IE 3265 R. Lindeke, Ph. D.

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IE 3265R. Lindeke, Ph. D.

• Quality Management in POM Part 2

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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

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Taguchis 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

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Exploring the Taguchi Method

• Considering the Loss Function, it is quantifiable
• Larger is Better
• Smaller is Better
• Nominal is Best

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Considering the Cost of Loss

• k in the L(y) equation is found from

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Loss Function Example (nominal is best)

• We can define a processes average loss as
• s is process (product) Standard Deviation
• ybar is process (product) mean

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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

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Example Cont.

• Average Loss
• If we make 250,000 units a year
• Annual Loss is 64,000

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Fixing it

• Shift the Mean to nominal
• Reduce variation (s 0.01)
• Fix Both!

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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

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Defining 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

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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

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Taguchi Method isStep-by-Step
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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

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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

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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

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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

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Selecting 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

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Preliminary 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!!

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Going 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

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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

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Generic Fishbone CE Diagram
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Building the Experiment Working From a Cause
Effect Diagram
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Designing 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

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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!)

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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

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Shainins Experimental Approaches to Quality
Variability Control
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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.)

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Basis 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

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Does the mean shift in time or between products
or is the product (alone) showing the variability?
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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 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

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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

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Temporal Variations

• Variations from hour-to-hour
• Variation shift-to-shift
• Variations from day-to-day
• Variation from week-to-week

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Components 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

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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 51, a significant and repeatable
  difference between good and bad units is
  established

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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
  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.

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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 IDd (on
  complete reversal) or two or more Pink X or pale
  Pink X families if only partial reversals are
  observed

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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.

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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 distinguish Good from Bad

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Steps 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

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Reviewing

• 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

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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

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Variables 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.

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Stage 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!

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Moving 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

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Continuing 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.

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Variables 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!

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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

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Results
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Continuing to Stage 2
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Factorial Analysis D F
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Factorial Analysis
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Factorial 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!

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Introduction 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

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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 FMs improved
  by 40 causes improved by 37. Overall risk in
  half in about 3 months.

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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

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Typical FMEA Evaluation Sheet
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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)