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8/22/2019 07 CM0471 Module 7
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Six Sigma Black Belt
Cert. Prep. Course:ImproveImproveImproveImproveModule VII
©2009 ASQ 2
Agenda
This module consists of seven lessons:
1. Design of experiments (DOE)
2. Waste elimination3. Cycle-time reduction
4. Kaizen and kaizen blitz5. Theory of constraints (TOC)
6. Implementation
7. Risk analysis and mitigation
©2009 ASQ 3
Lesson 1 – Measuring and ModelingRelationships Between VariablesVII. A.1 Terminology
Define basic DOE terms, including independent anddependent variables, factors and levels, response,
treatment, error, etc. (Understand)
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©2009 ASQ 4
DOE Terminology
• Certain terms used with Design of Experiments
need to be defined clearly.
– Independent Variable
• A variable that can contribute to theexplanation of the outcome of an experiment.
• This is also known as a “predictor variable.”
– Dependent Variable
• A variable representing the outcome of anexperiment or response.
• The response is often referred to as the
“output variable” or “response variable.”
©2009 ASQ 5
– Factors
• A predictor (or independent) variable that isvaried with the intent of assessing its effect
on a response (or dependent) variable.
• Most often referred to as an “input variable.”
• A factor may be quantitative such as atemperature in degrees F or cycle time
in pieces per minute.• A factor may also be qualitative, such as a
different production machine or a differentmold.
DOE Terminology
©2009 ASQ 6
– Levels
• A specific setting for a factor.
• In DOE, levels are frequently set as high and lowfor each factor.
• A level is a potential setting, value, or assignment
of a factor of the value of the predictor variable.
• If the experiment is to be performed at twodifferent temperatures, then the “factor”
temperature has two levels.
• For example, if the factor is time, then thelow level may be 50 minutes, and the high
level may be 70 minutes.
DOE Terminology
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©2009 ASQ 7
– Response
• The output(s) of a process. Sometimes calleddependent variable(s).
• It shows the observed result or value of anexperimental treatment.
– Treatment
• The specific setting of factor levels for anexperimental unit.
• For example, a level of temperature at 65°Cand a level of time at 45 minutes describe atreatment as it relates to an output of yield.
DOE Terminology
©2009 ASQ 8
– Experimental Run• Is a single performance of the experiment for
a specific set of treatment combinations
• n = Number of experimental runs that will beconducted in a given experiment
• If all of the factors have the same number oflevels:
– n = Lf
– n = number of runs – L = number of levels – f = number of factors
• If the factors have different numbers of levels: – n = (L1)(L2)(L3) … (Lf)
DOE Terminology
©2009 ASQ 9
– Error
• An error from an experiment reveals variationin the outcome of identical tests.
• It is the unexplained variation in a collectionof observations.
• An error is variation in the response variablebeyond that accounted for by the factors orother assignable sources while conducting
an experiment.
• Many times referred to as “experimentalerror.”
DOE Terminology
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©2009 ASQ 10
Progress Check
Match each DOE term to the appropriate definition
The output of a process.Factors
Unexplained variation in the processTreatment
Process inputs an investigator
manipulates to cause a changein the output
Response
A specific combination of factor levelsError
DefinitionTerm
©2009 ASQ 12
Progress Check
Which of the descriptions in the table below is anexample of DOE terminology?
©2009 ASQ 14
Define and apply DOE principles, including power andsample size, balance, repetition, replication, order,efficiency, randomization, blocking, interaction,
confounding, resolution, etc. (Apply)
VII.A.2 Design principles
Lesson 1 – Measuring and ModelingRelationships Between Variables
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©2009 ASQ 15
Design Principles
Sample not rejected Sample rejected
Sample should not be rejected
CorrectDecision
Type II orconsumer’s risk
β = P(Type II)
Sample should be rejected
Type I orproducer’s risk
α = P(Type I)
Correct
Decision
Action Taken
State of Nature
α is the risk of finding a difference when there really isn’t one
β is the risk of not finding a difference when there really is one.
Power and Sample Size: This was covered in the Analyze PhaseAs the power of the test increases, the probability of a Type II error β decreases
©2009 ASQ 16
Design Principles
Replication:Performing the entire
experiment more thanonce for a given set of
independent variables
Speed Pressure MPG
fast High 23.0
slow Low 23.5
slow High 25.0
fast Low 22.0
fast High 23.4
slow Low 23.8
slow High 25.5
fast Low 22.5
fast High 22.8
slow Low 23.0
slow High 24.8
fast Low 21.7
Replication is good for producing enoughdata to investigate error of the experiment.
Replication
©2009 ASQ 17
Repeated Measures is also known as Repetition:Is a repeat of the measurements taken for the samerun
Repeat Measures help determine the inherentvariability in the measurement system
Design Principles
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©2009 ASQ 18
Confounding:
• When factors or interactions are not distinguishablefrom one another
• Two or more effects that cannot beunambiguously attributed to a single factor or interaction. This isalso referred to as aliasing.
• Confounding indicates that the value of a maineffect comes from both the main effect itself andalso from interactions.
Design Principles
©2009 ASQ 19
Order:
• Refers to the sequence in which the runs in theexperiment will be conducted
Run Order: Typically a random order inwhich the experiment was run.
Standard Order: In a standard order, thefirst factor alternates between the low andhigh setting for each run. The second factor
alternates between low and high settings.
Design Principles
©2009 ASQ 20
Randomization:
• A schedule for conducting treatment combinations ina DOE such that the conditions in one run neither
depend on the conditions of the previous run norpredict the conditions in the subsequent runs.
• Randomization in an experiment reduces the effects
of factors (variables) outside the experiment. Whenoutside factors (called “noise”) affect an experiment,we may draw false conclusions regarding the factorsin the experiment.
Design Principles
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©2009 ASQ 21
Blocking:
Blocking allows unwantedeffects to be separated from
desired effects!
• Is a group ofhomogeneousexperimental units.
• Variation betweenblocks should begreater than variation
within blocks.In this example, the twoblocks might represent Day 1and Day 2. Note: each factor
level appearstwice withineach block.
Blocks Speed Pressure Temp
2 slow high cold
2 slow low hot
2 fast high hot
2 fast low cold
1 fast high cold
1 slow high hot
1 fast low hot
1 slow low cold
See CSSBB HB, page 299
Design Principles
©2009 ASQ 22
T = 50 T = 70 P = 1 P = 3
50 1 45 45 45
70 1 66 66 66
50 3 29 29 29
70 3 88 88 88
50 1 43 43 43
70 1 63 63 63
50 3 32 32 32
70 3 91 91 91
37.3 77.0 54.3 60.0Factor Level Mean Response or Effect >>
Factor Level
Temperature Pressure
Yield
(Response)
7050
80
70
60
50
40
31
Temperature
M e a n
Pressure
Main Effects Plot for YieldData Means
An effect is the differencebetween two means
See CSSBBHB, page 300
Main Effects
Design Principles
©2009 ASQ 23
50 1 45 45
70 1 66 66
50 3 29 29
70 3 88 88
50 1 43 43
70 1 63 63
50 3 32 32
70 3 91 91
44.0 30.5 64.5 89.5
T = 70 andP =3
Mean Response >>
Temperature PressureYield
(Response)T = 50 and
P = 1T = 50 and
P = 3T = 70 and
P =1
31
90
80
70
60
50
40
30
Pressure
M e a n
50
70
Temperature
InteractionPlotfor YieldData Means
31
90
75
60
45
30
7050
90
75
60
45
30
Temperature
Pressure
50
70
Temperature
1
3
Pressure
InteractionPlotfor YieldData Means
See CSSBB HB, page 303
Interactions
Design Principles
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©2009 ASQ 24
Graphical representations of possibilities
Magnitude of effect
( ) ( ) above)figure(See ] ∆- ∆[2
1 =Effect(X x Y)
scoordinateCartesianarepairs XY thewhere
)] Y X - Y (X -) Y X - Y [(X 2
1 =Effect(X x Y)
21
LOLOLOHIHILOHIHI
∆∆∆∆
1∆∆∆∆
2
Strength of Interaction
Design Principles
©2009 ASQ 25
1-1
5
4
3
2
1
Y
M e a n
-1
1
X
Interaction Plot for RESPONSEData Means
Effect = [(4 – 2 ) – (5 – 1)] /2 = -1
1-1
5
4
3
2
1
Y
M e a n
-1
1
X
Interaction Plot for RESPONSEData Means
Effect = [(2 – 4 ) – (5 – 1)] /2 = -3
This value is negative!
When the Lines Cross!
Design Principles
©2009 ASQ 26
Balanced designs are mucheasier to analyze!
Balanced Design
Balanced Design:Each experimental level for any one factor isrepeated the same number of times for all possible
combinations involving the levels of the other factors.Balanced Design
Run Order Speed Pres sure
1 Slow Low
2 Slow High
3 Fast Low
4 Fast High
Unbalanced DesignRun Order Speed Pressure
1 Slow Low
2 Slow High
3 Slow Low
4 Fast High
See CSSBB HB, Page 306
Design Principles
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©2009 ASQ 27
Fraction Runs Resolution
Full 64 Full 26
½ 32 VI 2(6-1)
¼ 16 IV 2(6-2)
1/8 8 III 2(6-3)
Designation
Resolution:
• It is the level of confounding in a fractional
factorial design.
• They are numbered using Roman Numerals
Design Principles
©2009 ASQ 28
Resolution III Designs:Designs in which no main effects are aliased with any othermain effect…
...But main effects are aliased with two-factor interactions,and 2-factor interactions may be aliased with each other.
Resolution IV Designs:Designs in which no main effect is aliased with any other maineffect or with any two-factor interaction…
…But two-factor interactions are aliased with other
interactions.
Resolution V and above Designs:Designs in which no main effect or two-factor interactions arealiased with any other main effect or two-factor interaction…
…But two-factor interactions are aliased with three-factorinteractions.
Design Principles
©2009 ASQ 29
A B C AB AC BC ABC
1 -1 1 -1 1 -1 -1
1 -1 -1 -1 -1 1 1
-1 -1 -1 1 1 1 -1
1 1 -1 1 -1 -1 -1
-1 1 -1 -1 1 -1 1
-1 -1 1 1 -1 -1 1
1 1 1 1 1 1 1
-1 1 1 -1 -1 1 -1
23 Factorial Matrix Let ABC = D
D is said to be confounded or aliased with the ABC interaction
AB = AxB ABC = AxBxC
First, we will use a typical three-factor design and use thethree-way higher interaction to substitute for the fourth factor:
Resolution IV Designs
Design Principles
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©2009 ASQ 30
I
Generator Generator Generator Generator
I=ABCD
A=BCD
B=ACD
C=ABD
D=ABC
AB=CD
AC=BD
AD=BC
A B C AB AC BC D (ABC) AD BD CD ABD ACD BCD ABCD1 -1 1 -1 1 -1 -1 -1 1 -1 1 -1 1 1
1 -1 -1 -1 -1 1 1 1 -1 -1 -1 -1 1 1
-1 -1 -1 1 1 1 -1 1 1 1 -1 -1 -1 1
1 1 -1 1 -1 -1 -1 -1 -1 1 -1 1 1 1
-1 1 -1 -1 1 -1 1 -1 1 -1 -1 1 -1 1
-1 -1 1 1 -1 -1 1 -1 -1 1 1 -1 -1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1
-1 1 1 -1 -1 1 -1 1 -1 -1 1 1 -1 1
Minitab Alias Structure
I + ABCD A + BCDB + ACDC + ABD
D + ABC AB + CD AC + BD AD + BC
A=I*A=A2BCD=BCD
AB=I*AB=A2B2CD=CD
Expanding the table...
Expanded Resolution IV Designs
Design Principles
©2009 ASQ 31
Progress CheckWhich of the descriptions in the table below are examplesof main effects or interactions? Check the appropriate boxat the right.
Description/Name ExampleNon-
Example
A two-factor DOE with two interactions.
A three-factor DOE with A x B, A x C, B x C, and A x B xC interactions.
A four-factor DOE with 2 x 4 = 8 main effects plots
A five-factor DOE with a highest order interaction ofA x B x C x D
A DOE with factors A, B, and C and four main effectsplots
A DOE with factors A and B and only an A x B interaction
Completing an analysis of variance to plot four-way
interactions
©2009 ASQ 33
Progress Check
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©2009 ASQ 34
Plan, organize, and evaluate experiments bydetermining the objective, selecting factors,responses and measurement methods, choosingthe appropriate design, etc. (Evaluate)
VII.A.3 Planning experiments
Lesson 1 – Measuring and ModelingRelationships Between Variables
©2009 ASQ 35
Planning Experiments
Establish the Objective:
What do you want to discover by conducting the experiment?For example:
•Are you trying to establish the relationship between theinput factors (Xs) and the output (response-Y)?
•Are you trying to d istinguish the vital few Xs from thetrivial many (possible factors)?
•Are you interested in knowing if several input factors acttogether to influence the output (Y)?
•Are you trying to determine the optimal settings of theinput factors?
©2009 ASQ 36
Usually stated in terms of the effects of inputson outputs
Typical experimental objectives are to determine the...
• Effects of material variation on product reliability
• Sources of variation in a critical process
• Effects of less-expensive materials on product performance
• Impact of operator variation on the product
• Cause-effect relationships between process inputs andproduct characteristics
• Equation which models your process
• Alternate method that produces the best output
Planning Experiments
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©2009 ASQ 37
A factor is one of the controlled or uncontrolled
inputs to a process whose influence upon one ormore responses is being studied in the experiment.
• Quantitative factors are variables data(temperature in degrees, time in seconds)
• Qualitative factors are attribute data(different machines, different operators,clean or not clean, algorithm A or B)
Planning Experiments
©2009 ASQ 38
The levels of an input factor are the values of the input factor(X) being examined in the experiment (not to be confused with the output (Y).
• Quantitative factors (continuous data):If an experiment is to be conducted using two d ifferent
speeds, then the factor speed has two levels.
• Qualitative factors (attributes data):If an experiment is to be conducted using location(Seattle and Reno), then the factor location has twolevels.
Planning Experiments
©2009 ASQ 39
A difference
10
20
30
40
50
60
70
80
0 100 200 300 400 500 600
B difference
B - l o w
A - l o w
A - h i g h
B - h i g h
R e s p o n s e V a r i a b l e ( Y )
Factor Settings
Lo(-)
Hi(+) Factor Settings
Experimental Effect
True EffectY
Lo(-)
Hi(+)
Factor Settings
Experimental Effect
True EffectY
Setting Factor Levels
Planning Experiments
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©2009 ASQ 40
Higher Levels of Experimentation Increase Knowledge of the Process
Selecting the Type of Experiment Design
• Response Surface Methods
• Full Factorials with Replication
• Full Factorials with Repetition
• Full Factorials without Replicationor Repetition
• Screening or Fractional Designs
• OFAT (One Factor At a Time)
Optimize
Characterize
Screen
Planning Experiments
©2009 ASQ 41
• Noise factors
• Confounding
• Blocking
• Randomization
• Cost
• Time etc…
• Different Operators
• Different Suppliers
• Different Shifts
• Machines
• Speed
• Form type
• Training
• Tool version
ProcessControllableInputs(KPIV)
Key ProcessOutputs(KPOV)
Noise Inputs(Continuous)
Noise Inputs(Discrete)
Different design considerations:
Planning Experiments
©2009 ASQ 42
Progress Check
Scenario:
• Your team is entering a bowling competition
• Your team wants to set up a DOE to win thecompetition
Task: as a group plan an experiment:
• Discuss the Experimental Objective
• Factors and levels• Appropriate design• Different design considerations – noise,
confounding, blocking, randomization,
resolution, cost, and time
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©2009 ASQ 44
Design and conduct completely randomized,randomized block, and Latin square designsand evaluate their results. (Evaluate)
See CSSBB HB, pages311 to 319
VII.A.4 One-factor experiments
Lesson 1 – Measuring and ModelingRelationships Between Variables
©2009 ASQ 45
One-Factor Experiments
• As the name implies, one-factor experimentsinvolve only one factor or input variable.
• In a one-factor experiment, the project team selects
a starting point or baseline set of levels for the factor.
• This is a single-factor analysis of varianceexperiment (one-way ANOVA) – covered in Analyze
module
See CSSBB HB, example 25.12,pages 256 to 258,
©2009 ASQ 46
Randomized Complete Block Design (RCBD):
• A completely randomized block design is usedwhen we want to make experimental error as smallas possible.
• We want to remove the variability between a factorthat is not in question from the factor being tested.
One-Factor Experiments
See CSSBB HB, example 28.3, pages299 to 305,
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©2009 ASQ 47
For example: In testing a tip for hardness on a hardness testingmachine, a metal coupon is used that provides a measurement ofdepth made by pressing the tip into the coupon.
• If we decide to obtain four observations for each tip, a completelyrandomized design would consist of randomly assigning each ofthe 4 x 4 = 16 runs to an experimental unit – that is, a metal coupon.
• Thus. 16 different metal coupons would be required – one for eachrun in the design.
Because we are only interested in the
hardness of the tip, we must removethe variability that may be inherent inthe coupon.To do this, we would test each tip onetime on the same coupon, thusreducing possible experimental error.
One-Factor Experiments
©2009 ASQ 48
Latin Square:
• A Latin square design is often used to reduce theimpact of two blocking factors by balancing out
their contributions.
• A basic assumption is that these block factors donot interact with the factor of interest or with each
other.
One-Factor Experiments
See CSSBB HB, pages 311 to 319
©2009 ASQ 49
Design, analyze, and interpret these types ofexperiments, and describe how confounding affectstheir use. (Evaluate)
VII.A.5 Two-level fractional factorial experiments
Lesson 1 – Measuring and ModelingRelationships Between Variables
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©2009 ASQ 50
Two-Level Fractional FactorialExperimentsFractional factorial designs:
• If we have a six-factor, two-level experiment, we would need64 runs required for a Full Factorial Experiment, which maynot be desirable.
• Thus, when there is a need to investigate only the main
effects and low-order interactions, we can study the problemwith far fewer than 2k runs.
• We accomplish this by performing a “fraction” of theruns required by the full factorial experiment.
See CSSBB HB, pages 319 to 325
©2009 ASQ 51
Two-Level Fractional FactorialExperiments
©2009 ASQ 52
Two-Level Fractional FactorialExperiments
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©2009 ASQ 53
Design, conduct, and analyze full factorialexperiments. (Evaluate)
VII.A.6 Full factorial experiments
Lesson 1 – Measuring and ModelingRelationships Between Variables
©2009 ASQ 54
Full Factorial Experiments
• A full factorial design tests all combinations of thefactor levels. The number of runs will be 2k wherek is the number of factors.
• Full factorial designs are typically used in thecharacterization phase of experimentation.
©2009 ASQ 55
Progress CheckMatch each Design to the appropriate Statement
A four-factor, two-levelexperiment has 16 runs
Latin Square
We want to remove thevariability between a factorthat is not in question fromthe factor being tested
Fractional Factorial Design
Cannot be used whenestimation of interactionis required
Randomized CompleteBlock Design
Confounding takes placeFull Factorial Design
StatementDesign
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©2009 ASQ 57
Lesson 2 – Waste Elimination
Select and apply tools and techniques for eliminatingor preventing waste, including pull systems, kanban,
5S, standard work, poka-yoke, etc. (Analyze)
VII.B Waste elimination
©2009 ASQ 58
Waste Elimination
Eight categories of Muda
1. Overproduction above demand
2. Waiting for processing, use, work
3. Transport of products/materials
4. Over-processing
5. Inventory
6. Unnecessary motion7. Defective parts/products
8. Underutilized people
Waste elimination is a goal of Lean and Six Sigma.
©2009 ASQ 59
Kanban:
• Another tool used to create a lean enterprise isa kanban system. Kanban is a system used to
minimize WIP (work in progress).
• This lean tool creates a pull system, which places a
cap on WIP, while maintaining focus on minimizing
the process lead-time.
• A Kanban can be a mechanical or electronic
mechanism to establish a “sign” for the release of
material and a “visible” record.
Waste Elimination
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©2009 ASQ 60
• Kanban is a Japanese term: kan meaning “card,”
ban meaning “signal.”
• The kanban system works by signaling the need to
replenish stock or materials, or to produce more ofan item.
• Kanban can be done using cards as the signaling
component.
• In a simple kanban system, an empty box, container,or pallet can signal the need for more supplies.
• The supplier or warehouse should only deliver
components to the production line when signaled.
Waste Elimination
©2009 ASQ 61
5S:
• Japanese originally, 5S stands for five “s” words.
• The 5S method assists in the organization of theworkplace and standardization of work procedures.
• Sorting (Seiri) – Keep only what is necessary in
the work area.
• Storage/Set in Order (Seiton) – Organize the way
necessary items are kept, making it easier to findand utilize.
Waste Elimination
©2009 ASQ 62
5S (continued)
• Shining (Seiso) – Cleanliness of the work environmentand the equipment to facilitate a quality process andproduct.
• Standardizing (Seiketsu) – Tasks, procedures,schedules, and the persons responsible for helpingkeep the workplace in a clean and organized mannerare parts of the control plan for the business unit ordepartment.
• Sustaining (Shitsuke) – Indoctrinate the practice of 5Sinto your organizatio’'s culture until it becomes part ofyour standard operating procedures.
• Note: Several companies have revised the Japaneseterm, 5S, to 6S. The sixth “S” is for safety.
Waste Elimination
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©2009 ASQ 63
Standard Work:
• Identification and agreement on the optimal way to performeach task/step in a process becomes the standard operatingprocedure or standard work procedure.
• Standard work contributes to process control by minimizing thevariation in the product flowing through the process. There arethree basic elements involved:
– Takt time – matches the time to deliver a service, producea part or finished product to the pace of sales, and is thebasis for allocating work among workers.
– Standard in-process inventory – the minimum number ofitems or parts, including units in machines, required tokeep a cell or process moving.
– Sequence – the order in which associates perform tasks atvarious processes.
Waste Elimination
©2009 ASQ 6464
Takt Time
TaktTime Volume (Daily demand requirement)
Time (Available time per working day)=
Sets pace of work tomatch customer demand
©2009 ASQ 65
Poka-yoke:
• Poka-yoke is a Japanese term that means “to avoid
inadvertent errors.”
• “Poka-yoke” is often referred to as “mistake-proofing.”
• A poka-yoke device is one that prevents incorrectparts from being made or assembled, or easilyidentifies a flaw or error, and helps to eliminatevariations in process.
• Example: A financial institution’s loan bookingsystem requires all data entry fields on a screen tobe populated before allowing the associate to move
to the next screen, preventing an incomplete accountset-up.
Waste Elimination
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©2009 ASQ 66
Progress Check
1. In a pull system, who is doing the pulling?
a. Customerb. Cycle time
c. Takt time
2. Identify the type of Non-Value-Added activitywhich would directly ensue as a result of poor
quality:
a. Reworking a product
b. Unnecessary motion
c. Transportation waste
©2009 ASQ 67
Lesson 3 – Cycle-Time Reduction
VII.C Cycle-time reduction
Use various tools and techniques for reducing cycletime, including continuous flow, single-minute
exchange of die (SMED), etc. (Analyze)
©2009 ASQ 6868
Cycle-Time Reduction
“One of the most noteworthy accomplishments in keeping theprice of products low is the gradual shortening of the cycle
time. The longer an article is in the process and the more it ismoved about, the greater is its ultimate cost.”
Henry Ford, 1926
• Time that elapses from beginning to end of process
• Ultimate objective or goal of Lean processes is to reduce cycletime by eliminating waste
Work Errors, waiting, transportation, movement etc…..
Total Cycle Time
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©2009 ASQ 69
• Cycle time is the amount of time needed to
complete a single task or activity for the productor service.
• Cycle times may vary by task; therefore, it isbeneficial to show a range and average on thevalue stream map.
• If cycle-time variation can be reduced, the processbecomes more predictable.
Cycle-Time Reduction
©2009 ASQ 70
• Often, cycle-time can be reduced by breaking down asingle task and analyzing the amount of time that it takesto complete each sub-activity of that task.
• Analyze task processing time
– Compare task processing times to Takt time – look
for steps with work time closest to or longer thanTakt time
– Analyze resources required to meet time constraints
• After this breakdown, it is easier to tell which sub-activitiesmay be contributing to a slower cycle time.
• Ultimately, non-value-added activities can be eliminated,and value-added activities can be performed more quickly
and efficiently.
Cycle-Time Reduction
©2009 ASQ 71
Progress Check
a. To please the customerb. To reduce internal wastec. To create a bottleneck
d. To increase capacity
e. To remain competitive
The reduction of cycle time is undertaken for which
of the following principle reasons? Identify all thatapply.
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©2009 ASQ 72
Lesson 4 – Kaizen and Kaizen Blitz
Define and distinguish between these two methodsand apply them in various situations. (Apply)
VII.D Kaizen and kaizen blitz
©2009 ASQ 73
Kaizen and Kaizen Blitz
“Means and methods to approach the target throughcycles of continuous improvement” (Kaizen)
Masaaki Imai: “Kaizen means gradual, unending
improvement, doing ‘little things’ better; setting andachieving ever-higher standards.”
©2009 ASQ 74
• Kaizen is a Japanese term that is translated tomean “continuous improvement.”
• Many companies have successfully used workshops
called kaizen “events” or “blitzes” to drive dramaticimprovements in cycle times, inventory levels,changeover times, and overall quality.
• Successful kaizen workshops require three keycomponents:
– Selecting the right project and boundaries
– Empowering the proper team
– Planning for follow-up
Kaizen and Kaizen Blitz
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©2009 ASQ 75
Progress Check
The key elements of kaizen are…… Check all that apply.a. Qualityb. Involvement of all employeesc. Poor morale
d. Willingness to changee. Indiscipline
f. Communication
©2009 ASQ 76
Lesson 5 – Theory of Constraints(TOC)
VII.E Theory of constraints (TOC)
Define and describe this concept and its uses.(Understand)
©2009 ASQ 77
Theory of Constraints (TOC)
• As opposed to variation reduction (Six Sigma) orwaste removal (Lean), theory of constraints (TOC)focuses on system improvement by first payingattention to the “weakest link” of the system.
• A constraint is anything that limits an organizationfrom moving toward its goal.
• A constraint can be physical and internal (such asa machine, facility, or policy) or non-physical andexternal (such as market conditions or demand fora product).
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©2009 ASQ 78
• The theory of constraints was created by Dr. Eliayhu Goldratt,
and is based on the assumption that every system has atleast one constraint limiting it from getting more of what itstrives for.
• If this were not true, then the system would produce infiniteoutput.
• The theory of constraints is both descriptive and prescriptive innature; it not only describes why system constraints happen,but also offers guidance on what to do about them.
• When applying TOC, a five-step progressive approach is usedto ensure a focus is placed on system-level improvements,
rather than efforts that would only sub-optimize systemcomponents.
Theory of Constraints (TOC)
©2009 ASQ 79
The Five Focusing Steps of the Theory of Constraints show howto achieve ongoing improvement by addressing theseconstraints in a continuous fashion:
1. IDENTIFY – the system’s constraint
2. EXPLOIT – use kaizen or other methods to improve the rateof the constraining process
3. SUBORDINATE – the rates of everything else to match thatof the constraining process
4. ELEVATE – the system’s constraint. Could mean training,additional equipment, or new technology
5. REPEAT – the systems rate can be further improved byrepeating these steps with a new constraint
Theory of Constraints (TOC)
©2009 ASQ 80
Drum-Buffer-Rope:
• It is named after the 3 essential elements of the solution
• The drum or constraint or weakest link,
• The buffer or material release duration,
• The rope or release timing.
• The aim of the solution is to protect the weakest link in the system,and therefore the system as a whole, against process dependency
and variation and thus maximize the systems’ overalleffectiveness.
• The outcome is a robust and dependable process that will allow usto produce more, with less inventory, less rework/defects, andbetter on-time delivery.
Theory of Constraints (TOC)
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©2009 ASQ 81
TOC defines three operational measures to determine whether an
organization is moving toward its goal: – Increased throughput (selling price - cost of raw materials)
– Decreased inventory
– Decreased operating expenses
The following four measurements are used to identify results for theoverall organization:
– Net profit (NP) = throughput - operating expense (T-OE)
– Return on investment = net profit / inventory (NP/I)
– Productivity = throughput / operating expense (T/OE)
– Turnover = throughput / inventory (T/I)
Theory of Constraints (TOC)
©2009 ASQ 82
Progress Check
The goals of TOC are…… Check all that apply.a. Increased throughputb. Reduced inventoryc. Increased operational expenses
d. Reduced demand
©2009 ASQ 83
Lesson 6 – Implementation
VII. F Implementation
Develop plans for implementing the improvedprocess (i.e., conduct pilot tests, simulations, etc.),
and evaluate results to select the optimum solution.(Evaluate)
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©2009 ASQ 84
Implementation
Pilot run:
• It is a short run terminated by elapsed time or quantity produced
• Helps the team identify what went well and what was overlooked
Simulation:
• Usually performed with computer software
• Simulation allows one to implement solutions off-line anddetermine conclusions on how the process improvementwill operate when brought on-line
Model:
• Provides a 3-D version of what is proposed, and is an effectivevisual communication
Prototype:
• Representation of the actual product in terms of form, fit, and function
©2009 ASQ 85
Implementation Strategy Considerations:
• Infrastructure
– Includes everything to ensure successfulcompletion
• Communication Plan
– Who, what, when, where, how, and to whom it iscommunicated
• Resources – Time, people, money, energy, etc.
• Management Commitment
– This is very important. Without it, a project will notbe successful.
Implementation
©2009 ASQ 86
Progress Check
When creating a communication plan, consideration
is given to the following …
Check all that apply
a. Goals and desired results of the programb. Overall communications objectives, such as
reinforcing customer service, loyalty, etc.
c. Is sent only to the President of the organizationd. Audience you want to influencee. Methods in your plan that you can use to
measure and evaluate the program’s resultsf. Media for communications
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©2009 ASQ 87
Lesson 7 – Risk Analysis andMitigation
VII.G Risk analysis and mitigationUse tools such as feasibility studies, SWOT analysis(strengths, weaknesses, opportunities, and threats),PEST analysis (political, environmental, social, and
technological), and consequential metrics to analyze
and mitigate risk. (Apply)
©2009 ASQ 88
Risk Analysis and Mitigation
Feasibility studies:
• A feasibility study is an analysis of the viability ofan idea. The feasibility study focuses on helping
answer the essential question of “should weproceed with the proposed project idea?”
• All activities of the study are directed toward helping
answer this question.
• The feasibility study will be a major informationsource in making a go/no-go decision.
• This indicates the importance of a properlydeveloped feasibility study.
©2009 ASQ 89
Risk analysis:
• Is a technique to identify and assess factors thatmay jeopardize the success of a project from
achieving a goal.
• The outcome of the risk analysis would be the
creation of a risk register to identify and quantify risk
elements to the project and their potential impact.
• This technique also helps to define preventive
measures to reduce the risk.
Risk Analysis and Mitigation
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©2009 ASQ 90
The Strengths, Weaknesses, Opportunities, and
Threats (SWOT) analysis provides a framework toidentify elements that help or hinder an organization.
Risk Analysis and Mitigation
©2009 ASQ 91
• PEST analysis stands for “Political, Economic,
Social, and Technological analysis”
• Describes a framework of macro-environmentalfactors used in the strategic planning process
• Political Factors:
– Will government policy influence laws that
regulate or tax your business? – Is the government involved in trading agreements
such as EU, NAFTA, ASEAN, or others?
Risk Analysis and Mitigation
©2009 ASQ 92
• Economic Factors:
– Interest rates
– The level of inflation
– Employment level per capita
– Long-term prospects for the economy
– Gross Domestic Product (GDP) per capita, and so on.
• Sociocultural Factors:
– What are attitudes to foreign products and services?
– What are the roles of men and women within society?
– How long is the population living?
– Does the population have a strong/weak opinion onenvironmental issues?
Risk Analysis and Mitigation
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©2009 ASQ 93
• Technological Factors:
– Does technology allow for products and servicesto be made more cheaply and to a better
standard of quality?
– How is distribution changed by newtechnologies, e.g., books via the Internet,
flight tickets, auctions, etc?
– Does technology offer companies a new wayto communicate with consumers?
Risk Analysis and Mitigation
©2009 ASQ 94
Risk Analysis and Mitigation
TECHNOLOGICALSOCIO-CULTURAL
•Impact of Emerging Technologies
•Impact of Internet and Reduced
Communication Costs
•R&D Activity
•Impact of Technology Transfer
•Likely Technological Change
•Population Growth/Age Profile
•Heath, Education, Social Mobility
•Employment Patterns, Attitudes to Work
•Press, Public Opinion, Attitudes and
Taboos
•Lifestyle Choices
•Likely Socio-cultural Change
•Business Cycle Stage
•Growth, Inflation and Interest Rates
•Unemployment, Labor Supply, Labor
Costs
•Disposable Income Distribution
•Globalization
•Likely Economic Change
•Government Type
•Government Stability
•Freedom of Press, Rule of Law,
Bureaucracy, Corruption
•Regulation/Deregulation Trends
•Social/Employment Legislation
•Likely Political Change
ECONOMICPOLITICAL
PEST ANALYSIS FRAMEWORK
PEST Analysis Framework
©2009 ASQ 95
Unintended consequences:
• Understanding the mindset of business is crucial to the successof any quality project.
• A system is defined as “a group of interacting, interrelated, orinterdependent elements forming a complex whole.”
• " The ASQ Glossary defines system as “a group of inter-dependent processes and people that together perform acommon mission.”
• This latter definition highlights an important aspect of systems,namely that a system operates in unity toward a unified purpose.
• Without a true understanding of a system’s purpose, elements,and interdependencies, it is difficult to know what improvementswould truly benefit the system as a whole, rather than benefiting
only one of its elements at the possible expense of others.
Risk Analysis and Mitigation
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©2009 ASQ 96
Progress Check
What are some of the reasons to do a FeasibilityStudy? Select all that apply.
a. Identifies new opportunities through theinvestigative process.
b. It is a waste of time.
c. Enhances the probability of success by addressingand mitigating factors early on that could affect
the project.
d. Provides quality information for decision-making.
e. Provides documentation that the business
venture was thoroughly investigated.
©2009 ASQ 97
Progress Check
• Your organization is planning to introduce a newlawnmower in Florida. Your boss has instructedyou as a part of market planning to conduct aPEST analysis.
• As a group, create a PEST analysis table and
discuss.
©2009 ASQ 98
Module Status
1. Design of experiments (DOE)2. Waste elimination3. Cycle-time reduction4. Kaizen and kaizen blitz
5. Theory of constraints (TOC)6. Implementation7. Risk analysis and mitigation
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©2009 ASQ 99
Module 7
Exercise Solutions
©2009 ASQ 100
Answer
Process inputs an investigatormanipulates to cause a changein the output
Factors
A specific combination of factor
levels
Treatment
The output of a processResponse
Unexplained variation in the
processError
DefinitionTerm
Match each DOE term to the appropriate definition.
©2009 ASQ 101
Answer
Which of the descriptions in the table below is anexample of DOE terminology?
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©2009 ASQ 102
Answer Which of the descriptions in the table below are examplesof main effects or interactions? Check the appropriate box
at the right.Description/Name Example Non-Example
A two-factor DOE with two interactions. X
A three-factor DOE with A x B, A x C, B x C, and
A x B x C interactions.
X
A four-factor DOE with 2 x 4 = 8 main effects plots X
A five-factor DOE with a highest order interaction ofA x B x C x D
X
A DOE with factors A, B, and C and four main effectsplots
X
A DOE with factors A and B and only an A x Binteraction
X
Completing an analysis of variance to plot four-way
interactions
X
©2009 ASQ 103
Answers
Answers:
1. B2. C3. C
©2009 ASQ 104
Answer Match each Design to the appropriate Statement
Cannot be used when estimationof interaction is required
Latin square
Confounding takes placeFractional Factorial Design
We want to remove the variabilitybetween a factor that is not inquestion from the factor beingtested.
Randomized CompleteBlock Design
A four-factor, two-level experimenthas 16 runs
Full factorial Design
StatementDesign
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©2009 ASQ 105
Answer
1. In a pull system, who is doing the pulling?
a. Customer
b. Cycle time
c. Takt t ime
2. Identify the type of Non-Value-Added activity which woulddirectly ensue as a result of poor quality:
a. Reworking a product
b. Unnecessary motion
c. Transportation waste
Answers:1. a2. a
©2009 ASQ 106
Answer
a. To please the customerb. To reduce internal waste
c. To create a bottleneckd. To increase capacity
e. To remain competitive
The reduction of cycle time is undertaken for which
of the following principle reasons? Identify all thatapply.
Answer:
a, b, d, and e
©2009 ASQ 107
Answer
The key elements of kaizen are…… Check all that apply.a. Qualityb. Involvement of all employeesc. Poor morale
d. Willingness to changee. Indisciplinef. Communication
Answer:a, b, d, and f
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©2009 ASQ 108
Answer
The goals of TOC are…… Check all that apply.a. Increased throughputb. Reduced inventoryc. Increased operational expenses
d. Reduced demand
Answer:a and b
©2009 ASQ 109
Answer
When creating a communication plan, considerationis given to the following …Check all that apply
a. Goals and desired results of the programb. Overall communications objectives, such as
reinforcing customer service, loyalty, etc.c. Is sent only to the President of the organizationd. Audience you want to influence
e. Methods in your plan that you can use tomeasure and evaluate the program’s results
f. Media for communications
Answer:a, b, d, e and f
©2009 ASQ 110
Answer
What are some of the reasons to do a FeasibilityStudy? Select all that apply.
a. Identifies new opportunities through the investigativeprocess.
b. It is a waste of time.
c. Enhances the probability of success by addressing andmitigating factors early on that could affectthe project.
d. Provides quality information for decision-making.
e. Provides documentation that the business
venture was thoroughly investigated.
Answer:a, c, d, and e