Title: IE 3265 R. Lindeke, Ph. D.
1IE 3265R. Lindeke, Ph. D.
- Quality Management in POM Part 2
2Topics
- ? 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
3Taguchis 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 ages) and any added expenses due to
harmful side effects of the product in use
4Exploring the Taguchi Method
- Considering the Loss Function, it is quantifiable
- Larger is Better
- Smaller is Better
- Nominal is Best
5Considering the Cost of Loss
- k in the L(y) equation is found from
6Loss Function Example (nominal is best)
- We can define a processes average loss as
- s is process (product) Standard Deviation
- ybar is process (product) mean
7Example 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
8Example Cont.
- Average Loss
- If we make 250,000 units a year
- Annual Loss is 64,000
9Fixing it
- Shift the Mean to nominal
- Reduce variation (s 0.01)
- Fix Both!
10Taguchi 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
11Defining the Taguchi Approach
- The Point Then Is To Produce Processes Or
Products The Are ROBUST AGAINST NOISES - Dont 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 SAYROBUSTNESS HIGH QUALITY
12Defining 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
13Taguchi Method isStep-by-Step
14Defining 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
15Defining 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
16Defining 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
17Defining 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
18Selecting Parameters for Study and Control
- Select The Quality Characteristic
- Define The Measurement Technique
- Ennumerate, Consider, And Select The Independent
Variables And Interactions - Brainstorming
- Shainins technique where they are determined by
looking at the products - FMEA failure mode and effects analysis
19Preliminary Steps in Improvement Studies
- To Adequately Address The Problem At 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!!
20Going Down the Improvement Road
- 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
21DEVELOPING 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
22Generic Fishbone CE Diagram
23Building the Experiment Working From a Cause
Effect Diagram
24Designing A Useful Experiment
- Taguchi methods use a cookbook approach!!
Building Experiments for selected factors on the
CE 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
25T.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!)
26Simplified 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
27Shainins Experimental Approaches to Quality
Variability Control
28Shainin 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.)
29Basis of Shainins Quality Improvement Approaches
- As Shainin Said Dont ask the engineers, they
dont know, ask the parts - 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
30Does the mean shift in time or between products
or is the product (alone) showing the variability?
31Positional 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 batch loading
processes - 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
32Cyclical 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
33Temporal Variations
- Variations from hour-to-hour
- Variation shift-to-shift
- Variations from day-to-day
- Variation from week-to-week
34Components Search the prerequisites
- The technique is applicable (primarily) in
assbly operations where good units and bad units
are found - 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
35The 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 51, a significant and repeatable
difference between good and bad units is
established
36Procedure (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
bad stays bad, the top guessed component (A) is
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.
37Procedure (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 IDd (on
complete reversal) or two or more Pink X or pale
Pink X families if only partial reversals are
observed
38Procedure (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.
39Paired 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 distinguish Good from Bad
40Steps in Paired Comparison
- Randomly select one Good and one Bad unit
call it pair one - 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) - Select a 2nd pair, observe and note as with pair
1. - Repeat with additional pairs until a pattern of
repeatability is observed between goods bads
41Reviewing
- The previous (three methods) are ones that
followed directly from Shainins 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
42Taking 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
43Variables Search is a 2 stage process
STAGE 1
- List the important input variables as chosen by
engineering judgment (in descending order of
ability to influence output) - Assign 2 levels to each factor a best and worst
level (within reasonable bounds) - Run 2 experiments, one with all factors at best
levels, the second with all factors at worst
levels. Run two replications sets - Apply the Dd ? 51 rule (as above)
- If the 51 ratio is exceeded, the Red X is
captured in the factor set tested.
44Stage 1 (cont)
- If the ratio is less than 51, the right factors
are not chosen or 1 or more factors have been
reversed between best worst levels.
Disappointing, but not fatal! - If the wrong factors were chosen in opinion of
design team decide on new factors and rerun
Stage 1 - 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 - 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!
45Moving on to Stage 2
- Run an experiment with AW (a at worst level) and
the rest of factors at best levels (RB) - If there is no change in best results in Stage 1
step 3, factor A is in fact unimportant - If there is a partial change from best results
toward Worst results A is not the only
important factor. A could be Pink X - If a complete reversal in Best to Worst results
in Stage 1 step 3, A is the Red X - Run a second test with AB and RW
- If no change from Worst results in Stage 1 the
top factor A is further confirmed as unimportant - 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 - If a complete reversal Best results in Stage 1
are approximated, A is reconfirmed as the Red X
46Continuing Stage 2
- Perform the same component search swap of step 1
2 for the rest of the factors to separate
important from unimportant factors - If no single Red X factor, but two or three Pink
X factors are found, perform a capping or
validation experiment with the Pink Xs at the
best levels (remaining factors at their worst
levels). The results should approximate the best
results of Step 3, Stage 1. - Run a second capping experiment with Pinks at
worst level, the rest at Best level should
approx. the worst results in Step 3, Stage 1.
47Variables Search ExamplePress 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!
48A 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
49Results
50Continuing to Stage 2
51Factorial Analysis D F
52Factorial Analysis
53Factorial Analysis
- Factor G is Red X It has a 41.9 main effect on
the process spread - Factor D is a Pink X with 10.9 main effect on
process spread - Their interaction is minor with a contribution of
4.9 to process spread - 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!
54Introduction to Failure Mode and Effects Analysis
(FMEA)
- Tool used to systematically evaluate a product,
process, or system - Developed in 1950s by US Navy, for use with
flight control systems - Today its 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
55Benefits 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 FMs improved
by 40 causes improved by 37. Overall risk in
half in about 3 months.
56FMEA 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
57Typical FMEA Evaluation Sheet
58Capturing 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)