Linking Contradictions and Laws ofEngineering System Evolution withinthe TRIZ Framework

Denis Cavallucci, François Rousselot andCecilia Zanni

Tools and methods developed during the era of quality and optimization have shown theirlimitations and become inappropriate in the context of the requirements of innovation. Nowa-days the need to rebuild design practices in enterprises is strongly felt both in terms of humanskills and methodological expertise. In part, a way to face the innovation era’s difficulties hasbeen provided through the theory of inventive problem solving. This theory represents asignificant breakthrough in driving problem statement and solving in a direction that isexpressed through the idea that technical systems are driven by objective laws. A secondpostulate concerns the notion of contradiction, but so far only few contributions haveaddressed the relations between laws and contradictions. This paper, through a qualitativeapproach, presents a solution to this limitation and proposes a possible use of laws within thechoice of the appropriate conflicting pair, prior to the use of any TRIZ solving techniques.Tests to observe the impact of the proposed approach were conducted in a French engineering‘grande école’ during three semesters with 180 engineers. The contribution of this paper istwofold. On the one hand, there is a theoretical contribution to the theory of inventiveproblem solving. In addition, the proposed method offers especially TRIZ practitioners newways for problem understanding and problem formulation.

Introduction

Since the socio-economic paradigm of thepast seven decades has turned towards

enhancing value through statistically balanc-ing procedures, elaborated engineering designtheories have been (and still are) mainly‘optimization-driven’. When observing actualapproaches empowered by industries, six-sigma-like methodologies are still employedand have proven their financial usefulness inmost cases (Thirunavukkarasu et al., 2008).Nevertheless, an increasing number ofresearchers are now addressing the innovationparadigm and are actively trying to contributeto the elaboration of a new know-how whichdiffers from established optimization theories(Moehrle, 2005). Some research is conductedwithin the context of the Theory of InventiveProblem Solving (TRIZ). This theory proposesa framework which is based on three majoraxioms (Cavallucci & Khomenko, 2007):

• First axiom: Technological systems do notevolve at random but according to objectivelaws of evolution. These laws do not dependon humans. They should be observed, for-mulated and used in order to develop effi-cient methods of problem solving.

• Second axiom: Technological systems do notevolve at random but in the course of over-coming contradictions. In order to come upwith a breakthrough idea we should find away to overcome contradictions.

• Third axiom: A specific problem must besolved in accordance with restrictions con-cerning the specific problem situation and aview to the peculiarities of every specificcase, and can thus not be solved generally. Arobust solution is a solution that involves assmall a number of new resources as possible.

The Laws of Engineering Systems Evolution(LESE) constitute the first of TRIZ’s mainaxioms. By means of observing expert applica-tions of TRIZ, teachers/professionals provide

LINKING CONTRADICTIONS USING THE TRIZ FRAMEWORK 71

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their students/clients with some theoreticalelements related to LESE as well as vivid illus-trations concerning their relevance in variouscases alongside anecdotes of actual situationsin which they were helpful in unlocking/orienting a study in the appropriate direction.

If we look carefully at what has been pro-posed and published on the subject of LESE, itmight even be understood that Altshuller’sintention was not to build upon the theoreticaldescription of these laws a useful operationalway to benefit from them. But although thereare some interesting theoretical explanationsassociated with practical attempts (Salamatov,1991), most newcomers to TRIZ realize, oncethey have dedicated some time to analysingthe TRIZ literature and other related material,that none of the available tools or techniquespropose a practical utilization of LESE.

In this paper we propose a bridge betweenLESE and contradiction thinking, and link thisbridge to the design situation (represented bypositions on S-curves). As a result we willanswer the question how to order contradic-tions as starting points for further instruments.Therefore, first we introduce nine LESE,grouping them in a static, dynamic and kine-matic set. Second, we discuss thinking in con-tradictions as a major element of TRIZ. Third,we introduce evolution hypothesis to connectLESE with contradiction elements and param-eters. Fourth, we show how this connectionmay be used to prioritize contradictions in adesign situation. Fifth, we present results froma test of the suggested process. Finally, we con-clude with some remarks.

The Notion of Law from a TRIZPoint of View

Laws Seen from Altshuller’s Viewpoint

The first occurrence of the term ‘law’expressed from Altshuller’s viewpoint (as it is

publicly known) can be found in his article ‘Anessay on the psychology of inventiveness’from 1956 (Altshuller & Shapiro, 1956). In thistext, references were made among others toMarx and Rubinstein; furthermore, the limita-tions of current visions such as enlightenmentand trial-and-error procedures were stated.The conclusions clearly expressed the visionthat technical systems were evolving accord-ing to laws and that these laws should beutilized in designing methods related toinventiveness. Here, ‘usability’ was putforward in the final sentence which containsthe following perspective ‘the objective of thepsychology of inventiveness is to be translatedinto practice: its inherent laws must be usedduring the development of scientific method-ology to work on inventions’.

There are nine laws which characterizethe evolution of technical systems. Each ofthese laws plays a particularly significantrole at a given stage of its lifetime. A detaileddescription of the nine laws can be foundin Salamatov (1991). In our paper we willuse the structure presented in Figure 1 tointroduce them, as well as the followingdefinitions:

Law 1: Completeness: Any technical systemresults from a synthesis of several parts into asingle whole. In order to be viable, the maincomponents of this technical system have to bepresent and perform within a minimalworking efficiency.Law 2: Efficiency: In order to make a systemviable, the energy flow that drives it must passthrough all of its main components.Law 3: Harmonization: In order to maximizea system’s performance, all of its main com-ponents must be either coordinated orun-coordinated.Law 4: Ideality: During its evolution thetechnical system tends to improve the ratiobetween its performance and the expensesrequired to attain this performance.

TransitionLaw 1: System

Completeness

Law 2 : Efficiency

Law 3 :

Harmonization

Law 5 :

Irregularity

of parts

evolution

Dynamisation

Law 7 : Towards microlevel

Law 6 : Towards supersystem

Law 8 :Through control

Law 9 : Through added S-Field

Static laws Kinematic laws

Dynamic laws

Law 4 : Ideality

Figure 1. Structure of the Nine LESE

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Law 5: Uneven development: Components oftechnical systems evolve irregularly. The morecomplex the system, the more irregularitiesone gets. These irregularities lead to the birthof new contradictions preventing its evolu-tion.Law 6: Transition to the super-system: In thecourse of their evolution, technical systemsmerge to constitute bi- and poly-systems. Inthe future, the system pursues its evolution asa part of the super-system.Law 7: Transition to the micro-level: The evo-lution of the ‘tool’ element within a givensystem begins on the macro-level and inclinestoward the micro-level. This evolution isbrought about by the advantages of usingproperties of dispersed materials and particlesof physical fields.Law 8: Control through dynamization: Inorder to improve their performance, rigidsystems should become more dynamic. Bymeans of dynamicity we intend: evolutiontowards more flexible and rapidly changingstructures, adaptability to changes of workingconditions and requirements of the environ-ment.Law 9: Control through added substance-fieldsystems: In order to improve their perfor-mance, systems should become more control-lable. By means of controllability we intend tofollow elementary rules expressed in theinventive standards.

Recently, Vladimir Petrovich has publishedseveral interesting articles in this respect(Petrovitch, 2005, 2006). He features a morestructured way of presenting the laws com-pared with Altshuller’s traditional divisioninto ‘static’, ‘kinematic’ and ‘dynamic’.

Attempts at Understanding

Ever since the TRIZ Body of Knowledge firstappeared in the western world (in the early1990s), LESE have attracted the attention ofmany industrialists, researchers and consult-ants. After the reception of whatever was pub-licly accessible, quite a few interpretationswere expressed in which various terms quali-fying the laws can be found, such as ‘trends’,‘logics’, ‘tendencies’, ‘patterns’. What is mainlyargued by several authors, either in theirarticles, in their public presentations or invarious web forum discussions, is that theterm ‘law’ should be carefully employed;moreover, the use of LESE from description toprescription should not lead to a ‘naive’ formof forecasting (Gavin & Mandal, 2003).

In 1999, one of the authors undertook anattempt to share Altshuller’s findings withscientific communities regarding the laws

(Cavallucci & Weil, 2001) through the postulatethat they could play a role in orienting design-ers in projects with inventive aims. After thispublication, some attempts at utilization bymeans of software tools arose (Mann, 2003)and met with positive feedback in education,modest success in industry and rejection inscientific communities. Consequently, onecannot help wondering, whether Altshuller’slaws may not still be underused at the presenttime.

TRIZ’s Contradiction Model

After having introduced the LESE, which rep-resent evolution paths of technical systems, wewill now discuss TRIZ’s contradiction model,which represents the core of the problem situ-ation. The formulation of a contradiction com-prises three types of components: elements,parameters, values (partially interpreted fromKhomenko & Ashtiani, 2007).

Elements (E) are components of a system.From a syntactical point of view, they caneither be subjects, names, nominal groups orcomplements to an object. (Example: thehammer drives in the nail; E = hammer). Thecharacter of an element can (within themeaning of TRIZ) constantly change its sys-temic level according to the description whichis given by it. Thus ‘the hammer inserts thenail’ can become ‘the anvil inserts the nail’from the point of view of another expert. In thesecond case, E = anvil. For a third ‘the maninserts the nail’, etc.

Parameters qualify elements in giving thema specificity which is the translation of explicitknowledge from the observed field. Thus,parameters are mainly names, complements toobjects or adverbs, the form of their expressionis multiple, sometimes contradictory whenexpressed by different experts. They aredivided into two categories:

– Action Parameters (AP): here the designerhas the capacity to modify them (thedesigner can decide to design an anvil oflarge volume or small volume, in this casevolume = AP).

– Evaluating Parameters (EP): having thecapacity to evaluate positive and negativeaspects resulting from the designer’s choice.The consequence of designing an anvil witha large volume is an increased facility ofinserting the nail; in this case facility ofinsertion = EP).

An interesting aspect we observed whentrying to identify whether a parameter was‘active’ or ‘evaluating’ is that an AP hastwo logical directions pointing to interesting

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outcomes, while EP possesses a single logicaldirection (the other seems absurd). As inour example, we can easily imagine thatthe ‘volume of the anvil’ is of interest inboth cases, whether it is ‘smaller’ or ‘larger’,therefore it represents an AP. In contrast, the‘facility of insertion’ seems interesting only if‘improved’, as it would be nonsensical to wishfor a decrease in the ‘facility of insertion’, thisrepresents an EP.

The values are mainly adjectives employedto qualify a parameter (the volume of theanvil must be important; in this caseimportant = Va). Let us note that the funda-mental aspect of the concept of contradiction(when elucidated at the physical level) lies inthe qualitative opposition of the values and thefact that if V is in a state that involves positiveaspects, then it is essential to investigate theopposite of V (Va) (its antonym in terms oflinguistics) to highlight and to prove the con-

tradictory aspects of the analysis. Thus, ananvil of a large volume involves a facility ofdriving and an anvil of a small volumeinvolves a facility of handling (in this caselarge = Va and small = Va).

Finally, a last step to highlight the concept ofcontradiction is to make certain that the oppo-site assertions are also true.

• Will a large mass of anvil invariably involvea minor facility of handling?

• Will a low mass of anvil invariably involve aminor facility of driving?

If the answer is yes in both cases, we canconsider the contradiction valid (TCx). TCsrepresent a partial modelling of knowledgeassociated with the description of the prob-lems involved in the evolution of the hammer.

Subsequently, the scheme shown in Figure 2is used for a graphical representation of con-tradictions.

Evolution Hypotheses as a Bridgebetween LESE and Contradictions

In the course of our analysis of the TRIZ Bodyof Knowledge, we formulated a statement onwhere, among all other tools, techniques andknowledge bases, LESE are tacitly or explicitlypresent (see Table 1). It is clear that, until now,there has been a missing link between LESEand contradictions.

A first objective of this study is to considerthat once a group of contradictions has beenrevealed, our initial question may be the fol-lowing: ‘How can contradictions be ordered inFigure 2. Generic Contradiction Table

Table 1. Tacit or Explicit Presence of LESE within the TRIZ Body of Knowledge (see also Altshuller, 1984,1991, 1999; Pinyayev, 1990)

Element fromthe TRIZ Body of

Knowledge

Type ofLaws

Present

Where

Tools Multi-screen analysis Tacit From past to present and frompresent to future

Matrix None

Methods Substances-field modelling Tacit Within the transformation logic frominitial situation to solution model

ARIZ-85C Tacit Through Inventive Standards use,and IFRs formulation

Knowledgebases

System of inventive standards Explicit Through the structure of classesand sub-classes

Inventive principles NoneSeparation principles None

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accordance with the fact that they represent anopposition to a specific law?’ The expected resultis the ability to ensure that the treated contra-diction (consequently being solved using TRIZtools) is standing in the way of a specific law.

According to our observations, parameters(the components of contradictions) canemerge based upon five distinct situations:

• when expressed by experts to describe aproblematic situation;

• when extracted from a text in which theproblematic situation is described;

• when disclosing the parametric character ofa system evolution (in multi-screen schemedescription);

• when discussing the maturity of the studiedsystem with regard to LESE;

• when populating incompletecontradictions.

Each of these five phases of problem analysissituations presents an opportunity for disclos-ing parameters and compiling them in a listthat can be used to populate contradictions. Afirst comment is that the fourth item on the listappears to be interesting as regards ourproblem, since in the course of the LESE obser-vation stage, parameters can emerge, andtacitly imply a link with the law from whichthey emerge. But these situations are not sys-tematic and the four other points are still notclearly showing the link.

There is need for an intermediary compo-nent during problem expression, whose role isto have, in its cognitive resonance, the capacityto be extracted from laws and within its struc-ture, when pronounced, to provoke the emer-gence of a parameter in the expert’s mind.Based on this statement we draw the hypoth-esis that a simple sentence, namely an ‘evolu-tion hypothesis’ (EH), takes the followingform:

If [technical system] is evolving in [lawdirection] then we can draw a coherentpicture of its probable future saying that: a[technical system] [describe its probablefuture state if one of its characteristic is logi-cally improved].

For example, the observation of Law 2 (energyconductivity) for a screwdriver will consist indiscussing how the energy flow within thesystem’s components may not be lost in orderto attain its main function of use. If one sup-poses that there is an ideal unscrewing posi-tion and that, whenever the user deviates fromthis ideal position, the screwdriver slips,wasting energy, we can derive the followingevolution hypothesis:

EH: A [screwdriver] that [never slips fromits ideal axial position when unscrewing].

As a result of this EH, a link or the emergenceof a new evaluating parameter (EPn) ‘Capacityto remain coaxial with screw axis’ can be for-mulated and listed as a parameter linked tolaw 2. The consequence will be that each TCpossessing this parameter (among its two EPs)will be correlated to law 2.

To summarize, an evolution hypothesis(EH) can be seen as a thinking component tofacilitate links between contradictions andlaws. EHs are literal interpretations of givenlaws, expressed in the form of a sentence. Thissentence is the result of a possible direction thetechnical system may take in his evolution. Thesemantic interpretation of a law will obviouslybring out more detailed and specified ele-ments (parameters, values, new elements) thanthe generic expression of a law (its postulate).By observing and analysing how these ele-ments may refer to a list of parameters aimingat evolving in an appropriate direction, it ispossible to establish a semantic proximity uni-fying a parameter to a specific EHx (Figure 3).

Linking Laws to the DesignSituation

Assuming that a list of contradictions has beenbuilt according to the frame TCn [APx; EPn;EPm], for ranking contradictions according tolaws, we need to organize the arrangement oflaws with regard to the design situation. Wenow encounter a problem that was alreadydisclosed by Altshuller (1984) where heunderlines the importance of identifying thematurity position along the ‘S’ curve. Thiskind of positioning is one of the most challeng-ing subjects of research in industry today. Thefindings of this research is aimed at reliablyindicating the maturity of a technology andforecast in a narrowest spectrum of variantswhere the studied technology is movingtowards (Kucharavy & De Guio, 2005).

This is not included in the scope of ourcurrent research, but the necessity to move

Is related to Is an interpretation of

Evolution HypothesisParameters Laws

EP1

EP2

EP3

...

EH1.1

EH2.1.2

EH2.2.1

...

Law 1

Law 2

Law 9

Law ...

EP z EH3.z

Figure 3. Contradictions and Laws Linkedthrough Evolution Hypothesises

LINKING CONTRADICTIONS USING THE TRIZ FRAMEWORK 75

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forward induces us to simplify the approach(even if we assume introducing a significantdose of imprecision) by inviting decisionmakers, along with the whole study team, tolocate both present and future situations intu-itively on two ‘S’ curves (based on their exper-tise regarding the treated subject).

In Figure 4, the present cursor (P) repre-sents the current situation. Prior to this, anapproximation using the approach developedin Altshuller (1984) can be used to lead man-agement, marketing and R&D representativesto an agreement on the current situation.

The future cursor (F) represents the aim ofthe study. It clearly indicates whether thecompany aims at developing a totally newconcept or at effecting a slight improvement.Usually, the ‘S’ curve positioning is supposedto assist in such decisions, but in our case,again, the strategy of the company shouldprovide R&D with a clear notion of ‘howimportant’ it is to accept the leap, even thoughbig companies often launch several alterna-tives (from slight improvements to pureresearch) anyhow, in order to move on care-fully while protecting possible inventive solu-tions, representative of a significant technicalleap in their technical system’s evolution.

In order to attribute a specific importance tocertain laws when a specific configuration ofcursors P and F is given, we have tried to inter-pret Altshuller’s texts as well as chapter 5 ofSalamatov’s book (Salamatov, 1991). Accord-ing to our understanding of this literature, thefollowing coefficient pre-set settings weredrawn up:

• Case 1: � P � ZA ∧ F � ZA \ L1 = 4; L2 = 3;L3 = 2; L4 = 2; L5 = 1

• Case 2: � P � ZA ∧ F � ZB \ L2 = 4; L3 = 3;L4 = 2; L5 = 1

• Case 3: � P � ZA ∧ F � ZC \ L2 = 4; L3 = 4;L4 = 2; L5 = 1

• Case 4: � P � ZB ∧ F � ZB \ L3 = 4; L2 = 4;L4 = 2; L5 = 1

• Case 5: � P � ZB ∧ F � ZC \ L3 = 4; L2 = 3;L4 = 2; L5 = 1

• Case 6: � P � ZC ∧ F � ZC \ L3 = 4; L4 = 2;L5 = 2

• Case 7: � P � ZA ⁄ ZB ⁄ ZC ∧ F � ZA+1 ⁄ZB+1 ⁄ ZC+1\ L6 ∧ L7 ∧ L8 ∧ L9 = 2

The aim of each of these statements is not toindicate an accurate assumption, but to focusattention on one specific law (or set of laws)rather than another, as suggested in Cavallucciand Weil (2001). The arrangement of lawsaccording to importance will also have animpact on each parameter they are associatedwith.

In order to be able to modify these pre-settings, the coefficient associated with eachlaw (a value from 0 to 5) has to remain variable.From the position of each coefficient, we havededuced a multiplying factor dedicated toraising the importance of certain parameters.

This multiplication has the objective ofinfluencing the weight of a contradictionincluding these parameters. The contradic-tion’s weight is therefore as follows:

Value of TCn Coef. APxWeight EPn Max. coef laws

Weight E

=×(

+ PPm Max. coef laws× )where,

Max. coef laws: correspond to the maximumvalue (from 0 to 5) of the coefficient attributedto laws in relation with a given parameter,Coef. APx: correspond to the influence anactive parameter switched from Va to �Va hason its associated EPs to provoke their evolu-tion towards a desired state.Weight EPn,m: the associated value attributedto a given EP corresponding to a company’spriority. Here, the notion of ranking EP can beused in order to classify all EPs from the list.

Each EP from the list of EPs, that has beenextracted from the contradiction formulationand MSA and linked to LESE through EHs,will then respectively be impacted by the exist-ence of a link and its importance. This impor-tance is directly associated with what hasstrategically been established in the ‘S’ curveobservation and the predominant law withinthe specified context.

The formula which attributes a value to aTC remains a qualitative approximation, butallows us to postulate that LESE (from the first

Time

Any o

bserv

ed p

erf

orm

ance

Zone A

Zone B

Zone C

Significant Leap resulting in an important functional change

P

F

Generic « S » curve of actual system

Generic « S » curve of future system

Figure 4. Two Cursors along Two Generic ‘S’Curves

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axiom) influence the ranking of contradictions(from the second axiom).

Towards a Procedure for OperatingSimple Problem Convergence toSolution Concepts

In this section we would like to summarizeour proposed framework by means of an algo-rithmic representation (Figure 5). This repre-sentation also includes other basic TRIZelements that are not described in this articlebut on which further information can be foundin Altshuller et al. (1989).

Steps 1 and 2 are supposed to reduce theobserved system to its essentials. Usuallyreduced down to a single but vital one, a mainuseful function can only be described by theset of elements forming an undividable groupfor delivering this function.

Step 3.1 is dedicated to a multi-screenscheme analysis, through the description ofevolving parameters (from past to present inall three lines of the systemic representation), afirst list can be established as well as a first setof EHs.

Step 3.2 can be either conducted in parallelwith or following step 3.1. This stage consistsin observing your potential system evolutionregarding each law (from 1 to 9). According tothese observations, a second set of EHs can beformulated and the first list of parametersenhanced by a second set of new ones (redun-dancies should be avoided).

Step 4 consists in merging EH1 and EH2,mostly because in our entire protocol oftesting, we have observed redundancies in EHformulation. As a result of this stage, a singlelist of EHs should be kept for the rest of thestudy while monitoring the relations betweenparameters and EH2 from step 3.2.

Step 5 consists in linking the two resultinglists (of parameters and EHs). Some links toprevious steps are to be conserved. New ones,linking all insulated parameters with one orseveral EHs, are to be created.

Step 6 is dedicated to contradiction synthe-sis. Taking into consideration the typology of acontradiction, contradiction templates are tobe filled with all available parameters from thelist. We have observed that many spaces in con-tradiction templates are vacant at first. It is nec-essary then to disclose the ‘missing’ elements(either parameters or values) in order to vali-date all contradictions completely.

Step 7 proposes weighting each contradic-tion according to the formula expressed aboveand choosing those to be involved in a solvingprocess.

Steps 8–11 are devoted to the use of TRIZmethods and tools. Depending upon indi-vidual skills developed in TRIZ learning andpractice, a proposed path regarding the use ofprinciples, standards or ARIZ is described inAltshuller et al. (1989).

Test of Procedure

In order to evaluate the relevance of ourapproach, represented in the form of ateachable and usable procedure (Figure 5), wetested it over three semesters with 180 engi-neering students in their fifth year (one semes-ter before graduation) from seven differentengineering departments (Civil Engineering,Topography, Mechanical Engineering, PlasticsEngineering, Mechatronics, Electrical Engi-neering, Building Services and Energy Conser-vation Engineering).

The syllabus of the course was divided intotwo sections:

• A series of seven theoretical 2-hour lessons(14 hours of lecture per student).

• A series of seven directed work sessions oftwo hours’ length, in which a simple objectfrom everyday life was chosen and studied(14 hours of directed work).

The evaluation of the directed work consistedin a vindication of choices made and resultsderived in a hearing presided over by a jury.Additionally, a written report giving feedbackon this work had to be handed in to the jury.Apart from a classical evaluation, this testintended to focus on the relevance of the utili-zation of laws. With a view to the appropriateinterpretation of laws regarding their study aswell as choosing (according to the approachdeveloped by us) a relevant set of contradic-tions to be solved using TRIZ techniques.

During the first semester, fundamentals ofTRIZ basic theory were taught (all topicsrelated to TRIZ and requisite for conductingthe directed work). An evaluation of the studyresults was undertaken and emphasizedseveral limitations:

• Out of 45 students and 17 project teams,only three teams addressed relevant contra-dictions properly (coherent with laws analy-sis). The remaining teams either completelyskipped the utilization of laws or totallyseparated the interpretation of laws fromtheir solving part. As a result, the theoreticalperiod only had a weak influence on theoverall study.

• During the second semester, the samemodel of course and directed work was

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1. State on technical

system boundaries

2. Elaborate tryptic

«Tool»-«MUF»-«object»

1st set of hypothesis (EH1.n)

First set of parameters

2nd set of hypothesis (EH2.x.n)

4. Merge hypothesis priorize

your list

5. Draw links between

hypothesis and preliminary

set of parameters

6. Build your contradictions

using

E-N-V scheme

7. Weight your

contradictions and point

which ones stands on the

line of a logical evolution

9. Summarize portrait of your solution model:? Which law(s) do I need to be in accordance with ?

? Which contradiction do I have to solve in priority ?

? What are the useful available ressources ?

? How do I describe the Ideality of my technical system ?

? Which standard/Inventive principle/way to solve

physical contradiction do I have as a recomendation ?

Set of parameters priorized upon hypothesis

Various interpreted Solution

Concepts11. Converge Solution Concepts into

Preliminary Solutions

Hypothesis of evolution

Priorized contradiction

List of ressources

Ideal studyed system

Appropriate knowledge base

10. Interpretation

3.1 Use multi-screen scheme analysis

3.2 Use Laws of engineering systems evolution

8.1 Extract Knowledge element from list of 11

ways to solve a physical contradiction

8.2 Extract Knowledge element from the use of

matrix and 40 inventive principles

8.3 Extract Knowledge element from the use of

system of Inventive Standards

List of hypothesis priorized

List of contradictions

Completed list of parameters

Priorized list of contradictions

Set of potential Separation Principles

Set of potential Inventive Principles

Set of potential Inventive Standards

Second set of parameters

Figure 5. Flowchart of the Proposed FrameworkMUF = main useful function, E-N-V-scheme = ‘element-name of the feature-value-scheme’.

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offered to 70 students forming 33 projectteams. In addition to this, the theoreticalcontent was restructured to introduce theidea of evolution hypothesis and its con-nection with other classical basic TRIZelements.

• After this, the evaluation showed that 12teams (out of 33) had obtained positiveresults in choosing a reduced set of appro-priate contradictions, based on laws inter-pretation. The others had not fully respectedthe proper procedure and consequently,laws – although interpreted – were used fora post-evaluation of solution concepts ratherthan for addressing a relevant contradictionprior to the solution process.

• During the final semester, 65 students and28 projects were involved in the test. Thistime we modified the process by introduc-ing an online java software application, TRI-ZAcquisition, for the conduct of directedwork sessions. This application was createdas a result of a formalized ontology of TRIZbasic concepts using protégé V2.0.

• Following this, 25 project groups proposeda relevant contradiction choice, using lawsinterpretation appropriately. The remainingteams skipped this step mostly due to timeresource limitations.

Discussion

Through the subject treated in this article wewanted to emphasize that LESE, as they wereelaborated and presented by Altshuller severaldecades ago, were aimed at being fully inte-grated into the scope of TRIZ’s theory usage.Up to now (as far as available written and pub-lished material goes), we have only foundcontributions which either aim at clarifyingexisting laws (descriptive contributions),re-interpreting these laws or trigger designers’imagination by means of utilizing these laws(using them along with pointed illustrations)to forecast a ‘brainstorm-oriented’ portrayal ofwhat the system is going to look like in thefuture.

This paper was aimed at proposing apossible use of LESE within the context ofcontradiction choice (ranking). The testsundertaken for the evaluation of the benefitsattached to our approach through threesemesters and with 180 engineering studentsassigned to 78 project teams have shown thatthe proposed formalism, when understoodand put into practice within a study, not onlymeets the initial target (a relevant use ofLESE) but also accelerates the mastering ofTRIZ basics, which it usually takes muchlonger to acquire. It is commonly acknowl-

edged that extensive periods of learning,combined with a considerable amount ofpractice, are required to be able to observe theresult of a relevant use of laws within a study.According to the accumulated data, weevolved from 15%, via 45% to an eventual75% in relevant use; this encourages us toimprove our approach and observe its pos-sible perspectives even further.

A first obvious limitation is that it is difficultto generalize the developed method, sincemany elements have been qualitatively evalu-ated by users, and thus there is a direct depen-dence upon the accuracy of their perceptions.But what we are targeting is at a crossroads ofhuman and engineering disciplines. If thesecond is easy to manage in terms of accurateformalism, the first is often qualified as‘science of the imprecise’ (Moles & Rohmer,1990). Further development will neverthelessbe aimed at reducing the zones of imprecisionwithin our approach.

A second point we have not yet clarified isthe fact that our tests were set within an edu-cational context. Although we were in thevery last period of the engineering curricu-lum, and the students involved already pos-sessed some industrial experience (throughvarious internships), we did not expose ourpostulates to a relevant number of industrialsituations. Nevertheless, the three cases inwhich this was done so far turned out prom-ising perspectives. The very next step in theprocess of improving our approach will there-fore consist in the enhancement of its robust-ness. Among others, one of our initialworking hypotheses was that if industry hasresisted the introduction of TRIZ, this wasoften due to the lack of a robust formalismthat could easily be taught to engineers andpractised within evaluated procedures. Webelieve that our partially achieved results cancontribute to a more formal and relevantTRIZ practice.

Conclusion

In the first section of this paper we brieflyintroduced an up-to-date account of actualvisions regarding Altshuller’s laws. His origi-nal idea was to look at problems from the engi-neering point of view, starting from technicalobservations and needs. He first of all consid-ered it his goal to provide the world of inven-tors with techniques and tools, and decidedthat these techniques and tools had to be sup-ported by methods. But to be efficient, thesemethods and tools also had to be built upon areliable and statistically relevant amount ofobservations, analyses and tests in real practice

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accompanied by a measurement of their effec-tiveness. Later (in the early 1970s), afterseveral iterations of tools and method ver-sions, the statement which emerged logicallywas that a theory had to be disclosed as thefoundation of Inventive Problem Solving.

For industrialists today, there is an urgentneed to efficiently operate inventive practice indesign and also in the R&D department. Thispaper is a proposal for experimenting withTRIZ in a way different from that currentlypractised in consulting areas, since our contri-bution suggests new options as to how anengineering design project could restructureits early stages. This restructuring aims at theprioritization of problems (here: reduced tocontradictions) being solved in conjunctionwith Altshuller’s laws in a pragmatic manner.As a result, a company may expect less fuzzi-ness in the undertaken design orientations,since all potential directions of design, whendealt with according to our approach, are cor-related to objective laws while highlighting thesum of unsolved problems standing in the wayof each LESE respectively.

From the educational viewpoint, ourapproach has been proven relevant in aninventive context, where the necessity of anorganizing breakthrough is strongly felt. Thisinduces us to postulate that in terms of indus-try new findings need to be worked out theo-retically to begin with, and subsequentlythrough tests as a second step of our presentcontribution.

Finally, the approach we have developed isaimed at providing an open framework fordiscussion. We observed that the evolutionhypothesis proved to be helpful in thecontext of LESE utilization, by increasing thecapacity of engineering students to benefitfrom a more coherent understanding andusability of some TRIZ elements. By attempt-ing to be a link between parameters andLESE, they have also linked MSA and contra-dictions, and thereby, if on a smaller scale,contributed to provoking a systemic impacton TRIZ itself.

References

Altshuller, G.S. (1984) Creativity as an Exact Science:The Theory of the Solution of Inventive Problems.Gordon and Breach Science Publishers, NewYork.

Altshuller, G.S. (1999) The Innovation Algorithm:TRIZ, Systematic Innovation, and Technical Creativ-ity. Technical Innovation Center, Worcester, MA.

Altshuller, G.S. (1991) To Find an Idea: Introduction tothe Theory of Inventive Problem Solving, 2nd edn.Nauka, Novosibirsk.

Altshuller, G.S. and Shapiro, R.V. (1956) AboutTechnical Creativity. Questions of Psychology, 6,37–49.

Altshuller, G.S., Zlotin, B.L., Zusman, A.V. and Phi-latov, V.I. (1989) Search for New Ideas: from Insightto Technology. Kartya Moldovenyaske PublishingHouse, Kishinev.

Cavallucci, D. and Khomenko, N. (2007) From TRIZto OTSM-TRIZ: Addressing Complexity Chal-lenges in Inventive Design. International Journal ofProduct Development, 4, 4–21.

Cavallucci, D. and Weil, R.D. (2001) Integrating Alt-shuller’s Development Laws for TechnicalSystems into the Design Process. CIRP Annals –Manufacturing Technology, 50, 115–20.

Gavin, W.T. and Mandal, R.J. (2003) EvaluatingFOMC Forecasts. International Journal of Forecast-ing, 19, 655–67.

Khomenko, N. and Ashtiani, M. (2007) ClassicalTRIZ and OTSM as a Scientific Theoretical Back-ground for Non-typical Problem Solving Instruments.7th ETRIA TRIZ Future Conference, Frankfurt,Germany.

Kucharavy, D. and De Guio, R. (2005) Problems ofForecast. ETRIA TRIZ Future 2005. Graz, Austria,219–35.

Mann, D. (2003) Hands-On Systematic Innovation.Creax Publisher, Ieper.

Moehrle, M.G. (2005) How Combinations of TRIZTools are Used in Companies – Results of aCluster Analysis. R&D Management, 35, 285–96.

Moles, A.A. and Rohmer, E. (1990) Les sciences del’imprécis. Seuil, Paris.

Petrovitch, V. (2005) Laws of Development ofNeeds. ETRIA TRIZ Future 2005. Graz, Austria.

Petrovitch, V. (2006) Laws of Dialectics in Technol-ogy Evolution. TRIZ Journal, June.

Pinyayev, A. (1990) Transition from the Administra-tive Contradiction to the Technical One in theAnalysis of Inventive Problems. TRIZ Journal, 1,(in Russian).

Salamatov, Y.P. (1991) System of Laws of TechnicalSystems Evolution, Chance to Adventure, Vol. 5.Karelia Publishing House, Petrozavodsk, 7–174.

Thirunavukkarasu, V., Devadasan S.R., Prabhushan-kar G.V., Murugesh R. and Senthilkumar K.M.(2008) Conceptualisation of Total Six Sigma Func-tion Deployment through Literature Snapshots.International Journal of Applied ManagementScience, 1, 97–122.

Denis Cavallucci is associate professor inEngineering Design and Innovation atINSA Strasbourg and co-fouder of theEuropean TRIZ Association (ETRIA).François Rousselot is associate professorin the same engineering school, researcherin artificial intelligence in the DesignEngineering Laboratory. Cecilia Zanni isalso associate professor at INSA andresearcher in computer sciences in the samelaboratory.

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