Taguchi and TRIZ: Comparisons and Opportunities
Editor | On 15, Nov 2001
By: P. R. Apte and D. L. Mann
In this article, we draw comparisons between TRIZ and the tools and strategies contained in Taguchi methods. Our aim is to identify areas of common ground and differences between the two approaches which might enable users of TRIZ to benefit from the findings of Taguchi methods. For those requiring a basic introduction to Taguchi Methods, we recommend (1).
We have arranged the article into the following topic areas:
 Taguchi Factor Effect Plots and their relation to TRIZ Physical and Technical Contradictions
 S/N Ratio (Objective Function) and relations to
- â€œIdeal Final Result (IFR)â€
- â€œpartial/inefficient useful actionâ€
- â€œelimination of harmful effectsâ€
 Taguchi Methods in an integrated (Define, Select, Solve and Evaluate) TRIZ Process
 IFR goals/characteristics and their achievement through Taguchi Method concepts/tools
 Trends of Evolution and Taguchi Method – Identifying and solving tomorrowâ€™s problems.
(deploying Taguchi NoIsE for future stages like manufacture, Operation/use, and Aging/drift)
 Utilization of Resources and the Taguchi Method
 The 8-steps of the Taguchi Method and ARIZ
 Taguchi Conceptsnot yet used in TRIZ, but which offer potentially significant improvements to the way TRIZ is used.
We see the article as a series of steps towards a much more closely integrated application of TRIZ and Taguchi methods. We invite reader contributions towards this evolution.
 Factor Effect Plots in relation to TRIZ Contradictions
(a) Technical Contradictions :
Referring to Fig 1,
A2 Ã Nominal value of Control Factor A
If A2 Ã A1, then QC2 improves but QC1 worsens
If A2 Ã A3, then QC1 improves but QC2 worsens
Fig 1. Factor Effect Plots for 2 Quality Characteristics, QC1 and QC2
This points clearly to a â€œTechnical Contradictionâ€ between features QC1 and QC2. This connection between hyperbolic profile curves and technical contradictions has previously been described in Reference (2) for cases in which QC1 and QC2 are drawn on the two axes of the same graph. The connection with Taguchi factor effect plots hopefully serves to re-enforce the connection between this curve shape and the existence of technical contradictions.
(b) Physical Contradictions :
Referring to the top part of Fig 2:
Best Value of QC is found at parameter setting A2
If A2 Ã A1 or A2 Ã A3, then QC worsens
This clearly points to the fact that A2 is the best setting of parameter A
Referring to the bottom part of the figure.
Taguchi Statement : Best settings could be A1 or A3
TRIZ statement : For best QC, A should be low as well as high
This clearly points to a â€œPhysical Contradictionâ€. The Feature associated with this contradiction is A. Again this parabolic-like graph shape has previously been seen to relate the existence of a physical contradiction – as described in Reference 1.
Fig 2. Factor Effect Plots for QC
The Taguchi method points out clearly the technical and physical contradictions and thus helps TRIZ in the sense that identification of the problem becomes easy. TRIZ tools can then be applied to resolve the contradictions. Exactly in the opposite way, the innovative solution concepts of TRIZ can be verified, evaluated, implemented by planning an experiment where parameter settings can be optimized and best process can be selected.
 S/N Ratio (Objective Function) and relations to TRIZ
Taguchi methods are experimental statistical methods to optimize a given process technology with respect to an objective function defined as
Variance is in fact reduced in presence of noise (variations in the control parameters of the process) and thus the product/process becomes â€œrobustâ€ and â€œlow costâ€. For both Taguchi and TRIZ the Ideal Value is Â¥ (infinity).
(a) Since the ideal value is Â¥ (infinity), the primary importance is shifted from â€œimproving meanâ€ (as in the conventional approach) to â€œreduction in Varianceâ€ to 0 (zero).
(b) Identify an â€œadjustment factorâ€ that has little or no effect on the variance but has a large effect on the mean
Ã use the adjustment factor to â€œputâ€ the â€œmean-on-targetâ€
COMPARISON with TRIZ :
(a) Objective functions of Taguchi Method, also called â€˜Signal-to-Noise Ratiosâ€™ (S/N Ratios). While these objective functions bear little relation to the concept of â€˜define the IFR and work back from itâ€™ found in TRIZ, they are similar to the â€œIdeal Final Resultâ€ of TRIZ in the sense of providing a measure of system ideality: Improvement in S/N ratio takes us closer to the IFR.
(b) In TRIZ, there are two main ways of moving an existing system forward towards ideality
(i) â€˜improvingâ€™ partial or inefficient useful action
(ii) â€˜eliminatingâ€™ harmful action/effects
Both directions can be achieved using the S-Fields and Trends parts of the TRIZ toolkit.
Harmful action can be eliminated in 3 possible ways,
1. Eliminate the â€œrootâ€ cause (of harmful action)
2. Eliminate the harmful â€œactionâ€ itself
3. Eliminate the â€œeffectsâ€ of the harmful action
The â€œrootâ€ cause identification and elimination is â€˜idealisticâ€™ and so is removal of â€œactionâ€™ itself. Many times, this changes the S-Field model or its implementation completely.
So, we look for more practical approach for eliminating the â€œharmful effectâ€ while allowing the harm causing action to persist! (It may be performing some useful function). THIS is the core principle of Taguchi Method and ROBUST design. Thus, Option-3 matches well with the Taguchi Method.
 â€œ4-stage TRIZ Processâ€: (Define, Select, Solve and Evaluate)
(a) TRIZ Stage-1 : Define the problem in TRIZ terminology
(i) as Technical Contradiction or Physical Contradiction (to be eliminated)
(ii) as Partial or inefficient useful action (to be improved)
(iii) as harmful action /or effect (to be eliminated)
(b) TRIZ Stage-2 : Select from several innovative problems (and identify appropriate TRIZ-tools)
(c) TRIZ Stage-3 : Solve the problem (contradictions, inefficient useful action, harmful effects)
(d) TRIZ Stage-4 : Evaluate (verify that the problem is solved and no new problem appears)
Taguchi Method helps
Ã˜ Identify contradictions from the factor effect plots (as shown in section  earlier)
Ã˜ Use â€˜useful actionâ€™ as a Quality Characteristic (as Larger-the-Better type) and maximize it
Ã˜ Include the â€˜harmful actionâ€™ as NoIsE during the experiments. Achieving â€˜insensitivityâ€™ to NoIsE thus makes the process ROBUST.
Ã˜ Taguchi method grades solutions in the following way
(i) primary purpose Ã to make the process â€˜insensitiveâ€™ to NoIsE
(ii) secondary purpose Ã to identify â€˜adjustment parameterâ€™ to put the mean-on-target
(iii) tertiary purpose Ã to identify settings that will improve 2 or more characteristics.
Ã˜ Factor Effect Plots are used to decide how two or more quality characteristics can be improved simultaneously, even though they appear to have contradictory behaviour with respect to a particular control factor. Separate control factors are identified that improve each of the characteristics.
Ã˜ Taguchi method helps
(i) improve (inefficient) useful action Ã as larger-the-better characteristics
(ii) eliminate the â€˜effectsâ€™ of harmful action (NoIsE) Ã make it ROBUST
(iii) give a â€˜measureâ€™ of the contradiction Ã from Factor effect plots
(both Technical and Physical)
Ã˜ Taguchi method evaluates the modified process (as suggested by S-Field transformation and/or ARIZ) by actually (i) improving quality characteristics, (ii) making a process ROBUST and (iii) eliminating/minimizing contradictions that are verified/shown by Factor Effect plots.
 IFR goals/characteristics are achieved through Taguchi Method concepts/tools
IFR has the following characteristics
Eliminates the deficiencies of the original system
Preserves advantages of the original system
Does not make the original system more complicated (uses free or available resources)
Does not introduce new disadvantages
Taguchi Method helps
Ã˜ Reduce â€˜varianceâ€™ (harmful effect of NoIsE)
Ã˜ Preserve â€˜meanâ€™ or even allows â€˜adjustmentâ€™ of mean-on-target
Ã˜ The definition of Control Factors is that its levels can be set easily and without incurring additional cost
Ã˜ While concentrating on main function (improvement), it also measures â€˜side effectsâ€™ to make sure that no â€˜newâ€™ disadvantage appears
 Trends of Evolution and Taguchi Method
(a) Identify and solve tomorrowâ€™s problem
Ã¨ Taguchi Method is an R&D method but it can and does include NoIsE from future stages like
(b) 4-Stages of Evolution
(ii) Selection and improvement of parts
(iii) Dynamization of parts
(iv) Self-development of parts
(i) Taguchi Method is not used concept design stage
(ii) Taguchi Method is ideal for improvement of parts
(iii) Taguchi Method continues to be used in optimizing â€˜modifiedâ€™ or â€˜dynamizedâ€™ systems
(iv) Taguchi method is not used in this stage. In fact, it goes exactly in the opposite direction – it is suggested that all feedbacks be removed and Taguchi Method optimizes individual blocks. Feedbacks are restored back again. This may well be an area to benefit from a more comprehensive investigation into the best combination of the two approaches.
 Utilization of Resources and Taguchi Method
Identification of resources (â€˜anything in or around the system not being used to its maximum potentialâ€™) is a powerful TRIZ strategy for solving problems. A typical application of the resources part of the method might typically comprise:-
(a) Identification of unused or inefficiently-used resources
(b) Exploration of how to make full utilization of system resources and Taguchi Method
(i) Substance Resources (system, sub-systems and surrounding/super-system)
(ii) Energy Resources (mechanical, thermal, electrical, chemical, gravity etc)
(iii) Space Resources (in/around the system/sub-systems/super-system)
(iv) Time Resources (before/during/after the function is performed in system/sub-systems)
(a)Ã¨ Taguchi method aims at optimizing
(i) Existing equipment
(ii) Available raw material
(iii) Available manpower
(a)Ã¨ Taguchi Method determines which of the resources contribute dominantly to the â€˜varianceâ€™ recommends â€˜Tolerance Designâ€™. The quality of the dominant resource is selectively improved.
(b)(i) SubstancesÃ¨ Usually, the system/sub-system resources are used as Control Factors (if levels are easy to set without incurring expenses). The resources of â€˜environment or surroundingsâ€™ are usually declared as â€˜NoIsEâ€™ factors (as controlling these is expensive). Taguchi Method determines which of the resources contribute dominantly to the â€˜varianceâ€™ reduction as well as towards making the process ROBUST against the variation in the environment.
(b)(ii) EnergyÃ¨ Energy transformations are involved in all â€˜functionsâ€™ whether â€˜usefulâ€™ or â€˜harmfulâ€™! The aim of Taguchi experimentation is to â€˜minimizeâ€™ the energy required for useful function such that there is no or little â€˜excessâ€™ energy to result in â€˜harmful effectsâ€™.
* so, in essence, Taguchi Method aims and achieves â€˜bestâ€™ energy utilization.
(b)(iii) SpaceÃ¨ In a batch process, the effect of NoIsE is felt differently at different â€˜spaceâ€™ locations (averaged over the entire process time). Usually, putting samples at different â€˜spaceâ€™ points captures the NoIsE : in x-, y- and z- directions. The optimized process will thus minimize the â€˜varianceâ€™ over the entire sample lot. Space resource is used very effectively to make the process robust.
(b)(iv) TimeÃ¨ in a continuous process, the effect of NoIsE is felt differently at different â€˜timesâ€™ (averaged over the entire process line). Usually, taking samples at different â€˜timeâ€™ points captures the NoIsE : 1st, 5th and 15th min. The optimized process will thus minimize the â€˜varianceâ€™ over the entire sample lot. Time resource is used very effectively to make the process robust.
| The 8-steps of Taguchi Method||The 9-steps in ARIZ-85C|
|Step 1 : Identify the main function, the side effects and failure mode(s)|| Step 1 : Identify and Formulate the problem
Ã˜ Factor-Effect plots clearly show Contradictions
Ã˜ â€œSide effects and Failure modesâ€ is similar to â€œintensify contradictionsâ€
|Step 2 : Identify the NoIsE factors, the testing conditions (to capture the effects of NoIsE)||Step 2 : Make S-Field Models of the system parts that have problem
Ã˜ Include NoIsE as harmful action in the S-Field model
|Step 3 : Identify Quality Characteristics (more than one), and objective functions (for each)||Step 3 : Formulate an Ideal final result (IFR) and define ideality
Ã˜ S/N ratio: measure of Ideality
|Step 4 : Identify the Control Factors (some correlating strongly with NoIsE) and their Levels||Step 4 : List of the available resources (of the system, subsystems and the super-system)
Ã˜ Control Factors do reflect resources in equipment, raw materials and manpower
|Step 5 : Select Orthogonal Array||Step 5 : Look into database of examples and find an analogous solution|
|Step 6 : Plan experiments based on OA, include NoIsE during experiments and measure quality characteristics (as well as side effects)||Step 6 : Resolve Technical or physical contradiction by using inventive or separation principles
Ã˜ Factor-Effect plots only point out the Contradictions, but do not help eliminate
|Step 7 : ANOVA Analysis, Factor-Effects Plots, Predict best Control Factor Levels and Best Results||Step 7 : Start with S-Field model to generate solution concepts using Standards/ Effects
Ã˜ Do not eliminate the NoIsE, only its effects : make it ROBUST
|Step 8 : Confirmation experiments (repeat many times), verify additivity, match with predicted results Ã adopt new settings||Step 8 : Implement solutions by using only the free available resources of the system
Ã˜ Best settings of Control Factors imply optimum utilization of resources
|Step 9 : Analyze the modified system to verify that no new drawbacks appear
Ã˜ Similar to Confirmation experiments
| Taguchi Conceptsnot yet used in TRIZ||
|(i) Almost all energy transformations in nature are highly non-linear
Ã¨ Taguchi method exploits these non-linearities
| Ã TRIZ has not yet exploited non-linearities
Ã S-Field models have no way of showing the non-linearities
|(ii) There is a large interaction between Control Factors and NoIsE Factors
Ã¨ Taking log form of objective function converts it (the objective function) into an additive function of Control Factors
| â€œold jungle sayingâ€
â€œwhat can be shown, can not be usedâ€
â€œold jungle sayingâ€
|(iii) â€œVarianceâ€ was recognized as the â€œrootâ€ cause of all â€œQuality Lossâ€. In fact, â€œQualityâ€ was defined in terms of â€œVarianceâ€ (and the â€œmeanâ€ was taken out of the definition by coining a new term â€œQuality Loss After Adjustmentâ€ that implicitly assumes that we know how to â€œputâ€ the mean-on-target.|| Ã â€œContradictionsâ€ have been given the â€œrootâ€ status in TRIZ
Next come the
(i) partial or inefficient useful action
Obviously, TRIZ can â€˜equateâ€™ the concept of â€œelimination of harmful effectsâ€ to â€œreduction in Varianceâ€ and concentrate on this rather than the improvement of partial or inefficient useful action
In the very simplest terms, the link between TRIZ and Taguchi comes in the interface between having the idea and turning into a robust reality. TRIZ continues to be unique in itâ€™s ability to help problem solvers generate good solution ideas (all other methods feature the â€˜insert miracle hereâ€™ moment when it comes to the part of the systematic problem solving process that involves creation of ideas); Taguchi has near similar uniqueness when it comes to transforming the idea into effective outcome. Links between the two methods have been explored before (3), but, we hope weâ€™ve begun to demonstrate here, there is still much ground to be covered before the two methods are generating the synergistic benefits we firmly believe are there waiting to be taken. We will return, in particular, to the implications and opportunities for benefit when TRIZ exploits non-linearities in a future article.
- Fowlkes, W.Y., Creveling, C.M., â€˜Engineering Methods for Robust Product Design: Using Taguchi Methods in Technology and Product Developmentâ€™, Addison Wesley Publishing Company, 1995.
- Mann, D.L., Stratton, R., â€˜Physical Contradictions and Evaporating Clouds (Case Study Applications of TRIZ and the Theory of Constraints)â€™, TRIZ Journal, April 2000.
- Terninko, J., Zusman, A., Zlotin, B., â€˜Systematic Innovation: An Introduction To TRIZâ€™, St Lucie Press, 1998.