected
No product is 100% certain to function properly
Reliability is a probability function dependent on sub-parts or components
Reliability – critical to quality
Reliability of a system is the product of component reliabilities
RS = (R1) (R2) (R3) . . . (Rn)RS = reliability of the product or
systemR1 = reliability of the components Increase reliability by placing
components in parallel
Reliability – critical to quality
Increase reliability by placing components in parallel
Parallel components allow system to operate if one or the other fails
RS = R1 + (R2* Probability of needing 2nd component)
Process Management & Managing Supplier Quality
Quality products come from quality sources
Quality must be built into the process Quality at the source is belief that it is
better to uncover source of quality problems and correct it
TQM extends to quality of product from company’s suppliers
ISO Standards ISO 9000 Standards:
Certification developed by International Organization for Standardization
Set of internationally recognized quality standards
Companies are periodically audited & certified
ISO 9000:2000 QMS – Fundamentals and Standards ISO 9001:2000 QMS – Requirements ISO 9004:2000 QMS - Guidelines for
Performance More than 40,000 companies have been
certified ISO 14000:
Focuses on a company’s environmental responsibility
Why TQM Efforts Fail Lack of a genuine quality
culture Lack of top management
support and commitment Over- and under-reliance on
SPC methods
TQM Within OM TQM is broad sweeping organizational change TQM impacts
Marketing – providing key inputs of customer information
Finance – evaluating and monitoring financial impact Accounting – provides exact costing Engineering – translate customer requirements into
specific engineering terms Purchasing – acquiring materials to support product
development Human Resources – hire employees with skills
necessary Information systems – increased need for accessible
information
Quality Planning It is important to design in quality and
communicate important factors that directly contribute to meeting the customer’s requirements
Design of experiments helps identify which variables have the most influence on the overall outcome of a process
Many scope aspects of IT projects affect quality like functionality, features, system outputs, performance, reliability, and maintainability
Quality Assurance Quality assurance includes all the
activities related to satisfying the relevant quality standards for a project
Another goal of quality assurance is continuous quality improvement
Benchmarking can be used to generate ideas for quality improvements
Quality audits help identify lessons learned that can improve performance on current or future projects
Quality Assurance Plan
Quality Assurance Plan
Pareto Analysis Pareto analysis involves identifying
the vital few contributors that account for the most quality problems in a system
Also called the 80-20 rule, meaning that 80% of problems are often due to 20% of the causes
Pareto diagrams are histograms that help identify and prioritize problem areas
Sample Pareto Diagram
Statistical Sampling and Standard Deviation
Statistical sampling involves choosing part of a population of interest for inspection
The size of a sample depends on how representative you want the sample to be
Sample size formula:Sample size = .25 X (certainty Factor/acceptable
error)2
Commonly Used Certainty Factors
Desired Certainty Certainty Factor
95% 1.960
90% 1.645
80% 1.281
95% certainty: Sample size = 0.25 X (1.960/.05) 2 = 38490% certainty: Sample size = 0.25 X (1.645/.10)2 = 6880% certainty: Sample size = 0.25 X (1.281/.20)2 = 10
Six Sigma DefinedSix Sigma is “a comprehensive and flexible system for achieving, sustaining and maximizing business success. Six Sigma is uniquely driven by close understanding of customer needs, disciplined use of facts, data, and statistical analysis, and diligent attention to managing, improving, and reinventing business processes.”**Pande, Peter S., Robert P. Neuman, and Roland R. Cavanagh, TheSix Sigma Way. New York: McGraw-Hill, 2000, p. xi
Basic Information on Six Sigma The target for perfection is the
achievement of no more than 3.4 defects per million opportunities
The principles can apply to a wide variety of processes
Six Sigma projects normally follow a five-phase improvement process called DMAIC
DMAIC Define: Define the problem/opportunity,
process, and customer requirements Measure: Define measures, collect,
compile, and display data Analyze: Scrutinize process details to
find improvement opportunities Improve: Generate solutions and ideas
for improving the problem Control: Track and verify the stability of
the improvements and the predictability of the solution
How is Six Sigma Quality Control Unique? It requires an organization-wide
commitment Six Sigma organizations have the ability
and willingness to adopt contrary objectives, like reducing errors and getting things done faster
It is an operating philosophy that is customer-focused and strives to drive out waste, raise levels of quality, and improve financial performance at breakthrough levels
Examples of Six Sigma Organizations Motorola, Inc. pioneered the adoption of
Six Sigma in the 1980s and saved about $14 billion
Allied Signal/Honeywell saved more than $600 million a year by reducing the costs of reworking defects and improving aircraft engine design processes
General Electric uses Six Sigma to focus on achieving customer satisfaction
Six Sigma and Project Management
Joseph M. Juran stated that “all improvement takes place project by project, and in no other way”
It’s important to select projects carefully and apply higher quality where it makes sense
Six Sigma projects must focus on a quality problem or gap between current and desired performance and not have a clearly understood problem or a predetermined solution
After selecting Six Sigma projects, the project management concepts, tools, and techniques described in this text come into play, such as creating business cases, project charters, schedules, budgets, etc.
Six Sigma and Statistics The term sigma means standard
deviation Standard deviation measures how much
variation exists in a distribution of data Standard deviation is a key factor in
determining the acceptable number of defective units found in a population
Six Sigma projects strive for no more than 3.4 defects per million opportunities
Standard Deviation A small standard deviation means
that data cluster closely around the middle of a distribution and there is little variability among the data
A normal distribution is a bell-shaped curve that is symmetrical about the mean or average value of a population
Normal Distribution and Standard Deviation
Six Sigma as a PhilosophyInternal &ExternalFailure Costs
Prevention &Appraisal
Costs
Old Belief4sC
osts
Internal &ExternalFailure Costs
Prevention &Appraisal
Costs
New BeliefCos
ts4s
5s
6s
Quality
Quality
Old Belief
High Quality = High Cost
New Belief
High Quality = Low Cost
s is a measure of how much variation exists in a process
Focus: The End User Customer: Internal or External Consumer: The End User
the “Voice of the Consumer” (Consumer Cue)must be translated into
the “Voice of the Engineer” (Technical Requirement)
Six Sigma as a Metric
CENSO
RED
1)( 2
n
xxiU
sSigma = s = Deviation ( Square root of variance )
-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7
Axis graduated in Sigma
68.27 %
95.45 %
99.73 %
99.9937 %
99.999943 %
99.9999998 %
result: 317300 ppm outside (deviation)
45500 ppm
2700 ppm
63 ppm
0.57 ppm
0.002 ppm
between + / - 1sbetween + / - 2sbetween + / - 3sbetween + / - 4sbetween + / - 5sbetween + / - 6s
s =
Six Sigma as a ToolProcess Mapping Tolerance Analysis
Structure Tree Components Search
Pareto Analysis Hypothesis Testing
Gauge R & R Regression
Rational Subgrouping DOE
Baselining SPC
Many familiar quality tools applied in a structured methodology
Distinguish “Vital Few”from “Trivial Many”
Y = Dependent Variable Output, Defectx = Independent Variables Potential Causex* = Independent Variable Critical Cause
Define the Problem / Defect Statement
Y = f ( x1*, x2, x3, x4
*, x5. . . Xn)
Process(Parameters)
Material
Methods
People
Environment
OutputMachine
Measurements
Strategy by Phase - Phase
Measure(What)
Analyze(Where, When, Why)
Improve(How)
Control(Sustain, Leverage)
Step
What is the frequency of Defects?• Define the defect• Define performance standards• Validate measurement system• Establish capability metric
Where, when and why do Defects occur?• Identify sources of variation• Determine the critical process parameters
How can we improve the process?• Screen potential causes• Discover relationships• Establish operating tolerancesWere the improvements effective?• Re-establish capability metric
How can we maintain the improvements?• Implement process control mechanisms • Leverage project learning's• Document & Proceduralize
Focus
YYYY
XVital X
XVital XVital X
Y, Vital X
Y, Vital X
Process Characterization
Process Optimization
Measure
Improve
Ana
lyze
Control
Measure
Improve
Ana
lyze
Control
Measure
Improve
Ana
lyze
Control
Measure
Improve
Ana
lyze
Control
Measure
Ana
lyze
Improvement
Control
A Black Belt has…, and will…
Leadership
Stat
istica
l,
Qualit
y Skil
l
Interpersonal Skill
Driving the UseTh
e new
of S
ix Si
gma
Mentoring
Black Belt TrainingTask
Time on Consulting/Training
Mentoring Related Projects
Green Belt
Utilize Statistical/Quality technique
2%~5%Find one new green belt
2 / year
Black Belt
Lead use of technique and communic-ate new ones
5%~10% Two green belts 4 / year
Master Black Belt
Consulting/Mentoring/Training
80~100% Five Black Belts 10 / year
Barrier Breakthrough Plan
1.00
10.00
100.00
J94
DPM
Op
6 Sigma
SIGMA
J95 J96 J97
6
5.65
5.6
5.5
5.4
5.3
MY95 MY96 MY97 MY98
Pareto, Brainstorming, C&E, BvC8D, 7D, TCS Teams, SPC
DOE, DFM, PCRenewBlack Belt Program (Internal Motorola)
Black Belt Program (External Suppliers)
Proliferation of Master Black Belts
MeasureCharacterize Process
Understand ProcessEvaluate
Improve and Verify ProcessImprove
Maintain New ProcessControl
DefineProblem
UnderstandProcess
CollectData
ProcessPerformance
Process Capability- Cp/Cpk- Run Charts
Understand Problem(Control or Capability)
Data Types- Defectives- Defects- Continuous
Measurement Systems Evaluation (MSE)
Define Process- Process Mapping
Historical Performance
Brainstorm Potential Defect Causes
Defect Statement
Project Goals
Understand the Process and Potential Impact
Measure Phase
ReduceComplaints (int./ext.)
ReduceCost
ReduceDefects
Problem Definitions need to be based onquantitative facts supported by analytical data.
What are the Goals?
What do you want to improve? What is your ‘Y’?
Problem Definition
Baselining: Quantifying the goodness (or badness!) of the current process, before ANY improvements are made, using sample data. The key to baselining is collecting representative sample data
Sampling Plan- Size of Subgroups- Number of Subgroups- Take as many “X” as possible into consideration
Time
How do we know our process?
Process Map
Historical Data
Fishbone
RATIONAL SUBROUPING Allows samples to be taken that include only white noise, within the samples. Black noise
occurs between the samples.
RATIONAL SUBGROUPSMinimize variation within subroups
Maximize variation between subrgoups
TIME
PR
OC
ES
S
RE
SP
ON
SE
WHITE NOISE (Common Cause Variation)
BLACK NOISE (Signal)
Visualizing the Causes
s st + sshift = stotal
Time 1
Time 2Time 3
Time 4
Within Group
• Called s short term (sst)• Our potential – the best
we can be• The s reported by all 6
sigma companies• The trivial many
Visualizing the Causes
Between Groups
s st + sshift = stotal
Time 1
Time 2Time 3
Time 4 • Called sshift (truly a measurement in sigmas of how far the mean has shifted)
• Indicates our process control
• The vital few
Assignable Cause Outside influences Black noise Potentially controllable How the process is actually performing
over time
Fishbone
Common Cause Variation Variation present in every process Not controllable The best the process can be within the
present technology
Data within subgroups (Z.st) will contain only Common Cause Variation
s2Total = s2
Part-Part + s2R&R
Recommendation:
Resolution £10% of tolerance to measureGauge R&R £ 20% of tolerance to measure
• Repeatability (Equipment variation)Variation observed with one measurement device when used several times by one operator while measuring the identical characteristic on the same part.• Reproducibility (Appraised variation)Variation Obtained from different operators using the same device when measuring the identical characteristic on the same part.• Stability or DriftTotal variation in the measurement obtained with a measurement obtained on the same master or reference value when measuring the same characteristic, over an extending time period.
Gauge R&R
Part-PartR&R
To understand where you want to be, you need to know how to get there.v
Map the Process
Measure the Process
Identify the variables - ‘x’
Understand the Problem -’Y’ = function of variables -’x’
Y=f(x)
MeasureCharacterize Process
Understand ProcessEvaluate
Improve and Verify ProcessImprove
Maintain New ProcessControl
In many cases, the data sample can be transformed so that it is approximately normal. For example, square roots, logarithms, and reciprocals often take a positively skewed distribution and convert it to something close to a bell-shaped curve
What do we Need?LSL USL LSL USL
LSL USL
Off-Target, Low VariationHigh Potential DefectsGood Cp but Bad Cpk
On TargetHigh VariationHigh Potential DefectsNo so good Cp and Cpk
On-Target, Low VariationLow Potential DefectsGood Cp and Cpk
Variation reduction and process centering create processes with less potential for defects.
The concept of defect reduction applies to ALL processes (not just manufacturing)
Eliminate “Trivial Many”
Qualitative Evaluation Technical Expertise Graphical Methods Screening Design of Experiments Identify “Vital Few”
Pareto Analysis Hypothesis Testing Regression Design of Experiments
Quantify Opportunity
% Reduction in Variation Cost/ Benefit Our Goal:
Identify the Key Factors (x’s)
Graph>Box plot
75%
50%
25%
Graph>Box plot
Without X values
DBP
Box plots help to see the data distribution
Day
DBP
109
104
99
94
109
104
99
94Operator
DBP
109
104
99
94
Shift
DBP
109
104
99
94
Statistical Analysis
0.0250.0200.0150.0100.0050.000
7
6
5
4
3
2
1
0
New Machine
Freq
uenc
y
0.0250.0200.0150.0100.0050.000
30
20
10
0
Machine 6 mths
Freq
uenc
y
Is the factor really important?
Do we understand the impact for the factor?
Has our improvement made an impact
What is the true impact?
Hypothesis Testing
Regression Analysis
5545352515 5
60
50
40
30
20
10
0
X
Y
R-Sq = 86.0 %Y = 2.19469 + 0.918549X
95% PI
Regression
Regression Plot
Compare
Sample
Means
& Variances
Identify
Relationships
Establish
Limits
Apply statistics to validate actions & improvements
A- Poor Control, Poor ProcessB- Must control the Process better, Technology is fineC- Process control is good, bad Process or technologyD- World Class
A B
C DTECHNOLOGY
1 2 3 4 5 6goodpoor
2.52.01.51.00.5CO
NTRO
L
Zshift
Z St
poor
good
Con-sumer Cue
Technical Requirement
Preliminary Drawing/Database
Identity CTQs
Identify Critical Process
Obtain Data on Similar Process
Calculate Z values
Rev 0 Drawings
Stop Adjust process & design
1st piece inspection
Prepilot Data
Pilot data
Obtain dataRecheck ‘Z’ levels
Stop Fix process & design
Z<3
Z>= Design Intent M.A.I.C
M.A.DSix Sigma Design Process
• #1 Define the customer Cue and technical requirement we need to satisfy
Consumer Cue: Blocks Cannot rattle and must not interfere with box
Technical Requirement: There must be a positive Gap
Reliability (Level)Time to installTotal Electricity Usage Req'd for SystemTime to RepairFloor space occupied NO INPUT IN THIS AREASensor Resolution Response time to power lossVoltagePowerTime to supply Backup Powerbackup power capacity (time)Sensor SensitivityFloor LoadingTime Between maintenanceTime between equipment replacementSafety Index RatingCost of investmentCost of maintenaceCost of installationYears in Mainstream marketCustomer Support RatingDependency on weather conditionsECO-ratingHours of training req'd
Preferred up dwn dwn dwn dwn dwn dwn tgt tgt dwn dwn tgt dwn up up
Engineering Metrics
Customer Requirements Cus
tom
er W
eigh
ts
Relia
bilit
y (L
evel
)
Tim
e to
insta
ll
Tota
l Ele
ctric
ity U
sage
Req
'd fo
r Sys
tem
Tim
e to
Rep
air
Floo
r spa
ce o
ccup
ied
Sens
or R
esol
utio
n
Resp
onse
tim
e to
pow
er lo
ss
Volta
ge
Pow
er
Tim
e to
sup
ply
Back
up P
ower
back
up p
ower
cap
acity
(tim
e)
Sens
or S
ensi
tivity
Floo
r Loa
ding
Tim
e Be
twee
n m
aint
enan
ce
Tim
e be
twee
n eq
uipm
ent r
epla
cem
ent
Safe
ty In
dex
Rat
ing
Cos
t of i
nves
tmen
t
Cos
t of m
aint
enac
e
Cos
t of i
nsta
llatio
n
Year
s in
Mai
nstr
eam
mar
ket
1 Fast Response 92 Long time of backup power supply 93 Low environmental impact 94 Safe to operate 95 Meet power requirements 96 Low investment cost 3 7 Occupies small floor space 38 Easy to upgrade 39 Low upgrading costs 3
10 Low time to implement 311 Cheap to maintain 312 Low recovery or cycle time 313 Long life cycle of the system/component 114 Cheap to operate 115 Cheap to install 116 Long Existing proven technology 1
QFD, F
MEA, R
TY
• #2 Define the target dimensions (New designs) or process mean (existing design) for all mating Parts
Gap Must Be T=.011, LSL=.001 and USL = .021
Gap
(-) (-) (-) (-)(+)
Step #3 • Gather process capability data.• Use actual or similar part data to calculate SS of largest contributors.• May use expert data for minimal contributors• Do not calculate s from current tolerances
Gap Requirements
mT = .010USL = .020 LSL = .001
(-) (-) (-) (-)(+)
mgap= mbox – mcube1 – mcube2 – mcube3 – mcube4
sgap = s2box + s2
cube1 + s2cube2 + s2
cube3 + s2cube4
Short Termmgap= 5.080 – 1.250 – 1.250 – 1.250 – 1.250.016
sgap = (.001)2 + (.001)2 + (.001)2 + (.001)2 + (.001)2 = .00224
Long Termsgap = (.0015)2 + (.0015)2 + (.0015)2 + (.0015)2 + (.0015)2 = .00335
From process:Average sst
Cube 1.250 .001Box 5.080 .001
Zshift = 1.6
MeasureCharacterize Process
Understand ProcessEvaluate
Improve and Verify ProcessImprove
Maintain New ProcessControl
Design of Experiments (DOE) To estimate the effects of independent
Variables on Responses.
Terminology Factor – An independent variable Level – A value for the factor. Response - Outcome
X YPROCESS
THE COFFEE EXAMPLE
Low HighCoffee Brand Maxwell House Chock Full o Nuts Water Spring TapCoffee Amount 1 2
LevelFactor
Main Effects: Effect of each individual factor on response
Bean ‘A’ Bean ‘B’
Tast
e
2.2
3.7
ME
Bean ‘A’Temp ‘X’
Bean ‘B’Temp ‘Y’
Tast
e
Interaction
Concept of Interaction
Why use DoE ? Shift the average of a process.
Reduce the variation.
Shift average and reduce variation
x1 x2
High LowAdhesion Area (cm2) 15 20Type of Glue Acryl UrethanThickness of Foam Styrene Thick ThinThickness of Logo Thick ThinAmount of pressure Short LongPressure application time Small BigPrimer applied Yes No
LevelFactor
Mini Case - NISSAN MOTOR COMPANY
++ +
+ +- -
-
++ -
- +- +
+
-- +
+ +- -
-
-- -
- +- +
+
12345678
9.8
Design ArrayA B C DNo Gluing Str
8.99.28.9
12.313
13.912.6
A - Adhesion Area (cm2)B - Type of GlueC - Thickness of Foam StyreneD - Thickness of Logo
`
A B C D+ 4.60 5.50 5.65 5.58- 6.48 5.58 5.43 5.50
Effect Tabulation
+ - + - + - + -
Gl u
ing
Str e
ngt h
AdhesionArea Type of Glue Thk of Foam
StyreneThk of logo
Factor Effect Plot
4.6
6.5
5.5
5.58 5.65
5.43
5.58
5
1. Define Objective.2. Select the Response (Y)3. Select the factors (Xs)4. Choose the factor levels5. Select the Experimental Design6. Run Experiment and Collect the Data7. Analyze the data8. Conclusions9. Perform a confirmation run.
STEPS IN PLANNING AN EXPERIMENT
MeasureCharacterize Process
Understand ProcessEvaluate
Improve and Verify ProcessImprove
Maintain New ProcessControl
CONTROL PHASE - SIX SIGMA
Control Phase Activities:
- Confirmation of Improvement- Confirmation you solved the practical problem- Benefit validation- Buy into the Control plan- Quality plan implementation- Procedural changes- System changes- Statistical process control implementation- “Mistake-proofing” the process- Closure documentation- Audit process
-Scoping next project
CONTROL PHASE - SIX SIGMA
How to create a Control Plan:
1. Select Causal Variable(s). Proven vital few X(s)2. Define Control Plan - 5Ws for optimal ranges of X(s)3. Validate Control Plan - Observe Y4. Implement/Document Control Plan5. Audit Control Plan6. Monitor Performance Metrics
CONTROL PHASE - SIX SIGMAControl Plan Tools:
1. Basic Six Sigma control methods. - 7M Tools: Affinity diagram, tree diagram, process decision program charts, matrix diagrams, interrelationship diagrams, prioritization matrices, activity network diagram.
2. Statistical Process Control (SPC) - Used with various types of distributions - Control Charts
• Attribute based (np, p, c, u). Variable based (X-R, X)• Additional Variable based tools
-PRE-Control-Common Cause Chart (Exponentially Balanced Moving Average (EWMA))
PRODUCT MANAGEMENT
OVERALL GOAL OF
SOFTWARE
KNOWLEDGE OF COMPETITORS
SUPERVISION
PRODUCT DESIGN
PRODUCT MANAGEMENT
PRODUCT DESIGN
PRODUCT MANAGEMENT
INNOVATION
OUTPUT
DIRECTORY ORGANIZATION
INTUITIVE ANSWERS
SUPPORT
METHODS TO MAKE EASIER FOR USERS
CHARACTERISTICS:• Organizing ideas into meaningful
categories• Data Reduction. Large numbers of qual.
Inputs into major dimensions or categories.
AFFINITY DIAGRAM
MATRIX DIAGRAM
Pat
ient
sch
edul
ed
Atte
ndan
t ass
igne
d
Atte
ndan
t arri
ves
Obt
ains
equ
ipm
ent
Tran
spor
ts p
atie
nt
Pro
vide
The
rapy
Not
ifies
of r
etur
n
Atte
ndan
t ass
igne
d
Atte
ndan
t arri
ves
Pat
ient
retu
rned
Arrive at scheduled time 5 5 5 5 1 5 0 0 0 0 0Arrive with proper equipment 4 2 0 0 5 0 0 0 0 0 0Dressed properly 4 0 0 0 0 0 0 0 0 0 0Delivered via correct mode 2 3 0 0 1 0 0 0 0 0 0Take back to room promptly 4 0 0 0 0 0 0 5 5 5 5
IMPORTANCE SCORE 39 25 25 27 25 0 20 20 20 20RANK 1 3 3 2 3 7 6 6 6 6
5 = high importance, 3 = average importance, 1 = low importance
HOWS
WHATS
RELATIONSHIP MATRIX
CUSTOMER IMPORTANCE MATRIX
Add
feat
ures
Mak
e ex
istin
g pr
oduc
t fas
ter
Mak
e ex
istin
g pr
oduc
t eas
ier t
o us
e
Leav
e as
-is a
nd lo
wer
pric
e
Dev
ote
reso
urce
s to
new
pro
duct
s
Incr
ease
tech
nica
l sup
port
budg
et
Out
arro
ws
In a
rrow
s
Tota
l arro
ws
Stre
ngth
Add features 5 0 5 45Make existing product faster 2 1 3 27Make existing product easier to use 1 2 3 21Leave as-is and lower price 0 3 3 21Devote resources to new products 1 1 2 18Increase technical support budget 0 2 2 18
(9) = Strong Influence
(3) = Some Influence
(1) = Weak/possible influence
Means row leads to column item
Means column leads to row item
COMBINATION ID/MATRIX DIAGRAM
CHARACTERISTICS:• Uncover patterns in
cause and effect relationships.
• Most detailed level in tree diagram. Impact on one another evaluated.
CONTROL PHASE - SIX SIGMAControl Plan Tools:
1. Basic Six Sigma control methods. - 7M Tools: Affinity diagram, tree diagram, process decision program charts, matrix diagrams, interrelationship diagrams, prioritization matrices, activity network diagram.
2. Statistical Process Control (SPC) - Used with various types of distributions - Control Charts
• Attribute based (np, p, c, u). Variable based (X-R, X)• Additional Variable based tools
-PRE-Control-Common Cause Chart (Exponentially Balanced Moving Average (EWMA))
How do we select the correct Control Chart:
Type Data
Ind. Meas. or subgroups
Normally dist. data
Interest in sudden mean changes
Graph defects of defectives
Oport. Area constant from sample to sample
X, Rm
p, np
X - RMA, EWMA or CUSUM and Rm
u
C, u
Size of the subgroup constant
p
If mean is big, X and R are effective too
Ir neither n nor p are small: X - R, X - Rm are effective
More efective to detect gradual changes in long term
Use X - R chart with modified rules
VariablesAttributes
Measurement of subgroups
Individuals
Yes
No No
Yes
Yes
No
Yes
No
Defects Defectives
Additional Variable based tools:1. PRE-Control
• Algorithm for control based on tolerances• Assumes production process with measurable/adjustable
quality characteristic that varies.• Not equivalent to SPC. Process known to be capable of
meeting tolerance and assures that it does so.• SPC used always before PRE-Control is applied.• Process qualified by taking consecutive samples of individual
measurements, until 5 in a row fall in central zone, before 2 fall in cautionary. Action taken if 2 samples are in Cau. zone.
• Color coded
YELLOW ZONE
GREEN ZONE
YELLOW ZONE
RED ZONE
RED ZONE
1/4 TOL. 1/2 TOL. 1/4 TOL.
Low
Tole
ran
ce L
imt
Tole
ran
ce L
imt
High
Refe
renc
e Lin
e
PRE-
Cont
rol
DIM
ENSI
ONNO
MIN
AL
Refe
renc
e Lin
e
PRE-
Cont
rol
2. Common Causes Chart (EWMA).• Mean of automated manufacturing processes drifts because of
inherent process factor. SPC consideres process static.• Drift produced by common causes.• Implement a “Common Cause Chart”.• No control limits. Action limits are placed on chart.
• Computed based on costs• Violating action limit does not result in search for special
cause. Action taken to bring process closer to target value.
• Process mean tracked by EWMA • Benefits:
• Used when process has inherent drift• Provide forecast of where next process measurement will be.• Used to develop procedures for dynamic process control
• Equation: EWMA = y^t + s (yt - y^t) s between 0 and 1
EWMA chart of sand temperature
0
50
100
150
1 4 7
10 13 16 19 22 25 28
Observations
Degr
ees Sand
TemperatureEWMA
Sand Temperature EWMA Error
125 125.00 0.00123 125.00 -2.00118 123.20 -5.20116 118.52 -2.52108 116.25 -8.25112 108.83 3.17101 111.68 -10.68100 102.07 -2.0792 100.21 -8.21
102 98.22 3.78111 101.62 9.38107 110.60 -3.60112 107.30 4.70112 111.53 0.47122 111.95 10.05140 121.00 19.00125 138.00 -13.00130 126.31 3.69136 129.63 6.37130 135.36 -5.36112 130.54 -18.54115 113.85 1.15100 114.89 -14.89113 101.49 11.51111 111.85 -0.85
Project Closure
• Improvement fully implemented and process re-baselined.• Quality Plan and control procedures institutionalized.• Owners of the process: Fully trained and running the process.• Any required documentation done.• History binder completed. Closure cover sheet signed.• Score card developed on characteristics improved and reporting
method defined.
The Cost of Quality The cost of quality is
the cost of conformance or delivering products that meet requirements and fitness for use
the cost of nonconformance or taking responsibility for failures or not meeting quality expectations
Five Cost Categories Related to Quality
Prevention cost: the cost of planning and executing a project so it is error-free or within an acceptable error range
Appraisal cost: the cost of evaluating processes and their outputs to ensure quality
Internal failure cost: cost incurred to correct an identified defect before the customer receives the product
External failure cost: cost that relates to all errors not detected and corrected before delivery to the customer
Measurement and test equipment costs: capital cost of equipment used to perform prevention and appraisal activities
Maturity Models Maturity models are frameworks for
helping organization improve their processes and systems Software Quality Function Deployment
model focuses on defining user requirements and planning software projects
The Software Engineering Institute’s Capability Maturity Model provides a generic path to process improvement for software development
Several groups are working on project management maturity models, such as PMI’s Organizational Project Management Maturity Model (OPM3)
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