Friday, 13 th July 2001 Of Course It is tip # 13 as well as It is Friday the 13th Why/When is Taguchi Method Appropriate?NOT

NOT When Taguchi Method is NOT Appropriate Tip #13 Friday, 13 th July 2001

NoIsENo NoIsE not NoIsE –When you can not think of NoIsE that can be included during the experiment NoIsENoIsE can be included during experiments not NoIsE –but can not think of Control Factors that have strong correlation to the NoIsE not NoIsE –but can not effectively capture the effects of NoIsE NOT When Taguchi Method is NOT Appropriate No NoIsE

1. IDENTIFY THE MAIN FUNCTION, SIDE EFFECTS, AND FAILURE MODE 2. IDENTIFY THE NOISE FACTORS, TESTING CONDITIONS, AND QUALITY CHARACTERISTICS 3. IDENTIFY THE OBJECTIVE FUNCTION TO BE OPTIMIZED 4. IDENTIFY THE CONTROL FACTORS AND THEIR LEVELS 5. SELECT THE ORTHOGONAL ARRAY MATRIX EXPERIMENT 6. CONDUCT THE MATRIX EXPERIMENT 7. ANALYZE THE DATA, PREDICT THE OPTIMUM LEVELS AND PERFORMANCE 8. PERFORM THE VERIFICATION EXPERIMENT AND PLAN THE FUTURE ACTION NOT 8-STEPS When Taguchi Method is NOT Appropriate in each of the 8-STEPS

Main FunctionMain Function no clue –When you have no clue as to what is the “Ideal Final Result” (the ‘distance’ between the ‘current’ result and IFR gives the necessary ‘boldness’ to vary Control factor levels widely enough to exploit non-linearities) Side EffectsSide Effects not –When you can not think of Side-Effects not NoIsEyou can not think of NoIsE that can cause such side-effects NOT When Taguchi Method is NOT Appropriate Step #1 : Main Function / Side Effects

NoIsE ???Include NoIsE ??? not NoIsE “experiment” –When you can not think of NoIsE that can be included during the “experiment” not NoIsE “measurements” –When you can not think of NoIsE that can be included during the “measurements” not NoIsE “aging”“slow degradation” –When you can not think of NoIsE that is analogous to “aging” or “slow degradation” “experiment” “measurements” during the “experiment” or “measurements” NOT When Taguchi Method is NOT Appropriate Step #2 : Including NoIsE

NoIsE includedNoIsE can be included during experiments –but not NoIsE –but can not think of Control Factors that have strong correlation to the NoIsE –but not NoIsE –but can not effectively capture the effects of NoIsE NoIsEExternal NoIsE (in explicitly added NoIsE Factors) NoIsEInternal NoIsE (in Control Factors) –but not –but do not wish to increase either the experimental effort experimental resources NOT When Taguchi Method is NOT Appropriate Step #2 : Capturing effects of NoIsE

notQuality Characteristics energy transfer”When you can not think of Quality Characteristics that closely represents the “energy transfer” mechanism in the main function Quality Characteristics notWhen the Quality Characteristics can not be quantitatively measured Quality Characteristics notWhen the Quality Characteristics is not monotonous (and has ‘phase-transitions’ or represents a ‘multiple valued function’) NOT When Taguchi Method is NOT Appropriate Step #3 : Quality Characteristics Step #3 : Quality Characteristics / Objective Function

not “Variations” quality CharacteristicsWhen you can not think of “Variations” in quality Characteristics as being important. “mean”In other words, you are able to give importance only to the “mean” value “mean”or even worseonly “mean”When you are interested only in improving the “mean”, or even worse, you are interested only in studying the factor effects (on “mean”) notWhen you are not interested in identifying Control Factors “Variance” –which help reduce the “Variance” “mean” –which help adjust the “mean” NOT When Taguchi Method is NOT Appropriate Objective Function Step #3 : Quality Characteristics / Objective Function

one’ Quality Characteristics notWhen you can think of only ‘one’ (desirable) Quality Characteristics and can not think of another (desirable or undesirable) not“contradictory” Quality CharacteristicsWhen you can not think of two “contradictory” requirements i.e. Quality Characteristics ‘improving’ (While Taguchi Method is capable of ‘improving’ both) notWhen you are not able to give priority to Variance  “Tomorrow’s Problem” (reducing Variance) and end up giving priority to “Mean”  “Today’s Problem” (improving “Mean”) NOT When Taguchi Method is NOT Appropriate Objective Function Step #3 : Quality Characteristics / Objective Function

Quality Characteristics mean smaller-the-better or Larger-the-Betternot Quality Characteristics varianceNominal-the-bestWhen you can think of Quality Characteristics that have more to do with mean like smaller-the-better or Larger-the-Better and can not think of any other Quality Characteristics that has to do with variance like Nominal-the-best adjustment factor negligiblevariancelarge meanWhen there is no need or scope of finding an adjustment factor (defined as the control factor that has negligible effect on variance and large effect on mean) NOT When Taguchi Method is NOT Appropriate Step #3 : No Need to determine an Adjustment Factor

not NoIsEWhen you can not think of Control Factors that are strongly correlated to NoIsE Factors twice NoIsE NoIsE Quality Characteristics)When the number of Control Factors is not even twice the number of NoIsE Factors (This is a ‘thumb’ rule – originating from the assumption that at least one of the two control factors will have a favorable and strong nonlinearity that will help reduce the effect of NoIsE on the Quality Characteristics) NOT When Taguchi Method is NOT Appropriate NoIsE Step #4 : Number of Control Factors and NoIsE Factors

NoIsE Quality Characteristics but notWhen Control Factors are chosen correctly (in the sense that these are strongly correlated to NoIsE Factors as well as have strong effect on Quality Characteristics) but the levels are not “wide apart”, with the result that the nonlinearity is not fully exploited (ending up in getting only sensitivity) On the other hand, one so widely separatedWhen the Levels of one of the Control Factors are so widely separated that only that control factor dominates (and other control factors show less than 5% effect) For example : Temperature in a bio-culture growth has levels of 25ºC, 37º C and 50º C This will dominate over all other control factors NOT When Taguchi Method is NOT Appropriate t o o w i d e too narrow Step #4 : Control Factors Levels (t o o w i d e or too narrow )

not orthogonal notWhen you can not guarantee that all the Control Factors are indeed orthogonal to each other and you have chosen an orthogonal array that does not allow study of all suspected interactions no left for estimating errorWhen the number of Control Factors and the chosen OA is such that there are no degrees of freedom left for estimating error (this forces one to declare control factors with less than 15% effect to be pooled as error) NOT When Taguchi Method is NOT Appropriate Step #5 : Select the “inner” Orthogonal Array

NoIsE ‘outer’ bigger defeatsWhen the OA selected for NoIsE factors (also called the ‘outer’ array) is bigger than the main OA (also called the ‘inner’ array) for Control Factors. (The main idea behind using an ‘outer’ OA is to reduce the number of testing conditions and a ‘bigger’ array defeats this main purpose). ‘worst case’‘failure’While the ‘outer’ array primarily gives the desired ‘worst case’ conditions, it should not lead to ‘failure’ of the experiment. (‘failure’ could be defined as – not able to quantitatively measure Quality Characteristics or – causing damage/breakdown of the process equipment) NOT When Taguchi Method is NOT Appropriate Step #5 : Select the “outer” Orthogonal Array

can notWhen the experimental conditions (other than the combinations of control factors that appear in the “inner” or “outer” OA’s) can not be maintained over the entire Matrix experiment NoIsE notWhen NoIsE can not be effectively captured on/in the samples or during the measurements all not lessincorrectWhen all experiments are not satisfactorily completed (even “one” less would give incorrect calculation of factor effects and predictions) NOT When Taguchi Method is NOT Appropriate Step #6 : Conduct the Matrix experiment based on “inner” and “outer” OA’s

Zero-Reading for a Larger-the-better type S/N Ratio or identical readings for Nominal-the-best type S/N Ratios (both give rise to “division by zero” when evaluating the above mentioned S/N ratios) one“detection sensitivity”multiple “measuring accuracy” –If you get one measurement less than the “detection sensitivity” or multiple measurements within the “measuring accuracy” of the measuring apparatus NoIsE  In fact, including NoIsE helps here, the measurements becomes larger than the least-count or measuring accuracy NOT When Taguchi Method is NOT Appropriate Step #6 and #7 : Make the Measurements and calculate the S/N Ratios

notWhen the confirmation experiments give results that are not close to the predicted results (i.e. are not within the prediction error)  Some important control factor is not chosen NoIsE  Some NoIsE factor that has a dominant effect  NoIsE  NoIsE is not captured effectively NoIsE  There is no control factor that has strong correlation to NoIsE is  Interaction between Factors : There is interaction between two dominant control factors and it has not been studied or the chosen OA does not allow this interaction to be studied NOT When Taguchi Method is NOT Appropriate Step #8 : Conduct the Confirmation / Verification Experiments

More Tips Links below 16.Taguchi Method V a r i a n c e R e d u c t i o n Factor Effects 1 st Priority : V a r i a n c e R e d u c t i o n 2 nd Priority : Factor Effects “inner” “outer” 15. “inner” L9 array with “outer” L4 and L9 NoIsE arrays 14.Taguchi Method “inner” “outer” “inner” L18 array with “outer” L4 and L9 NoIsE arrays not 13.Taguchi Method Why/When is Taguchi Method not Appropriate? Friday, 3rd Aug 2001 Friday, 27 th July 2001 Friday, 20 th July 2001 Friday, 13 th July 2001 Tips 12, 11, 10 

More Tips Links below 12.Taguchi Method “inner” “outer” “inner” L8 array with “outer” L4 and L9 NoIsE arrays 11.Taguchi Method ALL Life-stages Useful at ALL Life-stages of a Process or Product 10.Taguchi Method “centering”“fine tuning” Performs Process “centering” or “fine tuning” Friday, 6 th July 2001 Friday, 29 th June 2001 Friday, 22 nd June 2001 Tips 9, 8, 7 

More Tips Links below NoIsE Tolerance Design 9.Taguchi Method Identifies the “right” NoIsE factor(s) for Tolerance Design 8.Taguchi Method Finds best settings to optimize TWO quality characteristics Simultaneously 7. Taguchi Method When to select a ‘Larger’ OA to perform “Factorial Experiments” Friday, 15 th June 2001 Friday, 8 th June 2001 Friday, 1 st June 2001 Tips 6, 5, 4 

More Tips Links below 6.Taguchi Method Using Orthogonal Arrays for Generating Balanced Combinations of NoIsE Factors approaching IDEAL value 5.Taguchi Method Signal-to-Noise Ratio for Quality Characteristics approaching IDEAL value 4. Taguchi Method improves " quality “ at all the life stages the design stage itself at the design stage itself Friday, 25 th May 2001 Friday, 18 th May 2001 Friday, 11 th May 2001 Tips 3, 2, 1 

More Tips Links below Concurrent Engineering 3.Taguchi Method Appropriate for Concurrent Engineering 2.Taguchi Method can study Interaction Noise Factors Control Factors between Noise Factors and Control Factors Signal-to-Noise Ratios Log form 1.Taguchi’s Signal-to-Noise Ratios are in Log form Friday, 4 th May 2001 Friday, 27 th April 2001 Friday, 6 th April 2001

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