The Trend of Engineering System Evolution (TESE) or Evolutionary Potential (EP) is a widely adopted tool within the framework of the Theory of Inventive Problem-Solving (TRIZ). Due to its applicability across diverse domains, TESE has found widespread use in real-world projects, contributing to the development of engineering products and the anticipation of emerging technologies. It is a tool for inventiveness and the identification of future engineering systems, and it continues to evolve its own body of knowledge through applications to become a comprehensive in terms of its practical utility in the realm of product innovation. TESE/EP, the trend of engineering system evolution, functions as a heuristic and predictive toolkit capable of yielding either market pull through the analysis of S-curves or technology push through its sub-trends.
The examination of the evolution paths of technical systems has been a fundamental research approach within TRIZ since its inception. However, it wasn’t until the 1970s that the identified recurrent patterns of evolution were systematically consolidated into a dedicated section of TRIZ. Genrich Altshuller, the founder of TRIZ, named this section “the laws of technical systems evolution.” This section encompassed both previously identified recurring evolution patterns and newly discovered ones. In the 1970s, Altshuller took the initiative to bring together these patterns, creating a cohesive framework for understanding the evolution of technical systems. The study of these “laws of evolution” emerged as an independent and significant research topic within TRIZ. Several key contributors, in addition to Altshuller, played vital roles in advancing this field.
Laws of Technical System Evolution
However, in his work in 1975, Genrich Altshuller categorized all laws of technical systems evolution into three distinct categories:
1: Statics: This category focuses on the criteria that determine the viability of newly created technical systems. It deals with the foundational principles that assess the stability and functionality of systems at their inception.
2: Kinematics: The laws falling under this category define the general principles that govern how technical systems evolve, regardless of specific conditions. Kinematics in this context addresses the overarching dynamics and trends in the evolution of technical systems.
3: Dynamics: This category is concerned with how technical systems evolve under specific conditions. Unlike the more general principles covered in Kinematics, Dynamics delves into the detailed and context-specific aspects of system evolution, considering the influence of external factors and conditions.
1. Law of the Completeness of the Parts of the System (Statics): A functional system is comprised of four essential parts, each serving a specific role: Engine: Generates necessary energy. Transmission: Guides and directs energy flow. Working Unit (Working Organ): Interfaces with the external world or processed object. and Control Element (Organ of Steering): Ensures adaptability and control. This law emphasizes the necessity for a comprehensive set of components in a system to ensure its proper functioning and adaptability. (keywords: wholeness of system)
2. Law of Energy Conductivity of the System (Static): Recognizing that every technical system transforms energy, this law emphasizes the importance of efficient and unrestricted circulation of energy through the four main components (engine, transmission, working element, and control element). Energy can be transferred through substance, field, or a combination of both (substance-field). There is a neeed to ensures the smooth flow and utilization of energy within the system. (keywords: conductible energy flow)
3. Law of Harmonizing the Rhythms of Parts of the System (Static): Focuses on achieving synchronization in the frequencies of vibration or periodicity among the different parts and movements of the system. Aims to create harmony and coordination among the various components, preventing conflicts or inefficiencies caused by discordant rhythms. (keywords: coordination, harmonization)
4. Law of Increasing the Degree of Ideality of the System (Kinematics): The ideality of a system is a qualitative measure of the ratio between all desirable benefits of the system and its cost or negative effects. This law suggests that in the pursuit of improving an invention, there is a natural inclination to increase ideality by either enhancing beneficial features, reducing costs, or minimizing harmful effects. Although achieving a state of zero cost for all benefits is not feasible, successive versions of technical designs typically increase ideality over time. Formula: Ideality = Benefits / (Cost + Harm). Keywords: Degree of idealness
5. Law of Uneven Development of Parts of a System (Kinematics): Acknowledges that different parts of a technical system will evolve at varying rates, leading to the emergence of new technical and physical contradictions. This law highlights the importance of recognizing and addressing disparities in the development of system components. Alerts to potential conflicts or challenges arising from uneven evolution within a system. Keywords: Unequal development of parts
6. Law of Transition to a Super-System (Kinematics): When a system reaches the limits of significant improvement, it is incorporated into a super-system as one of its parts. This integration into a larger context opens up new possibilities for the development of the original system. Recognizes the finite nature of individual system improvement and suggests a pathway for continued development through integration into a larger framework.
7. Transition from Macro to Micro Level (Dynamics) : The development of working organs within technical systems initially occurs on a macro level and then progresses to a micro level. This transition from macro to micro is identified as a significant, if not the primary, tendency in the advancement of modern technical systems. Therefore, when addressing inventive problems, particular attention should be given to understanding and analyzing the “macro to micro transition” and the physical effects that drive this transition. Recognizes the overarching trend of moving from larger-scale components to smaller-scale components in the development of technical systems.
8. Increasing the S-Field Involvement (Dynamics): Non-S-field systems evolve into S-field systems, and within the class of S-field systems, there is a progression from mechanical fields to electromagnetic fields. This evolution is characterized by an increase in the dispersion of substances in the S-field, an expansion in the number of links within the S-fields, and a tendency for the entire system’s responsiveness to enhance. Highlights the trend toward greater involvement of S-fields (substance-field interactions) in the evolution of technical systems, emphasizing the shift from non-S-field to S-field systems and the progressive refinement within the S-field category. Keywords: Increasing dynamicity and controllability, Su-Field Involvement
Patterns of Evolution
The patterns of evolution you’ve listed encapsulate key principles in TRIZ that Genrich Altshuller developed to understand and guide the evolution of technical systems. These patterns are versatile and applicable across various domains, including education, software, economics, and business. These patterns serve as a valuable toolkit for problem-solving and innovation, providing a systematic approach to understanding and guiding the evolution of diverse systems across different domains. They offer a structured methodology for engineers, designers, and innovators to analyze and improve technical systems effectively:
1. Evolution of Useful Functions: Focuses on the enhancement and development of beneficial functions within a system.
2. Elimination of Harmful Functions: Aims to identify and eliminate functions that have negative or detrimental effects on a system.
3. Evolution of Applications: Involves the adaptation and expansion of applications and functionalities within a system.
4. Integration/Structuralization: Emphasizes the integration and structural organization of components within a system for improved efficiency.
5. Increasing Dynamicity and Controllability: Addresses the need for systems to become more dynamic and controllable over time.
6. Evolution of Matching/Mismatching: Focuses on improving the compatibility and reducing conflicts between different elements in a system.
7. Evolution of Resource Application: Involves optimizing the utilization and application of resources within a system.
8. Evolution of Contradictions: Deals with identifying and resolving contradictions that may arise within a system.
9. Evolution of Processes in System: Encompasses the improvement and development of processes within a system.
10. Evolution of Fields: Describes the evolution and transformation of fields, including physical, energy, and informational fields.
11. Evolution Toward the Multilevel: Focuses on the progression of systems towards a multilevel structure, indicating increased complexity and hierarchy.
Developing A Brainstorming Toolkit Based Upon “Patterns and Laws of Evolution of Technical System”
In 2003, researchers in the field of TRIZ recognized the generality of the existing laws and identified undiscovered trends that could illustrate correlations among various components of a system (Mann, 2003). This realization prompted Mann to introduce evolution trends, categorizing them into three distinct groups: (1) space, (2) time, and (3) interface (refer to the table below). Each of these trends encompasses sub-trends, respectively (Mann, 2003). This categorization and expansion of trends provided a more comprehensive framework for understanding the evolving dynamics within systems.
In 2003, researchers in the field of TRIZ recognized the generality of the existing laws and identified undiscovered trends that could illustrate correlations among various components of a system (Mann, 2003). This realization prompted Mann to introduce evolution trends, categorizing them into three distinct group of trends: (1) space, (2) time, and (3) interface (refer to the table below). Each of these trends encompasses 12, 6, and 17 sub-trends respectively (Mann, 2003). Later Nesting Down (In Space) and Nesting Up (In Interfaces) were added to the list of these 35 trends, taking the collection count to 37 trends. This categorization and expansion of trends provided a more comprehensive framework for understanding the evolving dynamics within systems.
The trends introduced in 2003 were carefully designed to follow a logical sequence that reflects progress, offering a comprehensive exploration of both generic and specific trends. The intention behind this systematic approach was to facilitate problem-solving and strategic planning by addressing the technical “reason for the evolution,” essentially predicting the evolution of a system. Mann suggestd that while inventors have the freedom to choose any method of system evolution, adopting these strategic categories can provide a consistent framework, thereby increasing the likelihood of discovering potential technical trends (D. L. Mann, 2007; Mann, 2007). By adhering to these structured trends, inventors can strategically and methodically navigate the development process, enhancing their ability to identify and capitalize on emerging technical opportunities.
Over time, researchers and practitioners in the field of TRIZ have devised several approaches to enhance and refine this problem-solving methodology. One notable development involves the construction of a quantitative method that integrates the trends mentioned earlier. This method was designed to efficiently prioritize and discern solution objectives within TRIZ tools. The work by Sheu and Chiu in 2017 represents a significant effort in creating a systematic and measurable approach to leverage the diverse trends within TRIZ for more effective problem resolution and increases the count from 37 to 52 trends. This quantitative method likely contributes to a more streamlined and targeted application of TRIZ principles in problem-solving scenarios.
The system becomes progressively better in terms of resource utilisation after that when it is trimmed. Therefore, fewer parts are required to provide the same (or better) functionality. As systems progresses along their s-curve, they also follow a typical pattern of first rising complexity, then decreasing complexity, according to one of the trends discovered by the original TRIZ researchers. There is a direct relationship between complexity and the number of parts in all systems except the simplest. As a result, part count and the characteristic of increasing-decreasing complexity are related. The consequences of this association are that there are instances during a system’s evolution where trimming is an option, and there are instances when trimming is not. The line separating the two possibilities could be thought of as the greatest practicable complexity.
There are two key ideas linked to the application of TRIZ trends in a strategic context: the first is that any existing system has a “evolutionary limit.” The second is that the extent to which the current system’s potential remains untapped determines how this limit differs from it. When seen as a whole, the key concept to remember is that any evolutionary steps that haven’t yet been fully utilised provide us “evolutionary potential.” To do this, we may take an existing system, compare it to the general TRIZ tendencies, and determine how far along the trend line it is. The idea that an evolutionary limit is determined by what a current system could evolve into if it proceeded to the limits of each of the generic trend lines is another helpful one to bear in mind.
Both the idea of evolutionary potential and the associated notion that we may forecast the evolutionary limits of a particular system by using the evolution patterns found in TRIZ are crucial. There will be little point investing in areas where the system is already at or approaching the fundamental limits of its potential, for example, and there will be a lot of point investing in the development of parts of the system right at the beginning of its evolutionary potential, so information from an analysis of the evolutionary potential of a given system may be expected to play a significant role in determining how to spend R&D funds.
The “evolutionary potential radar graphic” aids in organising and presenting the concept of evolutionary potential. The sample evolutionary potential graphic shown below is used to show how far each TRIZ trend has evolved a particular system (also refer to the patent research reports). A TRIZ trend pertinent to the component under inquiry is represented by each spoke in the plot. The shaded area on the figure shows how far the current system has progressed along each trend, while the outer edge of the plot symbolises the evolutionary limit. The area difference between the perimeter and the darkened area thus serves as a gauge of evolutionary potential.
Method: Using trends as a brainstorming tool
First Approach: Trendspotting
1. Defining the system and the hierarchy of its components
2. Pick the system or component as the starting point for trendstorming.
3. Choose any trend that pertains to the system or any of its parts. Additionally, the plots are meant to be adaptable, allowing the user to just display patterns that are pertinent to a certain system (the recommendation is that all trends are examined as being possibly relevant, however).
4. Conduct a brainstorming session to generate ideas that will aid in advancing the system or its constituent parts upward (left-2-right).
Second Approach: The Prospect of Evolution
1. Defining the system and the hierarchy of its components
2. Pick the system or component as the starting point for trendstorming.
3. Decide whether trends are relevant to the system or its parts (refer to the section “trends” below).
4. Show the system or component’s current state on a diagram. Repeat this process for each trend that pertains to the system or component. The system’s use of the current evolutionary limit is the end result.
5. Sort or choose trends for system development. There are two methods to go about this. To extend the evolutionary limitations of the system or component in the first scenario, one may choose to create ideas at random along each trend line. However, it is advised to keep in mind the three major trending categories. They stand for space, time, and the interface. In the second scenario, it could be interesting to map the same evolutionary limit for the rival companies’ products using a radar plot. choose the trends that will differentiate the industry and, therefore, put you in the best position to make sales.
6. List the concepts or ideas that were formed, display them, and assess them.
7. Decide which ideas will be implemented.
No. | Trend | Stages | Category |
1 | Smart Material | Passive Material , One Way Adaptive, Two Way Adaptive, Fully Adaptive Material | Space |
2 | Space Segmentation | Monolithic Solid, Hollow Structure, Structure With Multiple Hollows, Capillary/Porous Structure, Porous Structure With Active Elements | Space |
3 | Surface Segmentation | Proposed By Darrell Mann : Smooth Surface,Surface with Rib Protrusion, 3D Roughene Surface, Roughened Surface + Active Pores or elements D. Daniel Sheu and Sheng Chia Chiu proposed the Object Segmentation Stages to Surface Segmentation Stages as well, which includes: Segmented Solid, Particulate Solid, Fluid, Segmented Fluid, Gas, Plasma, Field, Vacuum | Space |
4 | Object Segmentation | Monolithic Solid, Segmented Solid, Particulate Solid, Fluid, Segmented Fluid, Gas, Plasma, Field, Vacuum | Space |
5,6 | Macro to Nano Scale (Space and Time) | …102, 101, 100, 10-1, 10-2… | Space Time |
7 | Webs & Fibres | Homogenous Sheet Structure, 2D Regular mesh Structure, 3D Fibre, With Active Elements | Space |
8 | Decreasing Density | …102, 101, 100, 10-1, 10-2… | Space |
9 | Asymmetry (Increasing Asymmetry) | Symmetrical System, Partial Asymmetry, Matched Asymmetry | Space |
10,11 | Boundary Breakdown (Space and/or Interface) | Many Boundaries, Few Boundaries, No Boundaries | Space Interface |
12 | Geometric Evolution (Linear) (Nonlinearities) | Point, 1D Line, 2D Plane, 3D Surface D. Daniel Sheu and Sheng Chia Chiu proposed other trends
(12a) : Single Level to Multi-levels | Space |
13 | Geometric Evolution (Volumetric) | Planar Structure, 2D Structure, Axi-Symmetric Structure, Fully 3D Structure | Space |
14 | Dynamization (Substance Dynamization) | Immobile System, Jointed System, Fully Flexible System, Fluid or Pneumatic System, Field Based System D. Daniel Sheu and Sheng Chia Chiu proposed other trends
(14a) : Composition of dynamization (14b): Function Dynamization (14c): Field Dynamization | Space |
15 | Action Coordination | Non-Coordinated Action, Partially Coordinated Action, Fully Coordinated Action, Different Actions During Intervals | Time |
16 | Rhythm Coordination (Stage of coordination) | Continuous Action, Periodic Action, Use of Resonance, Travelling D. Daniel Sheu and Sheng Chia Chiu proposed other trends (16a) : Coordinating shapes (16b): Coordinating rhythms
(16c): Coordinating materials (16d): Coordinating actions (16e): Choice of parameters of coordination | Time |
17 | Non-Linearities (matching to external) | Linear Consideration of System, Partial Accounting of Non-linearities, Full Accommodation of Non-Linearities | Time |
18,19 | Mono-Bi-Poly (Similar) (Increased Similar Integrated Systems) | Mono-System, Bi-System, Tri-System, Poly-System | Interface Time |
20,21 | Mono-Bi-Poly (Various) (Interace and Time) (Increased Various Integrated Systems) | Mono-System, Bi-System, Tri-System, Poly-System | Interface Time |
22 | Mono-Bi-Poly (Increasing Differences) | Similar Components, Components with biased characteristics, Component with negative component, Different Components D. Daniel Sheu and Sheng Chia Chiu proposed other trends (22a) : Increasing differentiation between main functions (22b): Deeper Integration (22c): Increasing the completeness of system components (22d): Trends of uneven development of system or interface components | Interface |
23 | Damping (Reducing) | Heavy, Critical, Light, Un-damped | Interface |
24 | Use of Senses (Sense Interactions) (Increasing Use) | 1 , 2, ,3, 4, 5 | Interface |
25 | Use of Colors (Color Interactions) (Increasing Use) | No Use of Color, Binary Use of Color, Use of Visible Spectrum, Full Spectrum Use of Colour | Interface |
| 26 | Transparency (Increasing) | Opaque,Partially Transparent, Transparent, Active Transparent Elements | Interface |
27 | Customer Purchase Focus | Performance, Reliability, Convenience, Price | Interface |
28 | Market Evolution | Commodity, Product, Service, Experience, Transformation | Interface |
29 | Design Point | Design Optimized For Single Operating Point, Design Optimized at Two Operating Points, Design Optimized at Several Discrete Operating Points, Design re-optimized Continuously | Interface |
30 | Degrees of Freedom | 1 DOF, 2 DOF, 3 DOF, 4 DOF, 5 DOF, 6 DOF | Interface |
31 | Trimming (Device and Process Trimming) | Complex Systems, Elimination of non-key Components, Elimination of non-key Subsystems, Trimmed Systems | Interface |
32 | Controllability | Direct Control Action, Action Through Intermediary, Addition of Feedback, Intelligent Feedback D. Daniel Sheu and Sheng Chia Chiu proposed other trends
(32a): Increasing level of control (32b): Increasing number of controllable states | Interface |
33 | Human Involvement (Reducing human involvement) | Human, Human + Tool, Human + Powered Tool, Human + Semi-automated Tool, Human + Automated Tool, Automated Tool | Interface |
34 | Design Methodology | Cut & Try, Steady State Design, Transient Effects Included, Slow Degradation Effects Included, Cross-Coupling Effects, Design for Murphy | Interface |
35 | Number of Energy Conversions (Reducing number of energy conversions, Tending to Zero) | 3 Energy Conversions, 2 Energy Conversions, 1 Energy Conversion, 0 Energy Conversion D. Daniel Sheu and Sheng Chia Chiu proposed other trends (35a): Increasing conductivity of the flow (35b): Increasing flow utilization (35c): Reducing the conductivity of the harmful flows | Interface |
Example 1 : The “Space Segmentation” trend you’ve mentioned involves various forms of structuring or segmenting the space within an object. Let’s consider how this trend could be applied to the design of a toothbrush:
Monolithic Solid: Example: A traditional toothbrush with a single, solid handle and bristle structure made from a single material, like plastic.
Hollow Structure: Example: Designing a toothbrush with a hollow handle to reduce material usage and weight, making it more environmentally friendly.
Structure With Multiple Hollows: Creating a toothbrush with multiple hollow sections in the handle, strategically placed to maintain structural integrity while reducing overall weight.
Capillary/Porous Structure: Designing a toothbrush handle with capillary or porous materials to enhance grip by absorbing moisture and providing a comfortable hold.
Porous Structure With Active Elements: Integrating an active element, such as a replaceable cartridge containing additional oral care agents, into the porous structure of the toothbrush handle to enhance its functionality.
Example 2: The space segmentation trend provided above, includes categories like “Monolithic Solid, Hollow Structure, Structure With Multiple Hollows, Capillary/Porous Structure, Porous Structure With Active Elements,” can be applied to the design and evolution of a brick. Let’s explore how this trend might manifest in the context of a brick:
Monolithic Solid: Traditional bricks are often monolithic solids, meaning they are solid structures without internal voids. This type of brick is known for its strength and durability.
Hollow Structure: An evolution in brick design might involve creating hollow bricks. This design could reduce the weight of the brick while maintaining structural integrity. Hollow bricks are often used for insulation purposes.
Structure With Multiple Hollows: Taking the hollow concept further, a brick could be designed with multiple hollow chambers. This design might enhance insulation properties and provide additional benefits such as reduced material usage.
Capillary/Porous Structure: A brick with a capillary or porous structure might be developed to enhance moisture absorption or drainage. This could be particularly useful in construction applications where managing moisture is essential.
Porous Structure With Active Elements: In a more advanced evolution, a brick could be designed with a porous structure that incorporates active elements. For example, the pores might be filled with a material that reacts to environmental conditions, offering functionalities like self-healing or improved thermal regulation.
Conclusion
The acceptance of TRIZ research work by rigorous scientific journals has faced challenges attributed to perceived shortcomings such as a lack of repeatability, rigorous modeling, and insufficient mathematical/quantitative content. These factors have contributed to the research not receiving the recognition it may deserve from certain scientific communities. Moreover, the issue of identifying relevant solution models becomes pronounced when dealing with a large number of potential solution models. The sheer volume of possibilities can pose a challenge in terms of selecting and presenting the most pertinent and effective models within the TRIZ framework. Addressing these challenges may involve enhancing the repeatability and rigor of TRIZ methodologies, incorporating more rigorous modeling techniques, and increasing the quantitative aspects in TRIZ research. These improvements could contribute to a more favorable reception of TRIZ research within the scientific community, as they align with the expectations and criteria set by rigorous scientific journals.
There are three major deficiencies associated with traditional TRIZ problem-solving tools. Addressing these deficiencies may involve the development of more standardized and repeatable TRIZ methodologies, the introduction of prioritization mechanisms for assessing potential solutions, and the establishment of knowledge-sharing platforms to accumulate and disseminate identification knowledge/experience across problem-solving teams. These improvements could enhance the efficiency and effectiveness of TRIZ in addressing complex problems:
Expert-Dependent and Non-Repeatable Solutions: Varying solutions often emerge from different experts or specific problem-solving occasions, resulting in solution models that are highly dependent on the expertise of individuals and are non-repeatable. This creates inconsistency in the application of TRIZ, as solutions may differ based on the expert providing them, making it challenging to establish standardized and repeatable processes.
Time-Consuming Individual Assessment: Experts are often required to individually assess numerous potential solution models without a clear priority, leading to a time-consuming process. The inefficiency of individually assessing a large number of potential solutions can slow down the problem-solving process and hinder productivity.
Limited Knowledge/Experience Accumulation: Each instance of solution identification relies on the expertise of a specific problem-solving team, with no mechanism to accumulate this knowledge and experience for future teams. Lack of a structured way to capture and share identification knowledge/experience restricts the transfer of insights and lessons learned from one problem-solving scenario to another.
The goals of solution priority, objectivity, repeatability, modeling rigor, and speed in obtaining solution models can be achieved through the application of quantitative measures. By incorporating mathematical and quantitative methods, the approach aims to enhance the TRIZ problem-solving process. The system design facilitates problem-solving based on the cumulative results of continually accumulating knowledge and experiences from many experts, as embedded in expert-solved cases. This contrasts with the reliance on the knowledge and experience of individual experts. Utilizing mathematical and quantitative methods within the TRIZ problem-solving framework as an overarching goal can contribute to the recognition of TRIZ in rigorous scientific research communities by introducing systematic, quantitative approaches to problem-solving within the TRIZ methodology.
References
Altshuller G.S., ‘Creativity As an Exact Science. Theory of Inventive Problems Solving’, (Moscow, Sovetskoye Radio, 1979).
Altshuller G.S., ‘To Find an Idea: Introduction to the Theory of Inventive Problems Solving’, (Novosibirsk, Nauka, 1986)
Altshuller G.S & Vertkin I., ‘Lines of Voidness Increase’, (Baku, 1987, Manuscript).
Altshuller G.S., ‘Small Infinite Worlds: Standards For Solving Inventive Problems’, in ‘A Thread in a Labyrinth’, Karelia, 1988, pp 183–185.
