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Integrated Conceptual Design

Integrated Conceptual Design with TRIZ

| On 01, Sep 2008

By Kai Ming Yu, C.T. Lau, K.L. Tong and W.K. Wong


In new product development, idea generation that is innovative, able to be manufactured and profitable is important. For discrete consumer products or technical systems, creative idea generation techniques advocated by psychologists, and practiced by management consultants, are usually too general for engineers. A successful product needs to implement most (if not all) functions specified in the client statement and a multitude of factors identified from consumer and competitor analyses.

Optimization techniques, however, will suit more for detailed design. QFD (quality function deployment) may link up various product development stages with the voice of the customer (VOC), but alternatives are ranked by simple weights. This paper proposes an integrated approach for inventive idea generation and multi-criteria decision-making during conceptual design. The Theory of Inventive Problem Solving (TRIZ) is employed for generating idea systematically for technical problems, while superiority and inferiority ranking (SIR) will be used for more comprehensive alternative selection. Details on how the methods complement each other to achieve innovative product development will be explained, along with an illustrative practical example.


TRIZ, SIR, new product development, conceptual design


New product development (NPD) is important to the growth of every company because it can contribute toward creating improved products and services, which give competitive edge to the company.21 In new product development, generation of ideas that are innovative, able to be manufactured and profitable is important. Design with consideration of downstream activities has been well accepted since 1982.23 Quality function deployment attempts to link up various NPD stages with the voice of customer.1 For discrete consumer products or technical systems, creative idea generation techniques advocated by psychologists and practiced by management consultants are usually too general for engineers.

For instance, there is the five-step model of the creative process: preparation, incubation, intimation, illumination and verification.34 Edward De Bono developed lateral thinking to provoke creativity.15 A successful product needs to include most (if not all) functions specified in the client statement and a multitude of factors identified from consumer and competitor analyses. Design optimization, however, using cost function minimization cannot be done in the conceptual design stage; alternatives in QFD are ranked by simple weights. Tools are needed that can provide truly innovative ideas rather than simply creative or inventive ones.


Engineering design includes conceptual, embodiment and detailed design stages.27 A typical product development workflow is shown in Figure 1. As the famous inventor C. F. Kettering once said, “A problem well-stated is a problem half-solved,” the conceptual design stage is further broken down into more steps (Figure 2). The integration of concept clarification, generation and selection ensures efforts and budgets are properly spent in NPD activities.

Figure 1: Typical Product Development Workflow

Figure 2: Elaborated
Conceptual Design

Theory of Inventive Problem Solving (TRIZ)

TRIZ was developed by Genrich Altshuller and his colleagues in the former U.S.S.R. starting in 1946, and is now practiced throughout the world.3,4,5

TRIZ research is based on the hypothesis that there are universal principles of invention for creative innovations driven by advanced technology. If these principles can be identified and codified, it makes the process of invention more predictable. More than two million patents have been examined, classified by level of inventiveness and analyzed to look for the principles of innovation. The three primary findings of this research are:

  1. Problems and solutions were repeated across industries and sciences,
  2. Patterns of technical evolution were repeated across industries and sciences and
  3. Innovations used scientific effects outside the fields where they were developed.

In TRIZ, the forty principles and the contradiction matrix are the most accessible tools.32,17 Contradictions occur when trying to improve one characteristic or parameter of a technical system causes another characteristic or parameter of the system to deteriorate. A compromise solution is usually sought. In TRIZ, however, truly innovative solutions are those that resolve contradictions. In this technique, a technical system can be featured by several of thirty-nine generic characteristics (Table 1), such as weight, size, brightness, speed, strength, etc. Two characteristics define a contradiction that can be solved by studying the principles (Table 2) suggested in the contradiction matrix.28

Table 1: 39 Characteristics (Parameters) in the TRIZ Contradiction Matrix
C1: Weight of a mobile objectC21: Power
C2: Weight of a stationary objectC22: Loss of energy
C3: Length of a mobile objectC23: Loss of substance
C4: Length of a stationary objectC24: Loss of information
C5: Area of a mobile objectC25: Loss of time
C6: Area of a stationary objectC26: Amount of substance
C7: Volume of a mobile objectC27: Reliability
C8: Volume of a stationary objectC28: Accuracy of measurement
C9: SpeedC29: Accuracy of manufacturing
C10: ForceC30: Harmful factor acting on an object from outside
C11: Tension/pressureC31: Harmful factor developed by an object
C12: ShapeC32: Manufacturability
C13: Stability of compositionC33: Convenience of use
C14: StrengthC34: Reparability
C15: Time of action of a moving objectC35: Adaptability
C16: Time of action of a stationary objectC36: Device complexity
C17: TemperatureC37: Difficulty of detecting and measuring
C18: BrightnessC38: Extent of automation
C19: Energy spent by a moving objectC39: Productivity
C20: Energy spent by a stationary object

Table 2: 40 Inventive Principles in the TRIZ Contradiction Matrix
P1: Divide an object into independent partsP21: Skipping
P2: Removal/extractionP22: Convert harm into benefit
P3: Local qualityP23: Feedback
P4: AsymmetryP24: Intermediary
P5: MergingP25: Self-service and self-organization
P6: UniversalityP26: Copying
P7: Nested structuresP27: Inexpensive short-lived objects
P8: Anti-weightP28: Mechanics substitution
P9: Preliminary anti-actionP29: Pneumatics and hydraulics
P10: Preliminary actionP30: Flexible shells and thin films
P11: Beforehand cushioningP31: Porous materials and membranes
P12: EquipotentialityP32: Color changes
P13: ReverseP33: Homogeneity
P14: Spheroidality – curvedP34: Discarding and recovering
P15: DynamismP35: Parameters and properties changes
P16: Partial, satiated or excessive actionsP36: Phase transitions
P17: Another dimensionP37: Thermal expansion
P18: Mechanical vibrationP38: Strong oxidants
P19: Periodic actionP39: Inert atmosphere
P20: Continuity of useful actionP40: Composite materials

Multi-criteria Decision Making

Another focus in conceptual design is on the solution selection process. In order to prevent parameter conflicts during product innovation development, there can be a list of solutions. Multi-criteria decision aid (MCDA) provides tools and procedures to help the decision makers achieve the desired solution in the situation of ambiguous and uncertain product environments. Instead of yielding a single solution, MCDA establishes a kernel of preferred solutions. Solving multi-criteria problem does not search out the hidden truth, but helps decision makers master the complex data involved in the problem and advance to a solution.33 MCDA can solve decision problems:

  1. A scientific and systematic approach for decision-making is developed,
  2. An effective approach to eliminate personal biases in the decision making process is invented and
  3. A method to measure designers’ or users’ preferences toward each decision criterion and balance the conflicting criteria in order to generate the most cost-effective decision can be set up.

Multi-criteria Decision Aiding (MCDA) Models

All MCDA models exhibit certain constraints:

  • Analytical hierarchy process and fuzzy models can only handle ordinal data.
  • ELECTRE III can analyze different criteria without converting them into a single scale; however, it uses upward and downward distillations in which the discrimination thresholds for cut-off levels are subjectively assigned.
  • The superiority and inferiority ranking (SIR) method can process both cardinal and ordinal data. It provides six different preference structures and also incorporates outranking rationales for dealing with “poor” true-criteria preference structures. It generalizes the superiority and inferiority scores via generalized criteria introduced in PROMETHEE methods and can provide more information to represent decision makers’ preferences toward each decision criterion.10,35

The Integrated Approach

When compared to other creative concept generation methods, like brainstorming, brainwriting 6-3-5, QFD, etc., TRIZ is better-suited to inventive engineering solutions.20 As such, requirements specified in client statements will be formulated in terms of the thirty-nine engineering characteristics of TRIZ. In order to compare the relative importance of the objectives or the translated characteristics, weights are assigned as appropriate. The weights are also used in the SIR analysis of the alternatives (from in-house or competitors). For these purposes, a weighted objectives tree will be constructed from the client statement in which the leaves in TRIZ characteristics serve as criteria for alternative selection.

The importance of different objectives and sub-objectives of product design with weighted objectives can be ranked and compared to variants by visualizing their weight value profiles.27 The assignment, calculation and comparison of relative weights to the objectives and sub-objectives can be constructed into a weighted objectives tree.14 An objectives tree is better-suited for quick concept clarification while substance-field analysis and ARIZ better for detailed design.19

As the SIR method can handle both ordinal and cardinal data, permits the use of different preference structures by the decision-makers and is able to measure the superiority and inferiority intensities of alternatives, it is used for multi-criteria alternative selection in the integrated method. The proposed conceptual design is shown in Figure 3.

Figure 3: Overview of
Conceptual Design

SIR – Generalized Criteria

A multi-criteria problem is first formulated by using a set of alternatives (A1, A2 ¡ … Am) and criteria (g1, g2 ¡ … gn) and letting gj(Ai) be the criteria value or performance of the ith alternative Ai with respect to the jth criterion gj. Decision-makers need to assign thresholds and weightings for each criterion and alternative, and assign levels of performance gn(.) for each criterion. Then a (m by n) decision matrix, D, is formulated as follows:

The level of performance can be measured as indices, numerical constants or values in different units for different criteria. Decision makers can choose from any of the six different preference structures shown in Figure 4 and Table 3 according to their knowledge about the criteria.10,11,31

Figure 4: SIR– Generalized Criteria

Table 3: Equations of Generalized Criteria

With the difference in criteria d = g(A)-g(A’)e(-a,a) and the intensity of the preference P(A,A’) = f(g(A) – g(A’)) = f(d)e[0,1] , the parameters p and q are respectively the preference and the indifference thresholds, while e is the Gaussian standard deviation. Types 1, 2 and 4 are discrete functions while Types 3, 5 and 6 are fuzzy, i.e., the latter functions deal with grey areas between strict preference and indifference among alternatives.

Intensities of Superiority and Inferiority

After all gn(.) are determined, comparison among alternatives is made on a pairwise basis. Equations (1) and (2) give the intensities of superiority and inferiority of alternative Ai when compared to other alternatives. Sj(Ai) and Ij(Ai) are superiority and inferiority scores respectively, that provide more detailed information than the decision matrix, D, because the intensities of superiority and inferiority given by the generalized criteria are taken into account.29



The scores constitute the superiority matrix or the S-matrix:


and the inferiority matrix or the I-matrix:


The two matrices include better information than the original decision matrix, D, because the intensity of superiority and inferiority given by the generalized criteria are taken into account. Also, the matrices convey different information because they represent different types of comparison results.

SIR-Simple Additive Weighting

The superiority and inferiority ranking (SIR) method (when using simple additive weighting (SAW) as the aggregation procedure) coincides with the second step of the PROMETHEE method.35 The superiority and inferiority flows φ>(.) and φ<(.) are used to derive two complete rankings, R> = {P>,I>} and R< = {P<,I<}, of the alternatives and the two complete rankings are then combined into a final partial ranking as the intersection of the two:R = {P,I,R} = R>R<.11,30



These two flows are used to determine AP>A’, AI>A’, AP

  • The preference relation P by: APA‘ iff (AP>A)‘ or (AP>A‘ and AIA‘)or(AI>A‘ and AP-A‘) (6)
  • The indifference relation I by: AIA‘ iff AI>A‘ and AIA’ (7)
  • The incomparability relation R by: ARA‘ iff (AP>A‘ and A‘P<A) or (A’P>A and AP<A‘ (8)

Some synthesizing flows can be used to derive a complete ranking, such as the net flow in PROMETHEE,

φn(.) = φ>(.)-φ<(.)(9)

and the relative distance in TOPSIS,


The decision-maker uses the derived ranking (partial and/or complete) for further exploitation before a final decision is made. The MCDA process is summarized in Figure 5.

Figure 5: SIR Method Process

Case Study: Inline Skating Shoes

Inline skating is done on shoes that normally have four to five wheels arranged in a single line pattern. In general, the heel is used as a brake for stopping rather than using a toe stop. The mechanism of inline skating is similar on surfaces like skateboarding on roads or sidewalks. It may also work on special tracks and areas like skate parks and half-pipes. Some skaters compete in artistic skating events. The growth of inline skating was robust in the 1990s in the United States and then spread.

Due to their popularity, designers developed new casual sport shoes in combination with inline skating mechanism to become inline skating shoes. The real challenges for this type of trendy item are the appearance, functionality, convenience in use, etc. – a good example for using TRIZ with SIR to select the most competitive choice.

Assessment Criteria

There are hundreds of criteria in consideration when designing the shoe. Customers may select according to brand name (K2, Salomon, Rollerblade, UltraWheels). Marketing people may emphasize specific functions (K2 is proud of its soft boot, big wheels, long mount frames, aluminum-titanium alloy frame; Twincarn’s ILQ-9 bearings, stability cuff, etc.).

But from NPD’s point of view, more generic features should be adopted. In particular, the features chosen cannot block creative innovation. Upon market research, the design criteria of the product can be consolidated in terms of TRIZ characteristics (Table 1). Without loss of generality, the authors have identified eight TRIZ characteristics as the design criteria. According to the TRIZ concept of contradiction, these eight criteria determine the success of the product, but potentially contradict each other. Some criteria may reduce the side effect of some contradiction pairs, but may worsen the others. Another aid, therefore, the SIR method, is introduced to select the most appropriate alternative. The eight criteria in consideration are:

  1. Stability of object C13 – the retractable component. Can this component maintain a certain level of performance of the desired function even subject to strong forces such as the weight of the person? The higher the score, the more stable the candidate.
  2. Strength C14 – the structure of the parts (shoes and rollers). It must be strong enough to withstand being damaged from reasonable forces. The preferred alternatives should be more damage-resistant.
  3. Durability of moving object C30 – measuring the life of moving parts. The ability of moving objects to withstand frictional, collision damages accounts for their durability. Again, the higher score should be the more durable skate.
  4. Reliability C27 – compensational mechanisms/measures in the design of the product. What happens if the primary component, which is critical to the performance of the function or the safety of the user (e.g., presence of braking system), fails?
  5. Manufacturability C32 – compares the complexity of the process for manufacturing the product. This is not necessarily related to “complexity of system,” because the latter can be measured according to the quantity of parts involved in the assembly of the product. The manufacturability can be poor in systems composed by a limited number of parts which have unique shapes or are to be made by special materials leading to manufacturing difficulties. A higher ranking means the high degree of manufacturability or difficulties; it is preferable to have a less complexity in the process.
  6. Convenience of use C33 – measures the time and procedures needed to complete each series of operations. The higher the score, the more convenient the use of the inline skates.
  7. Complexity of system C36 – roughly determined by counting the number of parts involved in the system. There is an overlapping area between “complexity of system” and “manufacturability,” i.e., the required precision of the dimension of the parts, positions where each part is installed, need of calibration, etc. In a technical contradiction, a precise system may lead to another negative factor of complexity. In this case, a high score in complexity is less preferred than a lower score.
  8. Bulkiness of component C37 – most relevant to the product studied. As the retractability of the roller blade is one key component of the product, the way that individual design can make the retractable component minimal in size is the critical value of the design. This even relates to customer satisfaction. The lower the score, the more bulky the inline skates.

The above criteria are scored from 1 to 10 according to their performance precise to 2 decimal places; Table 4 shows the performance of three inline skating shoe examples based on these criteria.

Table 4: Performance of Inline Skates

Models of Inline Skates

Selection Criteria

A1: KL-5001

A2: HG-3262

A3: FR-3019
g1: Stability of object




g2: Strength




g3: Durability of moving object




g4: Reliability




g5: Manufacturability




g6: Convenience of use




g7: Complexity of system




g8: Bulkiness of component




The SIR Process

After the candidates and the criteria for the ranking process are ready, the weight of each criterion is assigned. In order to match the desired functions, a weighted objectives tree is constructed such that the weight reflects the importance of individual criterion with respect to the total performance of the inline skates (Table 5).

Table 5: Features of Criteria Before Evaluation Process

Criterion gj


Type of Criterion









Weight Wj, SWj = 1









Preference Threshold, p









Indifference Threshold, q









Non-decreasing/non-increasing (1/0)









Figure 5: Weighted Objectives Tree of Inline Skates

Next, the criterion type and the threshold values are assigned accordingly. From Equations (1) to (4), the S-matrix and the I-matrix are calculated.

S = [ S1(A1) S2(A1) S3(A1) S4(A1) S5(A1) S6(A1) S7(A1) S8(A1)

S1(A2) S2(A2) S3(A2) S4(A2) S5(A2) S6(A2) S7(A2) S8(A2)

S1(A3) S2(A3) S3(A3) S4(A3) S5(A3) S6(A3) S7(A3) S8(A3) ]


= [ 1.47 0 0 1.76 1.667 1 0 0

0.33 2 0.04 0 0 1 0.085 0

0 1 2 0 0.667 0 1.085 0.14 ]

I = [ I1(A1) I2(A1) I3(A1) I4(A1) I5(A1) I6(A1) I7(A1) I8(A1)

I1(A2) I2(A2) I3(A2) I4(A2) I5(A2) I6(A2) I7(A2) I8(A2)

I1(A3) I2(A3) I3(A3) I4(A3) I5(A3) I6(A3) I7(A3) I8(A3) ]


= [ 0 2 1.04 0 0 0 1.685 1.14

0.47 0 1 1 1.667 0 0.085 0

1.33 1 0 0.76 0.667 2 0 0 ]

The ranking process continues by using Equations (5), (9) and (10) to generate the S-flows, the I-flows, the n-flows and the r-flows (Table 6).

Table 6: Superiority and Inferiority Flows





φ>(A1) = 0.7287

φ<(A1) = 0.6993

φn(A1) = 0.0294

φr(A1) = 0.5101

φ>(A2) = 0.6425

φ<(A2) = 0.5192

φn(A2) = 0.1233

φr(A2) = 0.5531

φ>(A3) = 0.728

φ<(A3) = 1.1527

φn(A3) = -1.1527

φr(A3) = 0.4525

From Table 6’s S-flows and I-flows and using Equations (6)-(8), the two complete rankings are:

R>: A1 → A3 →A2

R<: A2 → A1→ A3

The outranking relationships are (> means outrank, ~ means indifferent):

A1>A3 A3~A2


A partial ranking sequence is resulted by combining thetwo flows:

R = R>R<:

In other words, superiority and inferiority flows together cannot tell whether A1 or A2 is preferred. From Table 6, the complete rankings by n-flows and r-flows are:

Rn: A2 → A1 → A3

Rr: A2 → A1 → A3

As a result, A2 is the first choice as it ranks higher in net flow and has a larger relative distance value when compared to A1. The overall ranking order is depicted in Table 7.

Table 7: Final Ranking of Inline Skates


A2: HG-3262


A1: KL-5001


A3: FR-3019

In addition, weights corresponding to the characteristics “Strength” (g2) and “Bulkiness of component” (g8) are greatest in Table 4. If g2 is the improving factor and g8 the worsening factor, the contradiction matrix suggests principlesP39, P3, P35 and P23 be used for further investigation on alternative HG-3262.


The 39 TRIZ characteristics in the classical contradiction matrix have not changed since the 1970s. Problems may arise when associating real-life engineering design functions with the 39 TRIZ characteristics. For the simple design problem in the example, the weighted objectives tree approach suffices.

Though it has been proposed to use SAW and TOPSIS to perform the aggregation process, these approaches will encounter the problem of variation in the final ranking, leading to inconsistent results; grey system theory may overcome the problem. One potential future direction is to incorporate the grey relational grade in formulating the superiority flows and the inferiority flows.35,16 For example, S-flows become:


In Equation (11),

the grey relational grade can be applied to series with a minimum three points that facilitate applications with only a few criteria. andC*S1 and CiSjare indices of an augmented normalized decision matrix and re[0,1] is the distinguishing coefficient.

For the same test case, different student groups suggested different criteria, different weight objective trees, and different most preferred alternatives and contradiction pairs to be resolved. This is reasonable as there is no fair choice in decision making.7 As a result, no unique optimal design solution is achievable, particularly from a multi-disciplinary design team.

This paper only outlines the procedure in integrating concept clarification and multi-criteria alternative selection for the sake of innovative concept generation. The design and implementation of a full-fledged computer-aided conceptual design tool requires further research.


Innovative product development depends on unbiased systematic methods to generate innovative ideas. With the guidance of TRIZ, product designers are assisted with consolidating bundles of criteria into a reasonable range so that the core items of consideration on designing the product can be focused and the quality and competitiveness of the design can be maintained. From the case study, the three candidates have their own strengths and weaknesses. To determine which one is the most preferred, SIR can achieve its functions of evaluation of each alternative on individual criterion. The scoring and ranking approach makes the selection more scientific and logical. TRIZ and SIR fulfill their functions and complement to each other.

HG-3262 is ranked the top and can serve as a reference for the designer to develop the next successful inline skating shoe design.


The authors acknowledge the support offered by The Hong Kong Polytechnic University.


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