Designer’s confession: trial-and-error approach vs. ideality search - why I joined the TRIZ “sect”

A few years ago, I have got introduced to the TRIZ approach to innovation by Prof. Leonid Chechurin during his class at the LUT. The idea of being able to streamline the product design and innovation was so fresh and resonated so deeply with me. This was the point of no return. I joined the “sect” never to be back to the old thinking again. Here is why.

Product concept – a vision of a product on the early stage

A product concept is a vision of a product complete and developed on the earlier stage of the product design. The underlying challenge is to envision the final design at the very beginning of the design process. This is often the most resource demanding and challenging part in the entire design process. Especially challenging it becomes when there is a need to propose a solution to a system that is already quite simple, is limited to the component level only or is novice. That is why the common logic underlying the existing solutions for concept development is to engage in as many trial-and-error attempts as early as possible. However, this approach guarantees arrival to a workable result mainly by luck.

Design and development as continuous product conceptualization: trial-and-error vs. ideality search

At the most fundamental level, design and development is continuous product conceptualization process. When perceived from this perspective, it becomes evident that trial-and-error method limits effectiveness of design and development especially in a fast-paced environment (when solution was required yesterday). The problem lays in the underlying philosophic approach to research and development expressed in inductive or deductive reasoning. Trial-and-error is the approach based on the inductive reasoning and suitable when data is available and easily processed and analyzed, which is seldom the case with the new product design and development. An alternative approach is the ideality search, which is a form of deductive reasoning. It starts with an assumption that ideality of a system is possible leaving an engineer with freedom to find it. An ideal state of a system is reached when the function(s) is performed while the complexity of a system is reduced. The Theory of Inventive Problem Solving (TRIZ) as a method for concept development (Altshuller, 1984) is built on the ideality concept and is more suitable for conceptualization stage of design and development. Next, I briefly explain how I arrived to such decision.

Overview of the methods for product concept design and development

There are many tools and methods commonly used for innovation, system development, product development and (re)design. Their abundancy is just an indicator of their limited efficiency and effectiveness. Their comparison in relation to their performance and effectiveness is outside the scope of the article. Nevertheless, we want to recognize them and introduce their underlying approach to problem-solving. The list below is not finite, but we find these as the most recognized and utilized. Some of the tools are a part of the more complex approach yet their underlying principle makes it worth mentioning them separately.

1. Design thinking: A human-centered, iterative approach to problem-solving and innovation that involves understanding users, defining problems, generating potential solutions, prototyping and testing. (see, for instance, Ambrose and Harris, 2010; Brown, 2008; Dam and Siang, 2020; Lewrick et al., 2018; Mootee, 2013).
2. Brainstorming: A group-based approach to generating ideas and solutions, where participants are encouraged to share their thoughts and opinions freely and without criticism. (De Bono, 1970; Higgins, 1994; Osborn, 1953; Snyder, 2021).
3. Rapid prototyping: The process of quickly creating a preliminary version of a product or system in order to test and refine its design. (Kelly and Littman, 2001; McElroy, 2017; Norman, 2013).
4. Customer development: A process for understanding and validating customer needs, as well as for developing and refining a product or business concept. (Blank, 2013; Blank and Dorf, 2012).
5. Lean Startup: A methodology for developing and launching new products and businesses that emphasizes rapid experimentation and iteration, customer feedback, and data-driven decision-making. (Klein, 2013; Ries, 2011).
6. Minimum viable product (MVP): A part of Lean Startup approach that stresses the need in a minimal version of a product that has only the core features necessary to validate its viability with customers. (Ries, 2011).
7. Agile project management: An iterative and flexible approach to project management and product development that emphasizes adaptive planning, collaboration, and continuous delivery. (Cohn, 2006; Patton, 2014; Sutherland, 2014; Warfel, 2009).
8. Design of experiments (DOE): A statistical method for determining the relationship between inputs and outputs in a system, and for improving the performance of a product or process. (Box et al., 1978; Fisher, 1935).
9. Quality Function Deployment (QFD): A method for understanding and prioritizing customer needs, and for translating these needs into product design specifications. (Akao, 2004; Shillito, 1994).
10. User experience (UX) design: A design discipline that focuses on creating user-centered products and services that are both functional and enjoyable to use (Cooper et al., 2007; Norman, 2013; Walter, 2011).
11. Business process reengineering (BPR): A management approach for fundamentally redesigning business processes to achieve significant improvements in performance and efficiency. (Dumas et al., 2018; Hammer and Champy, 1993; Harmon, 2014).
12. Systems analysis and design: A systematic and comprehensive approach to designing, building, and testing information systems, taking into account the needs of users, stakeholders, and other systems. (DeMarco, 1979; Rodgers and Milton, 2011; Rumbaugh et al., 1991; Tilley, 2019; Valacich and George, 2021).
13. System design: A systematic and comprehensive approach to designing complex systems, such as products, services, and organizations, based on an understanding of their structure, behavior, and performance. (Budynas and Nisbett, 2020; Childs, 2019; Collins et al., 2009; Salvendy, 2001; Ugural, 2004).
14. Six Sigma: A data-driven quality improvement methodology that uses statistical tools and techniques to reduce defects and variability in a product or process, and to achieve near-perfect quality. (Breyfogle, 2003; Creveling et al., 2003; Cudney and Furterer, 2012; George et al., 2005; Harry and Schroeder, 2000; Levine and Gitlow, 2004; Pyzdek and Keller, 2010; Wilson, 2005).
15. Reverse Engineering: A process for taking apart and analyzing existing products, systems, or technologies in order to understand their design and functionality, and to identify potential improvements or innovations. (Eilam, 2005; Messler, 2014; Raja and Fernandes, 2009).
16. System Dynamics: A method for modeling and analyzing complex systems and their interactions, with a focus on understanding the behavior and performance of the system over time. (Palm, 2010; Pfeiffer and Wriggers, 2008; Seeler, 2014).
17. Failure Mode and Effects Analysis (FMEA): A systematic approach for identifying potential failures in a system, evaluating their effects, and developing corrective actions to prevent or mitigate those failures. (Carlson, 2012; Creveling et al., 2003; Magrab et al., 2010; McDermott et al., 2008; Stamatis, 2003; VDA QMC, 2019).
18. Root Cause Analysis (RCA): A method for identifying the underlying causes of problems or failures in a system and developing corrective actions to prevent future occurrences. (Andersen and Fagerhaug, 2006; Barsalou, 2015; McDermott et al., 2008; Vanden Heuvel et al., 2008).
19. Design for Manufacture and Assembly (DFMA): An approach to product design that aims to simplify the manufacturing and assembly processes by identifying and eliminating unnecessary parts, minimizing the number of assembly operations, and maximizing the use of standard, off-the-shelf components. (Anderson, 2020; Boothroyd et al., 2011, 2002; Bralla, 1999; Dewhurst, 2011; Molloy et al., 1998).

Common problem of mentioned methods: paradigm and approach

There are common issues that unite these methods deeming them less efficient: the underlying paradigm and approach to tackling the challenges. Inefficiency in this case is defined by the excessive time consumption to find a solution, higher resource demand, loss of opportunities for other innovations, challenges addressing more complex research problems especially in the fundamental science context to name a few.

The issue of the underlying paradigm – trail-and-error method

The underlying paradigm/approach to problem-solving follows the trial-and-error method. It typically involves making incremental changes to a solution, observing the results, and adjusting accordingly until the desired outcome is achieved. It is often used in situations where there is no clear path to a solution or where the consequences of failure are low, allowing for experimentation and exploration of different approaches. A quickly found solution is a by-product of a good luck and extended experience of the members of the development team. Often, the solution in trial-and-error mode takes years to find. Altshuller illustrates this problem with numerous examples through a comparison of the lost time while using the trial-and-error approach and TRIZ. (Altshuller, 1984).

The issue with the used approach to conceptualization – customer feedback

The issue with orientation, as the other common issue with the models, consists of roughly two dimensions: orientation on customer feedback and on statistical data. When regarding the customer feedback, taking this approach is similar to searching for a black cat in the dark room. Customers/users often do not know what they want; users can easily define what they do not like, yet definition of what they want seldom comes as easy. That means that there is a need for more cycles of trials before a solution is found. Such approaches as Design Thinking, Customer Development, Brainstorming, Lean Startup are amongst those employing this approach. Although customer involvement is undoubtedly important for testing and adaptation of a solution to a market, using it as a reference point while searching for a novice solution brings along the inefficiency side effects as reasoned above.

In regards to statistical data, such approach is similar to driving forward by looking in the rear-view mirror. The collected and analyzed data helps defining the issue, highlights the potential for improvement and focuses the effort on potential issue, but it still leaves the process of search for a solution to the discretion of the luck. This approach among others is shared by DOE, Six Sigma, RCA, DFMA leading to creation of more trials attempts in search of a better solution. The challenge becomes more complex, when the issue to be addressed lays in the fundamental science field requiring more and more observations and trials.

Introduction of TRIZ as a tool for concept development

In contrast to these approaches, TRIZ defines the ideality as the reference point. The ideality drives the engineering thought forward yet leaves abundant room for integration with other approaches. As a model that is built on the system view, TRIZ and system design models share a lot in common bringing them the closest together. The ideality though is the major difference that sets the approach apart from the other alternatives. In more details, the TRIZ approach is presented in the Altshuller and his colleagues/followers (starting with Altshuller, 2011, 1984; Altshuller et al., 1989; Bukhman, 2021; Gadd, 2011; Harrington, 2017; Petrov, 2016) along with the set of powerful tools such as Ideal Final Result (IFR) analysis, nine windows diagram, contradiction matrix, function analysis, Substance-Field analysis and Algorithm for Inventive Problem Solving (ARIZ).

Hopefully, this explains to a certain degree my "sect" choice and creates an interest for you to explore the theory for yourself especially if you are interested in new product design and development. Beware though, there is a high chance of falling under the spell. But if/when you do, get in touch with me. We might have some creative fun together.

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