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
  ages) and any added expenses
  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
• 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!!
   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
   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
                                                     Type                                                      Road
                                                                                           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
  don‟t 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
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
  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
         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
  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
             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
  and bad units is established
               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.
            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
  distinguish Good from Bad
     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
  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!
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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