Integrated Conceptual Design With TRIZ

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    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:

    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

    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 the two 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 principles P39, 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. and C*S1 and CiSj are 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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    About the Authors:

    Dr. Kai-ming Yu received his B.Sc. in mechanical engineering from the University of Hong Kong in 1985. He obtained his Ph.D. from the University of Hong Kong, Department of Mechanical Engineering in 1991. He worked in the Research Centre and Mechanical Engineering Department of the Hong Kong University of Science & Technology until 1993. He is currently an Associate Professor in the Hong Kong Polytechnic University Industrial and Systems Engineering Department. His research interests include CAD/CAM, CAE, PDM, reverse engineering and rapid prototyping technologies. He is also a Senior Member of the Society of Manufacturing Engineers. Contact Kai Ming Yu at mfkmyu (at)

    Mr. C.T. Lau is a research assistant in the Department of Industrial and Systems Engineering at The Hong Kong Polytechnic University, Hong Kong, China. Contact C.T. Lau at htony_lau (at)

    Dr. K.L. Tong is a senior research assistant in the Department of Industrial and Systems Engineering at The Hong Kong Polytechnic University, Hong Kong, China. Contact K.L. Tong at tklmas (at)

    Mr. W.K. Wong is a graduate of the Department of Industrial and Systems Engineering at The Hong Kong Polytechnic University, Hong Kong, China.

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