Friday, 13 th July 2001

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

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

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

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

5. Step #1 : Main Function / Side Effects When Taguchi Method is NOT Appropriate • Main Function • 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 Effects • When you can not think of Side-Effects • you can not think of NoIsE that can cause such side-effects

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

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

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

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

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

11. Step #3 : No Need to determine an Adjustment Factor When Taguchi Method is NOT Appropriate • When you can think of Quality Characteristicsthat have more to do with mean likesmaller-the-betterorLarger-the-Better and can not think of any other Quality Characteristicsthat has to do with variance like Nominal-the-best • When 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)

12. Step #4 : Number of Control Factors and NoIsEFactors When Taguchi Method is NOT Appropriate • When you can not think of Control Factors that are strongly correlated to NoIsEFactors • When the number of Control Factors is not even twicethe number of NoIsEFactors (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)

13. Step #4 : Control Factors Levels (t o o w i d e or too narrow) When Taguchi Method is NOT Appropriate • When Control Factors are chosen correctly (in the sense that these are strongly correlated to NoIsEFactors 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, • When 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

14. Step #5 : Select the “inner” Orthogonal Array When Taguchi Method is NOT Appropriate • When you can notguarantee 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 • When 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)

15. Step #5 : Select the “outer” Orthogonal Array When Taguchi Method is NOT Appropriate • When the OA selected for NoIsEfactors (also called the ‘outer’ array) is biggerthan 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). • 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)

16. Step #6 : Conduct the Matrix experiment based on “inner” and “outer” OA’s When Taguchi Method is NOT Appropriate • When 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 • When NoIsEcan not be effectively captured on/in the samples or during the measurements • When all experiments are not satisfactorily completed (even “one” less would give incorrect calculation of factor effects and predictions)

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

18. Step #8 : Conduct the Confirmation / Verification Experiments When Taguchi Method is NOT Appropriate • When 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 • Some NoIsEfactor that has a dominant effect • NoIsEis not captured effectively • There is no control factor that has strong correlation to NoIsE • 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

19. Taguchi Method 1st Priority : Variance Reduction2nd Priority : Factor Effects 15.“inner” L9 array with “outer” L4 and L9 NoIsE arrays Taguchi Method “inner” L18 array with “outer” L4 and L9 NoIsE arrays Taguchi Method Why/When is TaguchiMethod not Appropriate? Friday, 3rd Aug 2001 Friday, 27th July 2001 Friday, 20th July 2001 Friday, 13th July 2001 More Tips Links below Tips 12, 11, 10 

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

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

22. Taguchi MethodUsing Orthogonal Arrays for Generating Balanced Combinations of NoIsE Factors Taguchi MethodSignal-to-Noise Ratio for Quality Characteristicsapproaching IDEAL value 4. Taguchi Methodimproves " quality “ at all the life stages atthe design stage itself Friday, 25th May 2001 Friday, 18th May 2001 Friday, 11th May 2001 More Tips Links below Tips 3, 2, 1 

23. 3. Taguchi MethodAppropriate forConcurrent Engineering 2. Taguchi Methodcan studyInteraction between Noise Factors and Control Factors 1. Taguchi’sSignal-to-Noise Ratiosare inLog form Friday, 4th May 2001 Friday, 27th April 2001 Friday, 6th April 2001 More Tips Links below

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