# IE 3265 R. Lindeke, Ph. D. - PowerPoint by xje11366

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```									       IE 3265
R. Lindeke, Ph. D.
Quality Management in POM
– Part 2
Topics
•  Managing a Quality System
–  Total Quality Management (TQM)
•  Achieving Quality in a System
–  Look early and often
–  6 Sigma – an approach & a technique
–  Make it a part of the process
•  The Customers Voice in Total
Quality Management
–  QFD and the House of Quality
• Quality Engineering
– Loss Function
– Quality Studies
– Experimental Approaches
• T.M.; FMEA; Shainin
Taguchi‟s Loss Function
• Taguchi defines Quality Level of a
product as the Total Loss incurred
by society due to failure of a
product to perform as desired
when it deviates from the delivered
target performance levels.
• This includes costs associated
with poor performance, operating
costs (which changes as a product
due to harmful side effects of the
product in use
Exploring the Taguchi Method
• Considering the Loss
Function, it is quantifiable
L( y )  k  1 2 
– Larger is Better:
 y 
     
– Smaller is Better:   L( y )  ky 2

L( y )  k  y  m 
2

– Nominal is Best:     where :
m is the target of the
process specification
Considering the Cost of Loss
• k in the L(y) equation is found from:
A0
k
0
2

A0 is cost of repair or replace
a product and must include
loss due to unavailability
during repair
 0 is the functional limit on
y of a product where it would
fail to perform its function
half the time
Loss Function Example: (nominal is
best)
• We can define a processes average
loss as:


L  k s  y  m    
2
2

          

• s is process (product) Standard
Deviation
• ybar is process (product) mean
Example cont.
• A0 is \$2 (a very low number of this type!)
found by estimating that the loss is 10% of
the \$20 product cost when a part is exactly
8.55 or 8.45 units
• Process specification is: 8.5+.05 units
• Historically: ybar = 8.492 and s = 0.016
Example Cont.
• Average Loss:

L 2        0.016 2   8.492  8.500 2 
.052                                 
L  800  .00032  \$0.256
• If we make 250,000 units a year
• Annual Loss is \$64,000
Fixing it
• Shift the Mean to     L  800 .0162   0 2   \$0.2048
nominal                                      
Annual Loss is \$51200 about 20% reduction

• Reduce variation         .0102  .008 2   \$0.1312
L  800
(s = 0.01)                                
Annual Loss is \$32800 about 50% reduction

• Fix Both!           L  800 .0102   0 2   \$0.08
               
Annual Loss is \$20000 about 66% reduction
Taguchi Methods
• Help companies to perform the Quality Fix!
– Quality problems are due to Noises in the product or
process system
– Noise is any undesirable effect that increases
variability
•   Conduct extensive Problem Analyses
•   Employ Inter-disciplinary Teams
•   Perform Designed Experimental Analyses
•   Evaluate Experiments using ANOVA and Signal-
to noise techniques
Defining the Taguchi Approach –

• The Point Then Is To Produce
Processes Or Products The Are
ROBUST AGAINST NOISES
• Don‟t spend the money to eliminate all
noise, build designs (product and
process) that can perform as desired –
low variability – in the presence of
noise!
• WE SAY:
ROBUSTNESS = HIGH QUALITY
Defining the Taguchi Approach –

• Noise Factors Cause Functional Variation
• They Fall Into Three “Classes”
– 1. Outer Noise – Environmental Conditions
– 2. Inner Noise – Lifetime Deterioration
– 3. Between Product Noise – Piece To Piece
Variation
Taguchi
Method is
Step-by-
Step:
Defining the Taguchi Approach
• TO RELIABLY MEET OUR DESIGN
GOALS MEANS: DESIGNING
QUALITY IN!

• We find that Taguchi considered
THREE LEVELS OF DESIGN:
– level 1: SYSTEM DESIGN
– level 2: PARAMETER DESIGN
– level 3: TOLERANCE DESIGN
Defining the Taguchi Approach –
SYSTEM DESIGN:
• All About Innovation – New
Ideas, Techniques,
Philosophies
• Application Of Science And
Engineering Knowledge
• Includes Selection Of:
– Materials
– Processes
– Tentative Parameter Values
Defining the Taguchi Approach –
Parameter Design:
• Tests For Levels Of Parameter
Values

• Selects "Best Levels" For Operating
Parameters to be Least Sensitive to
Noises

• Develops Processes Or Products
That Are Robust

• A Key Step To Increasing Quality
Without Increased Cost
Defining the Taguchi Approach –
Tolerance Design:
• A "Last Resort" Improvement Step
• Identifies Parameters Having the
greatest Influence On Output
Variation
• Tightens Tolerances On These
Parameters
• Typically Means Increases In
Cost
Selecting Parameters for Study and
Control
• Select The Quality Characteristic
• Define The Measurement Technique
• Ennumerate, Consider, And Select The
Independent Variables And Interactions
• Brainstorming
• Shainin‟s technique where they are determined by
looking at the products
• FMEA – failure mode and effects analysis
Preliminary Steps in Improvement
Studies
Hand We Must:
1. Understand Its Relationship With The Goals
We Are Trying To Achieve
2. Explore/Review Past Performance compare
to desired Solutions
3. Prepare An 80/20 Or Pareto Chart Of These
Past Events
4. Develop A "Process Control" Chart -- This
Helps To Better See The Relationship
between Potential Control And Noise
Factors
• A Wise Person Can Say: A Problem
Well Defined Is Already Nearly Solved!!
Going Down the Improvement
• Start By Generating The Problem
Candidates List:
– Brainstorm The Product Or Process
• Develop Cause And Effects (Ishikawa)
Diagrams
– Using Process Flow Charts To
Stimulate Ideas
– Develop Pareto Charts For Quality
Problems
DEVELOPING A Cause-and-Effect
Diagram:
1. Construct A Straight Horizontal Line (Right Facing)

2. Write Quality Characteristic At Right

3. Draw 45° Lines From Main Horizontal (4 Or 5) For Major
Categories: Manpower, Materials, Machines, Methods And
Environment

4. Add Possible Causes By Connecting Horizontal Lines To 45°
"Main Cause" Rays

5. Add More Detailed Potential Causes Using Angled Rays To
Horizontal Possible Cause Lines
Generic Fishbone C&E Diagram
Main Causes

Methods           Manpower
2nd Cause

Primary Cause                            Primary Cause

2nd Cause                                     2nd Cause

Effect
under
Study

Environment              Materials          Machines
Building the „Experiment‟ Working
From a Cause & Effect Diagram
Package
Crystallization                       Over Weight                                   Raw Material
Type of                                                      Reaction
Balance
Time
Temperature                                  Maint. Of
Balance                                     Shortage of                        Sol A              Sol. A Conc.
Weight                   Pour Speed
Accuracy of
Balance                   Method of
Size                                                                                                                            Sol. B Temp.
Weight                                                                    Weighing
Concentration                                                 Discharge                     Stir RPM
Method                                            pH
Time
‘Mother Crystal’                       Operator
Fine Grained
RPM of                    Chemical Yield
Container
Dryer
Quality                                                                               Temperature
Spillage
Steam    Steam
Quantity                                Charge Speed           Press.    Flow
Wet Powder
Cover

Moisture
Catalyzer                             Transportation
Content
Designing A Useful Experiment
• Taguchi methods use a cookbook
approach!! Building Experiments for
selected factors on the C&E Diagram
• Selection is from a discrete set of
„Orthogonal Arrays‟
• Note: an orthogonal array (OA) is a special
fractional factorial design that allows study
of main factors and 2-way interactions
T.M. Summary
• Taguchi methods (TM) are product or
process improvement techniques that
use DOE methods for improvements
• A set of cookbook designs are available
– and they can be modified to build a
rich set of studies (beyond what we
have seen in MP labs!)
• TM requires a commitment to complete
studies and the discipline to continue in
the face of setbacks (as do all quality
improvement methods!)
Simplified DOE
• Shainin Tools – these are a series of
steps to logically identify the root
causes of variation
• These tools are simple to implement,
statistically powerful and practical
• Initial Step is to sample product (over
time) and examine the sample lots for
variability to identify causative factors
– this step is called the multi-vari chart
approach
• Shainin refers to root cause factors as the
“Red X”, “Pink X”, and “Pink-Pink X” causes
20 - 100 Variables

Shainin‟s
„Experimental   Components       Multi-vari         Paired

Approaches‟       Search          Charts          Comparisons

to Quality
Variability
Variables Search                   5 - 20 Variables
Control:

4 or Less
Full Factorials
Variables

B vs. C                         Validation

Scatter Plots                     Optimization
Shainin Ideas – exploring
further
• Red X – the primary cause of
variation
• Pink X – the secondary
causes of variation
• Pink-Pink X significant but
minor causes of variation (a
factor that still must be
controlled!)
• Any other factors should be
substituted by lower cost
solutions (wider tolerance,
cheaper material, etc.)
Basis of Shainin‟s Quality
Improvement Approaches
• As Shainin Said: “Don‟t ask the engineers, they
• Contrast with Brainstorming approach of Taguchi
Method
• Multi-Vari is designed to identify the likely home
of the Red X factors – not necessarily the factors
themselves
• Shainin suggests that we look into three source
of variation regimes:
• Positional
• Cyclical
• Temporal
Does the
mean shift
in time or
between
products
or is the
product
(alone)
showing
the
variability?
Positional Variations:
• These are variation within a given
unit (of production)
•   Like porosity in castings – or cracks
•   Or across a unit with many parts – like a
transmission, turbine or circuit board
• Could be variations by location in
•   Cavity to cavity variation in plastic injection
molding, etc.
•   Various tele-marketers at a fund raiser
• Variation from machine-to-machine,
person-to-person or plant-to-plant
Cyclical Variation
• Variation between consecutive
units drawn from a process
(consider calls on a software
help line)
• Variation AMONG groups of
units
• Batch-to Batch Variations
• Lot-to-lot variations
Temporal Variations
•   Variations from hour-to-hour
•   Variation shift-to-shift
•   Variations from day-to-day
•   Variation from week-to-week
Components Search – the
prerequisites
• The technique is applicable (primarily) in
ass‟bly operations where good units and
• Performance (output) must be measurable
and repeatable
• Units must be capable of disassembly and
reassembly without significant change in
original performance
• There must be at least 2 assemblies or
units – one good, one bad
The procedure:
• Select the good and bad unit
• Determine the quantitative parameter
by which to measure the units
• Dissemble the good unit –
reassemble and measure it again.
Disassemble and reassemble then
measure the bad units again. If the
difference D between good and bad
exceeds the d difference (within
units) by 5:1, a significant and
repeatable difference between good
Procedure (cont.)
• Based on engineering judgment, rank the
likely component problems, within a unit, in
descending order of perceived importance.
• Switch the top ranked component from the
good unit to the bad unit or assembly with
the corresponding component in the bad
assembly going to the good assembly.
Measure the 2 (reassembled) units.
• If there is no change: the good unit stays good
unimportant – go on to component B
• If there is a partial change in the two
measurements A is not the only important
variable. A could be a Pink X family. Go on to
Component B
• If there is a complete reversal in outputs of the
assemblies, A could be in the Red X family. There
is no further need for components search.
Procedure (cont.)
• Regardless of which of the three
outcomes above are observed,
restore component A to the original
units to assure original conditions
are repeated. Then, repeat the
previous 2 steps for the next most
important components: B, C, D, etc.
if each swap leads to „no‟ or „partial‟
change
• Ultimately, the Red X family will be
ID‟d (on complete reversal) or two or
more Pink X or pale Pink X families
if only partial reversals are observed
Procedure (cont.)

• With the important variables
identified, a „capping run‟ with the
variables banded together as good
or bad assemblies must be used to
verify their importance
• Finally, a factorial matrix, using data
generated during the search, is
drawn to determine, quantitatively,
main effects and interactive effects.
Paired Comparisons
• This is a technique like
components search – but
when products do not lend
themselves to disassembly
(perhaps it is a component in a
component search!)
• Requires that there be several
Good and Bad units that can
be compared
• Requires that a suitable
parameter can be identified to
Steps in Paired Comparison
1.   Randomly select one “Good” and one “Bad” unit – call
it pair one
2.   Observe the differences between the 2 units – these
can be visual, dimensional, electrical, mechanical,
chemical, etc. Observe using appropriate means (eye,
optical or electron microscopic, X-ray, Spectrographic,
tests-to-failure, etc)
3.   Select a 2nd pair, observe and note as with pair 1.
4.   Repeat with additional pairs until a pattern of
repeatability is observed between “goods & bads”
Reviewing:
• The previous (three methods) are ones that
followed directly from Shainin‟s “talk to the
animals (products)” approach
• In each, before we began actively specifying
the DOE parameters, we collect as much
information as we can from good or bad
products
• As stated by one user: “The product solution
was sought for over 18 months, we talked to
engineers & designers; we talked to
engineering managers, even product
suppliers – all without a successful solution,
but we never talked to the parts. With the
component search technique we identified
the problem in just 3 days”
Taking the Next step: Variables
Search
• The objective is to
– Pinpoint the Red X, Pink X and one to three (more) critical
interacting variables
– Its possible that the „Red X‟ is due to strong interactions between
two or more variables
– Finally we are still trying to separate the important variables from
unimportant ones
• Variables search is a way to get statistically significant
results without executing a large number of experimental
runs (achieving knowledge at reduced cost)
• It has been shown the this binary comparison technique
(on 5 to 15 variables) can be successful in 20, 22, 24 or
26 runs vs. 256, 512, 1024, etc. runs using traditional
DOE
Variables Search is a 2 stage
process:
STAGE 1:
1. List the important input variables as chosen by
engineering judgment (in descending order of
ability to influence output)
2. Assign 2 levels to each factor – a best and
worst level (within reasonable bounds)
3. Run 2 experiments, one with all factors at best
levels, the second with all factors at worst
levels. Run two replications sets
4. Apply the D:d  5:1 rule (as above)
5. If the 5:1 ratio is exceeded, the Red X is
captured in the factor set tested.
Stage 1 (cont):
6.    If the ratio is less than 5:1, the right factors are not
chosen or 1 or more factors have been reversed
between “best” & “worst” levels. Disappointing, but not
fatal!

a. If the wrong factors were chosen – in opinion of design team –
decide on new factors and rerun Stage 1
b. If the team believes it has the correct factors included, but some
have reversed levels, run B vs. C tests on each suspicious
factor to see if factor levels are in fact reversed
c. One could try the selected factors (4 at a time) using full
factorial experiments – could be prone to failure too if
interacting factors are separated during testing!
Moving on to Stage 2:
1.    Run an experiment with AW (a at worst level) and the
rest of factors at best levels (RB)
a) If there is no change in best results in Stage 1 step 3, factor A is
in fact unimportant
b) If there is a partial change from best results – toward Worst
results – A is not the only important factor. A could be Pink X
c) If a complete reversal in Best to Worst results in Stage 1 step 3,
A is the Red X

2.    Run a second test with AB and RW
a) If no change from Worst results in Stage 1 the top factor A is
further confirmed as unimportant
b) If there is a partial change in the worst results in Stage 1 –
toward Best results – A is further confirmed as a possible Pink
X factor
c) If a complete reversal – Best results in Stage 1 are
approximated, A is reconfirmed as the Red X
Continuing Stage 2:
3. Perform the same component search swap of
step 1 & 2 for the rest of the factors to separate
important from unimportant factors
4. If no single Red X factor, but two or three Pink
X factors are found, perform a capping or
validation experiment with the Pink X‟s at the
best levels (remaining factors at their worst
levels). The results should approximate the
best results of Step 3, Stage 1.
5. Run a second capping experiment with Pink‟s
at worst level, the rest at Best level – should
approx. the worst results in Step 3, Stage 1.
Variables Search Example:
Press Brake Operation
• A press brake was showing high variability with poor CPK
• The Press Brake was viewed as a “Black Magic”
operation – the worked sometimes then went bad „for no
reason‟
• Causes of the operational variability were hotly debated,
Issues included:
– Raw Sheet metal
• Thickness
• Hardness
– Press Brake Factors (some which are difficult or impossible to
control)
• The company investigated new P. Brakes but observed
no realistic and reliable improvements
– Even high cost automated brakes sometimes produced poor
results!
A Variables Search was
Performed
• Goal was to consistently achieve a .005”
tolerance (or closer!)
• 6 Factors were chosen:
– A. Punch/Die Alignment – B: „Aligned‟, W: „not
Specially Aligned‟
– B. Metal Thickness – B: „Thick‟, W: „Thin‟
– C. Metal Hardness – B: „Hard‟, W: „Soft‟
– D. Metal Bow – B: „Flat‟, W: „Bowed‟
– E. Ram Storage – B: „Coin Form‟, W: „Air Form‟
– F. Holding Material – B: „Level‟, W: „Angle‟
• Results reported in “Process Widths” which is
twice tolerance, in 0.001” units
Results:
STAGE 1            Process Width (x.001)
All Best         All Worst

Initial          4                47

Rep 1            4                61

D = 50; d = 7 D:d 7:1 (> 5:1) so a significant
repeatable difference; Red X (or Pink X‟s) captured
as a factor
Continuing to Stage 2
Test      Comb.        Results   Conclusion
1         AWRB         3
A. not Important
2         ABRW         102
3         BWRB         5
B. Not Important
4         BBRW         47
5         CWRB         7
C. Not Important
6         CBRW         72
7         DWRB         23        Pink X: Interaction w/ other
8         DBRW         30        factor(s)
9         EWRB         7
???
10        EBRW         20
11        FWRB         73
Prob. Red X + Interaction
12        FBRW         18
Cap Run   D W FW R B   70
Complete Reversal Effected
Cap Run   DB FB RW     4
Factorial Analysis: D & F

D Best           D Worst

F Best     4, 4, 3, 5, 7,   23, 18          Row Sum:
7, 4                             25.4
Avg: 4.9         Avg: 20.5
F Worst    73, 20           47, 102, 61     Row Sum:
47, 72, 70,     109.3
Avg: 51.5        20; Avg: 57.8
Diagonal   Column Sum:      Column Sum:     Diagonal
Sum: 72    56.4             78.3            Sum: 62.7
Factorial Analysis:

D
 20.5  51.8   4.9  51.5  78.3  56.4
2                     2
 10.95

F
 51.5  57.8   4.9  20.5  109.3  25.4
2                       2
 41.95
D. Sum1  D. Sum 2 72  62.7
DF (interaction)                    
2              2
 4.7
Factorial Analysis:
• Factor G is Red X: It has a 41.9 main effect
• Factor D is a Pink X with 10.9 main effect on
• Their interaction is minor with a contribution
• With D & F controlled, using a holding fixture
to assure level and reduction in bowing (but
with hardness and thickness tolerances
open up leading to reduced raw metal costs)
the process spread was reduced to 0.004”
(.002) much better than the original target
of .005” with an observed CPK of 2.5!
Introduction to Failure Mode and
Effects Analysis (FMEA)
• Tool used to systematically evaluate a product,
process, or system
• Developed in 1950‟s by US Navy, for use with flight
control systems
• Today it‟s used in several industries, in many
applications
–   products
–   processes
–   equipment
–   software
–   service
• Conducted on new or existing products/processes
• Presentation focuses on FMEA for existing process
Benefits of FMEA
• Collects all potential issues into one document
– Can serve as troubleshooting guide
– Is valuable resource for new employees at the process
• Provides analytical assessment of process risk
– Prioritizes potential problems at process
– Total process risk can be summarized, and compared to other
processes to better allocate resources
• Serves as baseline for future improvement at process
–   Actions resulting in improvements can be documented
–   Personnel responsible for improvements can gain recognition
–   Controls can be effectively implemented
–   Example: Horizontal Bond Process: FM’s improved by 40%;
causes improved by 37%. Overall risk in half in about 3 months.
FMEA Development
• Assemble a team of people familiar with
process
• Brainstorm process/product related defects
(Failure Modes)
• List Effects, Causes, and Current Controls
for each failure mode
• Assign ratings (1-10) for Severity,
Occurrence, and Detection for each failure
mode
– 1 is best, 10 is worst
• Determine Risk Priority Number (RPN) for
each failure mode
– Calculated as Severity x Occurrence x Detection
Typical FMEA Evaluation Sheet
Capturing The Essence of FMEA
• The FMEA is a tool to systematically
evaluate a process or product
• Use this methodology to:
– Prioritize which processes/ parameters/
characteristics to work on (Plan)
– Take action to improve process (Do)
– Implement controls to verify/validate
process (Check)
– Update FMEA scores, and start focusing
on next highest FM or cause (Act
Plan)

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