By: P. R. Apte and D. L. Mann
Introduction
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:
[1] Taguchi Factor Effect Plots and their relation to TRIZ Physical and
Technical Contradictions
[2] S/N Ratio (Objective Function) and relations to
- “Ideal Final Result (IFR)”
- “partial/inefficient useful action”
- “elimination of harmful effects”
[3] Taguchi Methods in an integrated (Define, Select, Solve and Evaluate)
TRIZ Process
[4] IFR goals/characteristics and their achievement through Taguchi Method
concepts/tools
[5] Trends of Evolution and Taguchi Method - Identifying and solving
tomorrow’s problems.
(deploying Taguchi NoIsE for future stages like manufacture, Operation/use, and
Aging/drift)
[6] Utilization of Resources and the Taguchi Method
[7] The 8-steps of the Taguchi Method and ARIZ
[8] Taguchi Concepts not 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.
[1] Factor Effect Plots in relation to TRIZ Contradictions
(a) Technical Contradictions :
Referring to Fig 1,
A2 à Nominal value of Control Factor A
If A2 à A1, then QC2 improves but QC1 worsens
If A2 à A3, then QC1 improves but QC2 worsens
Fig 1. Factor Effect Plots for 2 Quality Characteristics, QC1
and QC2
This points clearly to a “Technical Contradiction” 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 à A1 or A2 à
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 “Physical Contradiction”. 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.
[2] 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 “robust” and
“low cost”. For both Taguchi and TRIZ the Ideal Value is ¥
(infinity).
(a) Since the ideal value is ¥ (infinity), the
primary importance is shifted from “improving mean” (as in the conventional
approach) to “reduction in Variance” to 0 (zero).
(b) Identify an “adjustment factor” that has little or no effect on the
variance but has a large effect on the mean
à
use the adjustment factor to “put” the “mean-on-target”
COMPARISON with TRIZ :
(a) Objective functions of Taguchi Method, also called ‘Signal-to-Noise
Ratios’ (S/N Ratios). While these objective functions bear little relation
to the concept of ‘define the IFR and work back from it’ found in TRIZ, they
are similar to the “Ideal Final Result” 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) ‘improving’ partial or inefficient useful action
(ii) ‘eliminating’ 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 “root” cause (of harmful action)
2. Eliminate the harmful “action” itself
3. Eliminate the “effects” of the harmful action
The “root” cause identification and elimination is ‘idealistic’ and so is
removal of “action’ itself. Many times, this changes the S-Field model or
its implementation completely.
So, we look for more practical approach for eliminating the “harmful
effect” 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.
[3] “4-stage TRIZ Process”: (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
Ø
Identify contradictions from the factor effect plots (as shown
in section [1] earlier)
Ø
Use ‘useful action’ as a Quality Characteristic (as
Larger-the-Better type) and maximize it
Ø
Include the ‘harmful action’ as NoIsE during the experiments.
Achieving ‘insensitivity’ to NoIsE thus makes the process ROBUST.
Ø
Taguchi method grades solutions in the following way
(i) primary purpose à to make the
process ‘insensitive’ to NoIsE
(ii) secondary purpose à to identify
‘adjustment parameter’ to put the mean-on-target
(iii) tertiary purpose à to identify
settings that will improve 2 or more characteristics.
Ø
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.
Ø
Taguchi method helps
(i) improve (inefficient) useful action à
as larger-the-better characteristics
(ii) eliminate the ‘effects’ of harmful action (NoIsE)
à make it ROBUST
(iii) give a ‘measure’ of the contradiction à
from Factor effect plots
(both Technical and Physical)
Ø
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.
[4] 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
Ø
Reduce ‘variance’ (harmful effect of NoIsE)
Ø
Preserve ‘mean’ or even allows ‘adjustment’ of mean-on-target
Ø
The definition of Control Factors is that its levels can be set
easily and without incurring additional cost
Ø
While concentrating on main function (improvement), it also
measures ‘side effects’ to make sure that no ‘new’ disadvantage appears
[5] Trends of Evolution and Taguchi Method
(a) Identify and solve tomorrow’s problem
è
Taguchi Method is an R&D method but it
can and does include NoIsE from future stages like
(i) manufacture
(ii) operation/use
(iii) aging/drift
(b) 4-Stages of Evolution
(i) Synthesis
(ii) Selection and improvement of parts
(iii) Dynamization of parts
(iv) Self-development of parts
è
(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 ‘modified’ or
‘dynamized’ 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.
[6] Utilization of Resources and Taguchi Method
Identification of resources (‘anything in or around the system not being used
to its maximum potential’) 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)è Taguchi method aims at optimizing
(i) Existing equipment
(ii) Available raw material
(iii) Available manpower
(a)è Taguchi Method determines which of
the resources contribute dominantly to the ‘variance’ recommends
‘Tolerance Design’. The quality of the dominant resource is selectively
improved.
(b)(i) Substancesè Usually, the
system/sub-system resources are used as Control Factors (if levels are
easy to set without incurring expenses). The resources of ‘environment or
surroundings’ are usually declared as ‘NoIsE’ factors (as controlling
these is expensive). Taguchi Method determines which of the resources
contribute dominantly to the ‘variance’ reduction as well as towards
making the process ROBUST against the variation in the environment.
(b)(ii) Energyè Energy transformations
are involved in all ‘functions’ whether ‘useful’ or ‘harmful’! The aim of
Taguchi experimentation is to ‘minimize’ the energy required for useful
function such that there is no or little ‘excess’ energy to result in
‘harmful effects’.
* so, in essence, Taguchi Method aims and achieves ‘best’ energy
utilization.
(b)(iii) Spaceè In a batch process, the
effect of NoIsE is felt differently at different ‘space’ locations
(averaged over the entire process time). Usually, putting samples at
different ‘space’ points captures the NoIsE : in x-, y- and z- directions.
The optimized process will thus minimize the ‘variance’ over the entire
sample lot. Space resource is used very effectively to make the process
robust.
(b)(iv) Timeè in a continuous process,
the effect of NoIsE is felt differently at different ‘times’ (averaged
over the entire process line). Usually, taking samples at different ‘time’
points captures the NoIsE : 1st, 5th and 15th min. The optimized
process will thus minimize the ‘variance’ over the entire sample lot. Time
resource is used very effectively to make the process robust.
|
[7] 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
Ø Factor-Effect plots clearly show
Contradictions
Ø “Side effects and Failure modes” is
similar to “intensify contradictions” |
|
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
Ø 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
Ø 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)
Ø 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
Ø 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
Ø Do not eliminate the NoIsE, only
its effects : make it ROBUST
|
|
Step 8 : Confirmation experiments (repeat many times), verify
additivity, match with predicted results à
adopt new settings |
Step 8 : Implement solutions by using only the free available resources
of the system
Ø Best settings of Control Factors
imply optimum utilization of resources |
| |
Step 9 : Analyze the modified system to verify that no new drawbacks
appear
Ø Similar to Confirmation experiments
|
|
[8] Taguchi Concepts not yet used in TRIZ |
TRIZ
|
(i) Almost all energy transformations in nature are highly non-linear
è Taguchi method exploits these non-linearities |
à TRIZ has not yet exploited non-linearities
à S-Field models have no way of showing the non-linearities |
(ii) There is a large interaction between Control Factors and NoIsE
Factors
è Taking log form of objective function converts it (the
objective function) into an additive function of Control Factors
à Allows
o Straight forward calculations
o Ease in identifying non-additivity
|
“old jungle saying”
“what can be shown, can not be used”“old jungle saying”
“what can not be measured, can not be improved”
|
| (iii) “Variance” was recognized as the “root” cause of all “Quality
Loss”. In fact, “Quality” was defined in terms of “Variance” (and the “mean”
was taken out of the definition by coining a new term “Quality Loss After
Adjustment” that implicitly assumes that we know how to “put” the
mean-on-target. |
à “Contradictions” have been given the “root” status in TRIZ Next come the
(i) partial or inefficient useful action
(ii) Eliminate harm (‘cause’, ‘action’, ‘effect’ )
Obviously, TRIZ can ‘equate’ the concept of “elimination of harmful
effects” to “reduction in Variance” and concentrate on this rather than
the improvement of partial or inefficient useful action
|
Final Thoughts
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 it’s ability to help problem solvers generate good
solution ideas (all other methods feature the ‘insert miracle here’ 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 we’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.
References
- Fowlkes, W.Y., Creveling, C.M., ‘Engineering Methods for Robust Product
Design: Using Taguchi Methods in Technology and Product Development’, Addison
Wesley Publishing Company, 1995.
- Mann, D.L., Stratton, R., ‘Physical Contradictions and Evaporating Clouds
(Case Study Applications of TRIZ and the Theory of Constraints)’, TRIZ Journal,
April 2000.
- Terninko, J., Zusman, A., Zlotin, B., ‘Systematic Innovation: An
Introduction To TRIZ’, St Lucie Press, 1998.
