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Introduction to Product Development

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In everyday industrial life, the product development process is increasingly coming into the focus of corporate management. This is mainly due to product developers’ great influence on product design and thus on the influencing factors quality, time, and costs of the product to be developed. This chapter gives a brief overview of the product development process; a more detailed examination is provided in the following chapters.

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Schlattmann, J., Seibel, A. (2021). Introduction to Product Development. In: Structure and Organization of Product Development Projects. Springer, Cham. https://doi.org/10.1007/978-3-030-81046-7_1

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A Systems View Across Time and Space

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New product development process and case studies for deep-tech academic research to commercialization

• Pravee Kruachottikul 1 , 2 ,
• Poomsiri Dumrongvute   ORCID: orcid.org/0009-0009-7461-5888 3 ,
• Pinnaree Tea-makorn 4 ,
• Santhaya Kittikowit 5 &
• Arisara Amrapala 6  

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This research proposes a new product development (NPD) framework for innovation-driven deep-tech research to commercialization and tested it with three case studies of different exploitation methods. The proposed framework, called Augmented Stage-Gate, integrates the next-generation Agile Stage-Gate development process with lean startup and design thinking approaches. The framework consists of six stages and five gates and focuses on critical thinking to help entrepreneurs avoid psychological traps and make the right decisions. Early activities focus on scouting for potential socioeconomically impactful deep-tech research, developing a business case, market analysis, and strategy for problem–solution fit, and then, moving to a build–measure–learn activity with a validated learning feedback loop. Next, suitable exploitation methods are decided using weight factor analysis, developing intellectual property (IP) strategy, completing the university technology transfer process, and participating in fundraising. To pass each gate, the committee board members, consisting of tech, business, IP and regulatory, and domain experts, will evaluate the passing criteria to decide Go/No-Go. Applying the framework to the case studies results in successful university research commercialization. The model, case study, and lessons learned in this paper can be useful for other deep-tech incubator programs to successfully launch deep-tech research for commercialization. The case studies’ positive outcomes validate the Augmented Stage-Gate framework, yet their success is not entirely guaranteed due to external factors like regulatory constraints, entrepreneur characteristics, timing, and the necessary ecosystem or infrastructure, particularly in emerging markets. These factors should be taken into account for future research purposes.

Introduction

Deep-tech innovation is a new wave of impactful innovation that drives the economy and society. Unlike digital innovations such as mobile apps and digital platforms that disrupted many old-fashioned businesses in past decades, deep-tech is unique, high-value, hard-to-reproduce technological or scientific advances that will improve the technological frontier or disrupt existing solutions and result in socio-economic impacts (De la Tour et al., 2017 ). Deep-tech innovation is usually led by megatrends and unmet needs (Linden & Fenn, 2003 ).

Thailand, a developing country, relies heavily on traditional businesses such as sales, marketing, and services. Thailand’s gross expenditure on R&D (GERD) is lower than that of other middle-to-high income countries. In 2018, Thailand spent 1.11% of gross domestic product (GDP) (182 billion baht) compared with an average of 1.41% for the upper-middle-income group and 2.43% for high income countries. GERD was expected to reach 2% of GPD in 2027 but this was revised to 1.46% due to the COVID-19 pandemic, assuming no new measures to boost R&D investment. Nevertheless, various government policies require stimulus to R&D spending, especially for SMEs and innovation-driven enterprises through the Thai Bay-Dole Act (Office of National Higher Education Science Research and Innovation Policy Council, 2021 ). Therefore, deep-tech innovation applied to Thai businesses could be a potent new driver for its economy. Since most deep-tech originates from academia, researchers, patents, or publications, it is unlikely to be successful and sustainable without real demand from users or direction from the business side. This is because traditional academia focuses heavily on research, publication, and prototype development (Fellnhofer, 2016 ), rather than building a product that is ready for commercial use (Hicks et al., 2009 ). Promoting entrepreneurship, which is a combination of art and process to pursue opportunities and turn into a business regardless of resources, among academia can be helpful to create environments that support innovation development (Barringer & Ireland, 2012 ).

Moreover, many deep-tech innovations require a large amount of funding at the initial stage to build a prototype, perform user validation, and develop a business strategy. Additionally, deep-tech innovation is new, and the industry may not be clear about market needs or potential buyers. Therefore, the technology acceptance model (TAM) is used to understand predictors of human behavior toward potential acceptance or rejection of the technology, particularly technologies related to information and communication technology (ICT) (Lee et al., 2003 ). It can also provide a useful tool to assess the success of new technology introductions and help understand the drivers of acceptance to proactively design interventions targeted at users that may be less inclined to adopt new systems (Venkatesh et al., 2003 ). After validating the market and technology, it is time to decide on commercialization options (Yaldiz & Bailey, 2019 ).

For deep-tech innovation to become successful exploitation from the research ideation stage until commercialization, it requires a product development model suitable for university research initiation and developing market environment. Meanwhile, many pieces of prior research on the NPD model and case studies were primarily conducted based on developed countries where the product development was done within the established company ecosystem (Cocchi et al., 2021 ; Cooper, 2016 ; Cooper & Sommer, 2016 , 2018 ; Salvato & Laplume, 2020 ; Walrave et al., 2022 ; Wuest et al., 2014 ). However, this study highlighted the importance of a specific NPD model in the academic initiative context with low resources and a lack of infrastructure setting, which generally happens within developing countries (Ravi & Janodia, 2022a ). This study is essential to promote deep-tech in Thailand and to help other developing countries that require a new growth potential to drive the economy. Consequently, to accelerate deep-tech innovation in Thailand, the Chulalongkorn University Technology Center (UTC) was established in 2019 as a platform to spring-board academic research to commercialization and facilitate among stakeholders within the ecosystem based on triple helix model, which promotes the way of working that the government, private sector, and academia must collaborate to form a solid, deep-tech innovation ecosystem (Leydesdorff & Etzkowitz, 1998 ) to support manpower, finance, know-how, production facilities, regulation, and sandbox testing in order to expedite the speed of innovation development.

This study uses qualitative research and observation based on the actual case studies of the UTC portfolio research teams. The goal is to understand the pain points, needs, obstacles, and processes required for the successful exploitation of their project and then extract the vital insightful factors for applying to the NPD model, which will be later discussed in the Methods section.

To develop the proposed NPD model, several related NPD studies have been reviewed. Then the next-generation stage-gate development system integrated with agile development, lean startup, and design thinking methods is selected and then applied together with the insights obtained from qualitative research as the NPD model to develop successful business-driven deep-tech innovation. The effectiveness of the model is later tested and confirmed using both experts and observation, which will be later described further in the Results section. This framework, which we call the Augmented Stage-Gate framework, is important for successful innovation and is based on critical thinking. Because human decisions are influenced by the subconscious, it is essential to make decisions based on the results of logical reasoning and avoid psychological traps (Linden & Fenn, 2003 ).

In addition, three case studies are explained and discussed. Applying the Augmented Stage-Gate framework results in successful commercialization process in all three cases where the teams transferred the technology via a spin-off startup with a patent, non-profit use with trade secret, and licensing. The benefits of this study can be used as a framework and case study for successful deep-tech innovation development and commercialization, especially in the context of developing markets and academic research initiation. Several options are proposed and discussed. Finally, the study makes several recommendations for future research, including its application to other vertical deep-tech innovation areas.

Literature review

In this section, the literature on the NPD model, TAM model, and product readiness assessment is discussed. Generally, the NPD model, is a nonlinear and iterative process based on a problem-solving approach that is used for the conception, development, and launch of new products or services. It can help management understand user insights, challenge assumptions, redefine problems, and create innovative solutions to prototype and test with target users to successfully launch in the market. In addition, the NPD process is based on critical thinking, which is the ability to look at events, conditions, or thoughts with a careful eye and make decisions about the reliability and validity of the knowledge according to standards of logic (Seferoglu & Akbiyik, 2006 ). It involves identifying and analyzing informational sources for credibility, indicating previous knowledge, making connections, and deducing conclusions (Thurman, 2009 ). Higher-order thinking ability provides the opportunity to analyze the existing knowledge or situation to correct mistakes and complete deficits to reach correct conclusions (Howard et al., 2015 ). In this study, the authors select Stage-Gate, which is a macro idea-to-launch product development planning process that involves the Go/No-Go decision-making (Cooper & Kleinschmidt, 2001 ), as the baseline NPD framework because the model is easy to understand among stakeholders in a simple linear system format that consists of detailed guidelines for every stage and explains the criteria for management to make a decision whether to allow the development to pass each gate. These unique characteristics of Stage-Gate model strongly fit within the context of our study. While its principles can be applied, the Stage-Gate model, including the number of stages, activities, and gate criteria, has to be adjusted according to our objectives using the insights obtained from this study.

After the core concept of Stage-Gate model was chosen, several modern State-Gate models were reviewed. The next-generation Stage-Gate process that comes with the Triple A system and spiral concept that promotes the development process to be adaptive, flexible, iterative, and accelerated using a feedback loop from user validation (Cooper, 2016 ) can be applied to the model. Furthermore, there was a study of applying Agile project management methods, which highlights a process that is a dynamic planning process that is adaptive and flexible to changes in product development, into a traditional Stage-Gate system, called Agile-Stage-Gate Hybrids. The results looked promising for faster product releases, quicker and better responses to changing customer requirements, and improved team communication and morale (Cooper, 2016 ). Moreover, case studies in manufacturers conducted by R. Cooper in 2018 also supported the earlier finding; yet it also added some challenges in terms of management buy-in, resources needed and allocation, and fluid product definitions and development plans (Cooper & Sommer, 2018 ). These insights are also similar to the study by Zasa et al. ( 2020 ) who highlighted that agile project management will increase interaction among project stakeholders and help break big tasks into small and achievable action items (called sprints ) within a short period of time. They also suggested that successful implementation required the integration between traditional project planning modes and the agile method, cultural change, and perceptions of all stakeholders in the organization (Zasa et al., 2020 ).

Therefore, by applying modern concepts of Stage-Gate like triple A system with spiral concept and agile development, the earlier Stage-Gate baseline model can be improved in many ways. That is, the model becomes more adaptive and flexible to changing customer requirements and situations, increasingly improved team communication and morale, and further highlights on an iterative process to promote interfacing between the development team and the target user. Moreover, the importance of interfacing with users iteratively for business assumption validation is also similar to the principle of lean startup and design thinking. The lean startup encourages startups to challenge business growth hypotheses and use them to build the minimal viable product (MVP), then test and validate with the real user to learn whether it is required to pivot or preserve. This can be repeated many times during the NPD process; an approach called build–measure–learn (Ries, 2011 ). On the other hand, design thinking uses a designer’s sensibility and methods to match people’s needs to what is technologically feasible and a viable business strategy that can be converted into customer value and market opportunity (Brown, 2008 ).

In addition, the TAM can be useful to consider during the NPD process, in particular with ICT-related technologies. It can provide information regarding the probability of success during the introduction of a new technology and the key drivers of user acceptance to enable proactively designed interventions and strategies targeted at populations of users who may not be inclined to adopt new systems (Venkatesh et al., 2003 ).

Lastly, the authors review the study of product readiness assessment. This is important for our context because there is a misalignment issue from different stakeholders when evaluating the readiness of the new product development. This is a typical problem found when the product is not ready for commercial. Yet the team has to communicate readiness level with stakeholders for different purposes such as fundraising, selling, field testing, etc. The first assessment is the technology readiness level (TRL) which was introduced by the National Aeronautics and Space Administration (NASA) in the 1970s. It is a well-recognized and useful tool to determine the maturity of new technologies. It is also a discipline-independent program that enables more effective assessment and communication. Its nine assessment levels are beneficial to determine the readiness of new technology and/or capability during the technology life cycle, which includes the completion of systems analysis and conceptual design studies, determination from several design options, and decision to start full-scale development (Mankins, 2009 ). Another assessment is the investment readiness level (IRL) proposed by Steve Blank in 2013, which is also divided into nine levels. IRL is used to evaluate how investment-ready a technology is by validating its business model to help investors assess the risk of investment (Blank, 2014 ). Investment readiness can be defined as a set of business development processes that increase business venture readiness as candidates for equity investors (Aernoudt et al., 2007 ). Alternatively, it is the capacity of the business venture to look for external funding, especially from an equity investor, to understand the specific needs required by an investor and be able to give an investor an attractive business proposal with high confidence (European Commission, 2006 ). Entrepreneurs need information and advice on the advantages of raising equity financing, what it means, and how to become investment-ready (Mason & Kwok, 2010 ). In addition, Australia National Investment Council. & Marsden Jacob Associates ( 1995 ) proposed that businesses that are not investment-ready are primarily the result of a lack of information. This means that they do not know about the role of equity finance and are unaware of what is involved in raising money, what is required to attract investors, and how to convincingly express their investment proposals (Australia National Investment Council. & Marsden Jacob Associates., 1995 ).

In this research, the authors use the next-generation stage-gate process as the baseline for the NPD process and then propose the modified NPD framework for new deep technologies that are more suitable for academic research initiation to commercialization in developing markets, called the Augmented Stage-Gate framework. The framework was designed using the insights obtained from in-depth interviews of 19 research teams who had been working on deep tech research and entered the three-month entrepreneurship development program in 2019. The interview was conducted at the end of the program and focused on understanding the pain points in the research-to-commercialization process in terms of entrepreneurship, business development, networking, financial, technology transfer process, progress assessment, and goal. After careful analysis, several recommendations were proposed and integrated into the Augmented Stage-Gate framework as shown in Table 1 .

The Augmented Stage-Gate framework highlights more on the Agile development process, flexible entrepreneurial development program, progress assessment using TRL and IRL, process management specialist to guide along the academic research to commercialization journey and bring in a network of business partners and legal experts to support. Its structure is divided into six stages (innovation ideation, build business case, development, test and validation, launch, and scale-up) with five gates (screening, go to development, go to test, go to commercial, and post-launch review). Here, stage means the process for work to be completed, and gate is for the Go or No-Go decision-making. TRL and IRL assessments, as shown in Table 2 , can be used to evaluate progress in terms of technology and business readiness at each stage.

The Augmented Stage-Gate framework applies the principle of the next-generation Stage-Gate’s triple A system and spiral development, which aims to overcome the typical challenges when handling undefined requirements during initial development, and Agile development, which aims to increase interaction among project stakeholders and help break big tasks into small and achievable action items (Sprints). This is because most customers are uncertain about their needs and so the product definition prior to development is unclear. The triple A model promotes each stage to be adaptive and flexible, agile, and accelerated while the spiral development concept promotes experimentation. This is also similar to what Isaacson ( 2011 ) described Steve Jobs’ philosophy during his development career at Apple that encouraged project teams to fail often, fail quickly, and fail cheaply. With the benefits obtained from the Augmented Stage-Gate core concept, the product design and definition can adapt to new information, customer feedback, and changing conditions along with multiple iterations of validation activities with users or customers throughout the NPD cycle. In addition, it is important to understand that the details of the process and its functions may differ from project to project, especially with deep tech, academic research initiative, and emerging market environment. Therefore, a flexible gating process must be leaner, faster, adaptive, and risk based. Experienced project teams, mentors, and stage-gate committees are also important to guide startup work throughout the NPD process. Additionally, even though the NPD model is represented in a simple linear format, in reality, it is common that each step can be repeated many times and also go back and forth between stages, depending on the readiness, criteria, and requirement to pass each stage.

Then the effectiveness of the Augmented Stage-Gate framework was tested with three cases, to be discussed in Sect. 4. The cases were research teams that joined UTC in 2019 after the new framework had been designed and completed the final stage of the framework by September 2022. The teams were willing to participate in the study. We gathered the information for the cases via observations and interviews.

The authors directly observed the teams as they moved through each stage of the framework. Tangible results such as actual sales, contract execution, regulatory approval, and certifications, were recorded. The authors also had access to relevant documents related to the development process since the teams were required to submit a progress checklist and presentation slides. Information reported (as appropriate to each stage) includes team, research and development progress, regulatory process, business plan, project planning and concept, product design, milestones, risk assessment, technology verification and validation (MVP), market validation, legal activities, IP status, implementation and operations, sales and marketing, and financial activities. These documents were collected and analyzed for the case studies.

In addition to observation, the authors interviewed the stage-gate committees and two or three people from each team (the principal investigator and 1–2 team members). The interviewees were asked to describe the team’s journey, how they applied the Augmented-Stage-Gate framework, and the results they achieved. The interviews also explored any significant challenges encountered during implementation, along with the solutions that the teams developed.

The interviews were recorded and transcribed, with the transcriptions used to create a final summary of the case. The summary was then reviewed and approved by the interviewees. In some cases, we went back to the interviewees multiple times to get additional information or to conduct follow-up interviews when the implementation and results had become clearer.

The Augmented Stage-Gate process of new product development

The proposed Augmented Stage-Gate process, as shown in Fig.  1 , is divided into six stages. In addition, the below detail explains the objective, activity, and criteria to pass the gate of each stage (as also summarized in Table 3 ).

Stage 0: innovation ideation stage. As a technology incubation office, one of the important roles at UTC is to search for impactful deep-tech research in focused areas that potentially impact our way of life and attitudes in all aspects. To achieve this, UTC has been working with various business partners and consultants to gain market insights while studying market research information for mega trends. Using this information, UTC scouts, classifies, and prioritizes potential research projects. After finding candidates, UTC works closely with them through various programs such as boot camp, workshop, and mentoring to develop the entrepreneurial knowledge and skill in order to help conduct an initial business feasibility study. Another advantage is to give entrepreneurs an understanding of the business journey, challenges, and exit plan so that they can prepare themselves with both skills and morale to be ready before launching. Moreover, the entrepreneurial development program is provided in a flexible format both online and offline to suit with the availability of researchers who might have other full-time jobs at the beginning. Usually, the business model canvas (Osterwalder et al., 2005 ), with its nine building blocks template, is used to communicate a firm’s or product’s value proposition, infrastructure, customers, and finances to stakeholders. After completion, the team is ready for the official screening, where the committee board consisting of business, technology, and legal experts will evaluate each research project.

The first step is to identify the target customer and study the user journey to understand the pain points and user insights. Additionally, lead users—advanced users who deal with an individual problem very intensively (von Hippel, 1986 )—are a subset of target users and can be helpful for the research team to test, validate, and gain valuable feedback on the early development product. Like design thinking, the concept starts with understanding the way customers do things and why, their physical and emotional needs, how they think about the world, and what is meaningful to them. This can be done by carefully observing, engaging, watching, and listening to the users and stakeholders, and then crafting a meaningful and actionable problem statement that focuses on the insights and needs (Brown, 2008 ).

The second step is to analyze internal and external market data. This process aims to understand the business environment and will allow us to better plan so that the threats and opportunities associated with the target area of the business are understood. An internal analysis examines factors within the research project and its co-founding team. The preferred analysis is a SWOT (Strength, Weakness, Opportunity, Threat). Meanwhile, an external analysis examines the wider business environment outside the research project. A popular tool for this is the PESTEL five-force analysis. The key to this process is to ensure that there is market demand to continue the tech-market fit development process.

The third step is to complete an initial financial management strategy, including profit and loss analysis, cash flow planning, and fundraising, that can help the entrepreneur understand the business from a financial perspective in different scenarios and help the business thrive. Because deep-tech product development usually requires a large amount of money and lengthy development time, careful planning in this step is much cheaper regarding business risk. It can avoid cash flow issues that may cause the company to go bankrupt or project delays. Moreover, financial planning can be used to estimate how much investment is needed in each venture development stage so that the entrepreneur can develop a successful fundraising strategy for investors or government grant agencies.

The next step is a preliminary study of the IP landscape. This gives the research project a high-level perspective on the constraints and opportunities regarding the potential exploitation and freedom to operate of IP rights. The researchers can conduct this by themselves or consult with the university IP office since normally the university provides IP support through its Technology Transfer Office (TTO) and IP Practicum Clinic, or by outsourcing services to specialized law firms.

After that, it is time for regulatory planning to help the research team understand and anticipate what regulations are required for each target market. For instance, Med Tech requires FDA (Food and Drug Administration) for commercialization, IRB (Institutional Review Board) for conducting a clinical trial in humans, and GMP (Good Manufacturing Practice) for manufacturing medical devices. Meanwhile, the PDPA (Personal Data Protection Act) is required to use personal data. Generally, the university technology office can be a helpful resource for regulatory advice.

Finally, since deep-tech initiates from academic research by nature, the original research team usually consists mainly of tech-savvy people. Therefore, to become a successful venture, it is crucial to find co-founders with business skills to join the team. Business case competitions or networking events within the university ecosystem can help form an organic partnership.

Augmented Stage-Gate framework

Stage 1: build business case stage. The main activity focuses on developing and validating the business model with target users by demonstrating the prototype and then measuring customer satisfaction, interest, or purchase intent. Usually, the prototype in this stage can be nonfunctional and developed based on the concepts of rapid, rough, and right. For example, AI and computer science technology can use UX/UI (user experience and user interface) and wireframe, which is a schematic or blueprint that is useful for thinking and communicating about the software structure among team members, as a prototype to validate the end-to-end solution idea with the user. Moreover, a network of mentors, domain experts, or key opinion leaders, which are mostly university alumni, can be useful resource because they are knowledgeable and experienced, in which they can give truthful advice and validate the solution idea. Another important thing is to interact with real users or customers as early as possible because today users’ roles have become more significant as a new source of innovation than in the past, when innovation was created solely from producers and supplied to consumers via goods and services, as described in Schumpeter’s theory of innovation in 1934 (Schumpeter, 1934 ). By working together, the research team can provide product knowledge, engineering, and manufacturing for innovative users to think and be creative (von Hippel, 1976 ), which means innovators receive an incentive to engage with users to develop innovative designs (Baldwin & von Hippel, 2011 ).

Stage 2: development stage. The main objective in this stage is to develop a workable and functional MVP, validate with the target user, and refine the business model. That is, it aims to improve technology progress and business strategy so that business risk can be reduced. However, it is noted that due to the Agile concept, the startup should target to break the development plan into small and achievable action items so that their hypothesis can be tested and learned often. In addition, validating the MVP in the closest real environment or sandbox, which refers to the environment that allows some players under specific conditions, to enter the market with fewer administrative constraints (e.g., licenses) or legislative requirements (Tsai et al., 2020 ), is recommended to move the MVP and business closer to the commercial version.

Stage 3: test and validation stage. The goal in this stage is to obtain a commercial version of the MVP and business model. To do that, the lean startup’s validated learning concept is applied to this stage because it can show whether the innovation development and business are moving in the correct direction according to the business model. If not, the innovation can be pivoted; a structural course correction to test a new fundamental hypothesis about the product, strategy, and engine of growth. To make the validated learning successful, cause-and-effect questions with actionable and quantitative metrics are essential. After the new features of the MVP are developed, it will be measured with the user to determine if it demonstrates business growth according to the underlying hypothesis, a process can be repeated many times. The benefit of embracing validated learning is to substantially shorten the developmental cycle.

Stage 4: launch stage. The main goal for this stage is to introduce the market of commercial products. The technology development team participates in a build–measure–learn activity to reach the closest version of a commercial product, while the business development team focuses on delivering a commercial final version of the business plan, sales and marketing strategy, IP strategy, regulatory planning, team formation and financial strategy to select the best commercial option with the highest probability of success and return on investment. In addition, if the university IP is used, the team must complete the technology transfer process. Moreover, according to the business model canvas template, this step must ensure that all nine blocks are validated with stakeholders in a way that leads to business growth and the commercial version of the MVP is refined accordingly. The next step is to finalize the IP submission and strategy, consisting of the final IP draft, valuation, and portfolio management, to obtain optimal legal protection and manage the IP effectively. IP valuation, calculated using either cost-based, income-based, or market-based methods, is useful for the entrepreneur to decide on a proper commercialization option and IP valuation for fundraising. Thus, it should be finalized before going to market. Even though IP services can be particularly expensive and time consuming for such early-stage endeavors, the benefit obtained from IP valuation and protection with a well-managed IP strategy generally increases company competitive advantages tremendously after successful exploitation.

The university technology transfer process is an intrinsic part of the technological innovation process. It is the process of conveying results stemming from scientific and technological research to the marketplace and to the wider society along with associated skills and procedures. To achieve a successful technological transfer, many factors must be considered. Souder et al. ( 1990 ) described seven best practices as analytical, facilities, pro-actions, people roles, conditions, technology quality, and organization. Meanwhile, Gorschek et al., ( 2006 ) recommended close cooperation and collaboration between researchers and practitioners. However, both entrepreneurs and tech transfer officers must discuss and plan each option carefully for the benefit of all stakeholders.

After completing the previous steps, it is time to decide on commercialization. Exploiting an innovation is not only about starting a new company, but there are also many other pathways to bring ideas to markets, such as licensing, joint ventures, and M&A (Schaufeld, 2015 ). Thus, to choose which option is suitable, the entrepreneur needs to consider factors such as market opportunity, IP protection, operation risk, time commitment, return on investment, and investment amount. A complete business plan should be developed and carefully verified, so that entrepreneurs can understand the business opportunities and risks in advance. Table 4 shows an example of an option comparison with a weight matrix between spin-offs and licenses. Briefly, the Option A spin-off scores higher than the Option B license, which means it is the more desirable commercial option to an entrepreneur.

Stage 5: scale-up. This activity focuses on collecting and analyzing the feedback obtained after launch, providing newer and better versions of commercial products or business plans using market feedback, and fully penetrating the target market. Several considerations can be analyzed. The first is to assess whether the product is performing according to pre-defined expectations in terms of technical and business aspects such as functionality, revenues, costs, profits, and so on. The second is to check customer satisfaction or anything that affects the company’s value chain, including purchasing raw material, selling the product, and delivering the goods to the customer. Finally, we examine the strengths and weaknesses of the entire NPD process to learn and improve.

Results and discussion

Case studies.

The case studies below highlight the importance of having an NPD framework that is adaptable to deep-tech within university research and emerging market contexts, yet extensive enough to cover all the essential components to transform deep-tech research into an innovation that has a high-fidelity MVP, an accomplished business and market strategy, a clear pathway towards implementation in the real world, and a complete IP strategy and technology transfer process from academia IP.

ReadMe is an artificial intelligence (AI) research project application that began in 2013 to perform Thai object character recognition (OCR) in any scene image, which often has high perspective and distortion error, uneven illumination, and different image resolutions. Additionally, the Thai character structure itself is very difficult to read automatically, particularly using software algorithms, because it consists of a syntactic structure of up to four layers and a strict relationship between words. The research team was conducting research and development internally and working with various industry partners. An e-commerce platform and a railway engineering company were contracted to help understand business demand as well as to improve and optimize the AI model for real-world applications. Nevertheless, after many years the technology remained a research project; early customers did not have purchase intent with a long-term commitment although the Thai OCR reading accuracy was high. Upon applying our Augmented Stage-Gate Framework to ReadMe in 2019, we successfully transformed the deep-tech research into a tech startup named Eikonnex AI ( https://www.eikonnex.ai/ ) that has now secured business deals for commercial use in private companies.

At the screening stage, the project’s potential for exploitation, validity, market feasibility, and technological feasibility was assessed and found to fulfill all the framework’s criteria. ReadMe, a national award-winning research project, was a deep-tech text reader that was in development for six years, had a research prototype proven well in the lab with a TRL of 4 and an IRL of 1, was the state-of-the-art Thai text reader that was more accurate than other better-known OCR technologies, and is a high-potential technology that could impact the business, medical, and transport industries.

Following their selection, the research team carried out innovation framework activities starting with continuous customer validation, that later helped them develop their market research and business plans. A large majority of their customers were banks, driven by the digital transformation trend and strong competition in the financial industry. One of the most challenging and high-volume applications is the personal loan approval credit scoring. Most were unable to automatically read Thai bank statements correctly due to statement template differences from different banks and Thai character challenges, increasing the time required for loan approval. The team saw this opportunity and pivoted their target customer and core technology to become an OCR with automatic template detection to read bank statements instead. After this decision, the team quickly redeveloped their MVP and carried out multiple user validations using the build–measure–learn process. In the meantime, the team worked closely with a network of mentors to adjust and validate the product idea and business plan.

After rigorously applying the framework’s validation activities, the technology underwent a complete transformation and reached commercial readiness. The technology now had a TRL of 7 and an IRL of 7, completed the IP strategy by obtaining a patent for their technique, concluded the technology transfer process, and set up a spin-off tech startup. Moreover, in early 2021 a few months after their establishment as a startup, the company received its first business deal from one of the biggest banks and completed the technology transfer process. Currently, the company is making its first sales by providing Thai document reader solution services either as an API or as a customized technology. They will continue to move towards digital transformation and expand into a coherent document digitization platform.

It is clear that with the support, guidance, and structure provided by the Augmented Stage-Gate Framework as explained in Table 5 , deep-tech research can be transformed into an innovative, high-impact, commercializable product and company in one to two years.

Chest X-ray AI reporter for COVID-19

Following the trend in the use of AI for healthcare, the chest X-ray reporter was an R&D project by physicians and computational researchers that aimed to create AI software that could classify and report abnormalities for physicians to consider as part of their diagnosis. Nonetheless, the technology remained a research project as it lacked a workforce to develop the complete application software and system integration and had no exit strategy.

With the application of our framework and the outbreak of the coronavirus (COVID-19) pandemic, the technology met the immediate needs of society by being able to detect COVID-19 and numerous other conditions from chest X-rays. As of the end of 2021, this innovation was used as a not-for-profit technology in the King Chulalongkorn Memorial Hospital, helping many patients in need.

The technology had a TRL of three and an IRL of one at the time of screening with an alpha version of the AI algorithm. As this project is led by physicians and computational researchers who are experts in the field, it is considered a deep technology with high potential for use in hospitals, especially rural government hospitals that sometimes lack healthcare personnel or technology to analyze chest X-rays efficiently. This innovation may also be adapted for use in other types of X-rays for other diseases and undoubtedly has large potential to improve the accuracy of medical diagnosis. Thus, this research is a good candidate for our Augmented Stage-Gate framework as explained in Table 6 .

Following the development and validation activities of our framework, the research team recruited more AI engineers to develop their algorithms and UX/UI to enable intuitive use of the technology. Here, the code and interface were continuously revised with frequent customer and domain expert validations to select the most relevant features and data for physicians. To protect intellectual property, the technique was kept a trade secret. After using the framework for only one year, the work reached a TRL level of 7 and an IRL level of 7 and gained acceptance for not-for-profit use in the hospital for preliminary screening of COVID-19 and other chest X-ray abnormalities. At present, the innovation is used at Chulalongkorn Hospital. We believe that, with its initial success, the technology can be implemented in other hospitals to help improve patients’ quality of life. The project team is now involved in the process of technology transfer and spin-off.

Progesterone test kit

The progesterone test kit for swine is a medical technology that began with a contracted research project between the Chulalongkorn University Faculty of Veterinary Medicine and a multinational science and technology company. The research team has in-depth knowledge and IP for developing a test kit that can easily test the progesterone level of animals from serum samples. In this research, the industry partner wanted to detect swine progesterone in the form of a strip test as it is a cheap and convenient method for mass adoption. The company promised to license the technology for sales and marketing purposes after the prototype showed promising results.

This research project has a potentially high impact on the local livestock industry. It is a new state-of-the-art technology and is an easy, effective, and low-cost solution that addresses many pain points faced by the swine farm industry. Moreover, we foresaw that the technology could be adapted to detect other hormones and health- or disease-related biomolecules in other livestock, increasing the market size and potential customers in the future. Finally, the initial readiness assessment revealed a TRL of 6 and an IRL of 1.

With our Augmented Stage-Gate framework, as explained in Table 7 , and business directions from the industry partner, the project established its market and business strategy and financial analysis. Moreover, the project team also brought in the qualified diagnostic development (QDD) center of Chulalongkorn University to support strip test design and small-scale manufacturing. Furthermore, with continuous iterations of customer validation, the researchers were able to fit the technology to the user’s needs and better understand the type of collaboration the industry was looking for. Thus, the team had business matching opportunities and discussed plausible deals with potential customers.

After more than 6 months of fine-tuning all aspects of the innovation, the project had a TRL of 7 and an IRL of 7 with a final prototype and licensed their technology to an international company that will use the kit for real-world applications. With the success of their first deal, the team has leverage to make future deals with other private companies.

The Augmented Stage-Gate Framework was used in these cases to validate the potential for exploitation, validity, market feasibility, and technological feasibility. All projects had low levels of investment readiness and different levels of technological readiness at the time of screening but were all considered deep technologies with high potential for use in their respective industries. The framework helped the teams carry out innovation framework activities, including continuous customer validation, market research, and business plans. All projects underwent a complete transformation after rigorously applying the framework’s validation activities, which included developing their MVP, carrying out multiple user validations, and adjusting their product idea and business plan with a network of mentors. In terms of commercial success, ReadMe successfully transformed into a tech startup named Eikonnex AI and secured business deals for commercial use in private companies. Chest X-ray AI Reporter for COVID-19 remained a not-for-profit technology used in King Chulalongkorn Memorial Hospital to detect COVID-19 and other chest X-ray abnormalities. Progesterone Test Kit licensed their technology to an international company. It is shown that the Augmented Stage-Gate Framework effectively transformed research projects into innovative, high-impact, commercialized products and companies.

Past literature has mentioned that traditional Stage-Gate models are not suitable for many of today’s businesses due to fast-changing user needs, uncertain market requirements (Cooper & Sommer, 2018 ), or industry complexity that requires highly iterative cycles and external collaboration (Sommer et al., 2015) and requires a more flexible and adaptive Stage-Gate model such as integrating agile process (Cocchi et al., 2021 ). Case studies leveraging these models were mostly conducted in corporates in developed economies. Directly adopting successful models from developed countries’ academic institutions require a well-established technology transfer office (Ravi & Janodia, 2022b ). Other studies that focus on the academic context in developing countries made suggestions in the policy level, recommending that the government encourage technology transfer by connecting industry and academia (Kirby & El Hadidi, 2019 ; Ravi & Janodia, 2022b ). None has given practical, step-by-step guideline model for technology initiated from academic institutions like ours.

Therefore, our work provides the first proved example of a new product development model that can be applied in similar contexts—commercializing university technology in an emerging economy. It solves the problems that persist in developing countries, Thailand especially, of lack of literature, lack of evaluation from key stakeholders, and a design-actuality gap (Abbasi et al., 2022 ; Heeks, 2002 ; Kalyanasundaram et al., 2021 ; Ravi & Janodia, 2022a ). However, we believe this model can also be applied to ecosystems with better infrastructure and maturity. Once research can be stably commercialized, building a strong infrastructure for technology transfer office like those in developed countries is a task recommended in the long run.

Lastly, even though the result from these case studies can confirm the validity of the proposed NPD model, it is not a hundred percent guarantee of successful exploitation. There might be other factors or circumstances that can affect the result such as market or technology that is highly regulated by local law, certain requirements of entrepreneur characteristics, appropriate timing for market or technology readiness, ecosystem or infrastructure that is required for research to commercial process, especially in emerging markets that might have no mature standard yet, etc. Those mentioned can be considered for future research.

Theoretical implications

This study develops a modified NPD framework that incorporates agile, lean startup, and design thinking to the Stage-Gate model for effective research to commercialization process generated from within the university in developing markets. Using the proposed Augmented Stage-Gate framework that has six stages (Innovation Ideation, Build Business Case, Development, Test and Validation, Launch, and Scale-up), we have presented three case studies from the Chulalongkorn University Technology Center. The approach is structural and based on critical thinking, which helps the technology incubator to accelerate the idea-to-launch process, decide the Go/No-Go of each innovation project stage to prioritize resource contribution, and reduce the risk of failure. Applying an open innovation concept can be beneficial during the NPD process of exchanging internal and external ideas. For example, introducing market demand to guide the direction of research, bringing in high-quality human resources from outside firms to accelerate the research and development, engaging users or customers to trial the product at an early stage, and co-creating the sandbox area to test and validate the innovation. Nevertheless, the project team must have an open mindset and absorptive capability to capture the value of this approach. In addition, university or business incubators should engage legal experts to supervise each activity to avoid conflicts of interest with external parties.

Managerial implications

The actual journey from idea to launch can be different from project to project. Engaging the Next-generation Stage-Gate’s Triple A System, (Adaptive, Agile and Accelerated) and Agile development to the NPD process is very important. Especially during early stages, each project team should focus on setting up a problem statement and then experimenting to learn and fail early, fast, and cheaply. Additionally, we summarized the key lessons learned during the first few batches of the UTC incubation program. First, the importance of the stage-gate committee role and organization as they are the gatekeepers in deciding the Go/No-Go of each project’s stage. The team needs to understand each project very well and be able to effectively track development progress and milestones. Project management software tools can be helpful in sharing ideas and tracking progress among teams, mentors, and committees whose roles must be considered carefully. Second, the incubator is usually responsible for providing NPD guidelines and mentoring for each stage; yet the incubator must also sometimes play a hands-on role solving issues by working closely with each team, especially for topics that they are unfamiliar with or that are at high risk such as regulatory and IP issues. Third, especially during the COVID-19 pandemic period, many activities were conducted online, such as business matching, mentoring, and customer meetings. Online activities lack many of the emotional and social aspects of work done in person. Therefore, the community manager had to work hard to build a supportive environment, maintain momentum and create positive team dynamics. Still, our experience suggests that it is possible to practice a hybrid onsite/online model while maintaining social distancing during the COVID-19 period. Fourth, legal considerations such as NDAs (Non-disclosure Agreements) and co-founder agreements should be considered as early as possible to avoid any conflicts that could cause project delay or failure. Finally, creating an environment where research, business partners, investors, and mentors can get to know each other is very important. These relationships can be developed informally and can lead to successful business deals. However, tech incubators should be able to identify, understand, and manage the expectations and relationships of each party before organizing networking events so that win–win situations can be realized.

Ideas for future research

Further research on the deep-tech NPD framework applied to specific technologies such as Med Tech that require extraordinary activities or have important limitations is needed. Case studies of successes and failures can be very useful. Challenges involving multiple stakeholders in different development journeys can lead to project failure due to miscommunication, lack of transparency, and a lack of legal knowledge. Thus, integrating legal perspectives and creating legal readiness levels in each NPD journey is essential. Finally, an analysis of co-founder characteristics, such as personality and working style, can suggest suitable ways of commercialization to maximize the probability of success.

Availability of data and materials

Not applicable.

Abbreviations

Artificial intelligence

Food and Drug Administration

Gross domestic product

Gross expenditure on R&D

Good manufacturing practice

• Intellectual property

Institutional review board

Investment readiness level

Minimal viable product

National Aeronautics and Space Administration

Non-disclosure agreement

• New product development

Object character recognition

Personal Data Protection Act

Politics, economics, social, technology, environment and legal

Qualified diagnostic development

Strength, weakness, opportunity, and threat

Technology acceptance model

Technology readiness level

Technology transfer office

User interface

Chulalongkorn University Technology Center

User experience

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Acknowledgements

The authors would like to thank Eikonnex AI Co., Ltd., Chulalongkorn University Center for Artificial Intelligence in Medicine (CU-AIM), Chulalongkorn University Center of Excellence in Swine Reproduction, and Qualified Diagnostic Development (QDD) Center of Chulalongkorn University for assisting the required information and being used in the selected case studies. We would like to express our gratitude to the Second Century Fund (C2F) of Chulalongkorn University and the Program Management Unit for National Competitiveness Enhancement (PMU-C) of The Office of National Higher Education Science Research and Innovation Policy Council (NXPO) to support this research project. Lastly, we would like to thank the staffs of UTC, which now forms a research group called Ignite Innovation Lab.

Second Century Fund (C2F) of Chulalongkorn University and the Program Management Unit for National Competitiveness Enhancement (PMU-C) of The Office of National Higher Education Science Research and Innovation Policy Council (NXPO) to support this research project.

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PK, PD, and SK conceived the concept of new product development and entrepreneurship for academic research and technology transfer. PT wrote the manuscript. AA collected data from each research team and the publication templating.

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Kruachottikul, P., Dumrongvute, P., Tea-makorn, P. et al. New product development process and case studies for deep-tech academic research to commercialization. J Innov Entrep 12 , 48 (2023). https://doi.org/10.1186/s13731-023-00311-1

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Mayilvaganan, Naveen, and Juet Jacob. "Integrating Market-based Partners Into Fuzzy Front End of New Product Development." Thesis, Högskolan i Halmstad, Akademin för ekonomi, teknik och naturvetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-40671.

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The business environment is changing rapidly, becoming very competitive and challenging for all firms, and particularly for small and medium enterprises (SMEs). As innovation and new product development represent valuable sources for SMEs’ future sustainability and development, making these processes more effective is essential. Previous literature, with the focus on large firms, underlined the importance of efficiently managing the early period of new product development (NPD), as this can reduce the product’s time to market and increase its performance. For this reason, contributing to a developed understanding of the challenges of medium-sized firms in managing this phase, the fuzzy front-end (FFE) of NPD, is the aim of this study.

The theoretical framework of this study combines prior theories that relate to the difficulties, shortcomings, challenges that SMEs meet during the whole NPD process, including FFE, and theories that resulted from research on FFE in large firms. The structure is based on four elements referring to managing the idea generation process, new product development team, evaluation of product concept feasibility, and the organization of FFE.

A qualitative strategy and a research design with two case studies on high-tech, medium-sized manufacturing firms were used in reaching the purpose of this study. This methodology choice reflects the explorative purpose of this research. The empirical data are mainly primary data, collected during three interviews with development managers and a product developer, completed as well with secondary data like general company information, collected from companies’ websites.

The analysis of empirical findings revealed some relevant conclusions, which can bring value to the research area, and also to the practice. Our findings show that lack of communication with customers during the whole FFE phase, collecting limited or inaccurate information to be processed during this phase, finding the right formalization degree of FFE activities, determining the complexity of the product concept, and assessing external technology and expertise, represent the main challenges faced by medium-sized firms in the FFE of NPD.

The study’s practical relevance consists in the advices and solutions suggested to managers for overcoming the challenges of the FFE phase and improving their results in the development projects. The theoretical implications reflect the importance of organizational size variable in association with the challenges of FFE.

The sample of only two cases and the quality of the empirical data collected from two high-tech Swedish manufacturing firms which have a large focus on innovation are the main limitations of this study, as these medium-sized firms have gained some experience to face the specific challenges of FFE of NPD and the data they provide may be influenced by this aspect.

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Digital twins: The art of the possible in product development and beyond

Industrial companies around the world rely on digital tools to turn ideas into physical products for their customers. These tools have become increasingly more powerful, flexible, and sophisticated since the 1960s and 1970s, when computers first began replacing drawing boards in design offices. Today, product life-cycle management (PLM) has become engineers’ first language: PLM systems help companies to capture, codify, process, and communicate product knowledge across their organizations.

About the authors

This article is a collaborative effort by Mickael Brossard , Sebastien Chaigne, Jacomo Corbo, Bernhard Mühlreiter , and Jan Paul Stein, representing views from McKinsey’s Operations Practice.

Yet as engineering tools have become more capable, the demands placed upon them have also increased. Product functions are increasingly delivered through a combination of hardware and software. Sensors and communications capabilities allow products to offer more features and to respond more effectively to changing operating conditions and user requirements. Advanced, adaptable user interfaces have simplified the operation of complex and sophisticated machines.

Evolving business models are also blurring the boundaries between design and use. Customers expect the performance and functionality of products to improve during their life cycle, enabled by over-the-air software updates or the ability to unlock new features as needed. Many products operate as part of an ecosystem of related products and services. Increasingly, customers are not buying products outright, but paying for the capabilities they provide on a per-use or subscription basis.

The birth of the digital twin

These changing requirements have triggered a transformation in digital product representation and the creation of a new tool: the digital twin. Digital twins combine and build upon existing digital engineering tools, incorporating additional data sources, adding advanced simulation and analytics capabilities, and establishing links to live data generated during the product’s manufacture and use. A conventional PLM system uses one digital model to represent each variant of a product. A digital twin, by contrast, may have one model for each individual product, which is continually updated using data collected during the product’s life cycle.

The digital-twin approach can be applied to products, manufacturing processes, or even entire value chains. In this article, we will focus on their application to products, specifically to product design.

Digital twins offer multiple potential benefits for product-based companies and users. They can aid design optimization, reduce costs and time to market, and accelerate the organization’s response to new customer needs. Digital twins can also be a critical enabler of new revenue streams, such as remote maintenance and support offerings and “as a service” business models.

Based on the experience of companies that have already adopted the approach, we estimate that digital-twin technologies can drive a revenue increase of up to 10 percent, accelerate time to market by as much as 50 percent, and improve product quality by up to 25 percent. Digital-twin technology  is becoming a significant industry. Current estimates indicate that the market for digital twins in Europe alone will be around €7 billion by 2025, with an annual growth rate of 30 to 45 percent. 1 Infinium; MarketsandMarkets; MarkNTel Advisors; Meticulous Market Research; Mordor Intelligence; SBIS; Technavio, last accessed April 2020.

Digital twins in practice

Companies in many different industries are already capturing real value by applying digital twins to product development , manufacturing, and through-life support (exhibit).

An automotive OEM, for example, has used the digital-twin approach to create a concept configurator for early phase development . The start of the development process is especially challenging for complex products because the various stakeholder groups, such as sales, engineering, and finance, may have different or even contradictory product requirements. The OEM now balances these trade-offs using a digital concept configurator that allows for simultaneous evaluation of customer requirements, technical concepts, and product costs. When a technical concept within a system or subsystem of the product is changed, the implications for meeting customer requirements or product cost targets become immediately transparent.

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Using the configurator within cross-functional development teams has helped the OEM to reallocate 5 to 15 percent of a new vehicle’s material costs to the attributes that drive the most customer value. Applying the approach to select customer-facing components has allowed the company to optimize costs and customer value simultaneously, improving the contribution margin of those parts by 5 to 10 percent. As a further benefit, the configurator helped the team reduce the time taken to reach agreement on changes by 20 percent, thus accelerating time to market.

Digital twins are even being used to replicate systems in complex mission scenarios. Using this approach, one aerospace and defense player has cut the time required to develop advanced products by 30 to 40 percent. The digital twin also aids discussion with customers during the development process, helping the company validate and improve its designs.

In the consumer electronics sector, a company is using product digital twins to boost quality and supply chain resilience . It stores detailed information on the content of its products, including the exact source of individual components. In the event of quality issues during production or early failures in the field, the company can trace problems back to specific supplier facilities, then take appropriate action to prevent reoccurrence of the issue. An automotive supplier uses the same approach to trace quality deviations in its production through to the upstream supply chain, and in the process has reduced scrap by 20 percent.

Digital twins are increasingly being used to improve future product generations . An electric-vehicle (EV) manufacturer, for example, uses live data from more than 80 sensors to track energy consumption under different driving regimes and in varying weather conditions. Analysis of that data allows it to upgrade its vehicle control software, with some updates introduced into new vehicles and others delivered over the air to existing customers.

Developers of autonomous-driving systems , meanwhile, are increasingly developing their technology in virtual environments. The training and validation of algorithms in a simulated environment is safer and cheaper than real-world tests. Moreover, the ability to run numerous simulations in parallel has accelerated the testing process by more than 10,000 times. Incorporating sensor data from real-world vehicles into these tests helps companies improve the veracity of their simulations and identify blind spots in the virtual test database.

The mainstreaming of additive manufacturing

A company in the renewable-energy sector is using a digital twin to automate, accelerate, and improve the engineering of hydroelectric turbines . Using the machine learning system to evaluate the likely performance of the new designs allowed it to rate more than a million different designs in seconds rather than the hours required for conventional computational flow dynamics (CFD) analysis. The winning geometry delivers the maximum theoretical performance, significantly higher than what is achievable by conventional optimization methods. Moreover, by using machine learning, the overall end-to-end design cycle time was cut in half compared with the conventional approach.

Digital twins in three dimensions

Digital twins can take many different forms. Organizations that want to take advantage of digital-twin technologies must select an appropriate form that will enhance its technical and business objectives. The design of a digital twin can vary across three dimensions (exhibit).

The first dimension encompasses the value chain steps that the digital twin will cover. An engineering twin covers value chain steps similar to those covered by conventional PLM systems, ranging from product definition to detailed engineering. A production twin replicates a product throughout the manufacturing process, incorporating data such as the components, materials, and process parameters used, as well as the results of tests and quality checks. A service twin incorporates data collected from the product in use, such as operating modes, performance, diagnostic information, and maintenance history. The most sophisticated digital twins span multiple parts of the value chain, allowing in-service data to optimize manufacturing processes or future design iterations.

The second dimension is the scope of the digital twin. A product may consist of several major systems, multiple subsystems, and hundreds or thousands of hardware and software components. Some digital twins cover only one or several components, for example, those that simulate the flow of liquids through a pipe. Others cover a full product, for example, those that simulate a car’s crash characteristics. Given the limitations of computing power, generally, the narrower the scope of a digital twin, the more precise its virtual replica will be. In contrast, full-product digital twins often need to abstract or simplify certain product behaviors to remain manageable.

The final dimension of a digital twin is its degree of sophistication . The simplest digital twins consist of various sources of data relating to a product, often from sources that have few or no links with one another. The second level of sophistication uses traditional simulation tools to perform analyses of design performance and integrate the various sources through a PLM system or similar platform.

At the third level of sophistication, a digital twin will use predictive or prescriptive analytics, as well as machine learning technology to run automated simulation refinements and yield new insights. This allows design and manufacturing teams to make informed decisions based upon direct results and performances.

At the last level of sophistication, digital twins use predictions of component failure rates or performance variations to react to changing environments and manipulate the real-world counterpart in a closed-loop setup. This approach might be used in a condition monitoring system, for example, where sensor data and simulations are combined to make inferences and predictions about the state and behavior of a specific product, and might allow a machine to compensate for wear or variations in operating conditions by adjusting parameters in real time.

Companies in other sectors are also starting to use digital twins to derive deeper insights into customer behaviors and preferences . For example, white-goods manufacturers can use data from in-service products to identify the most and least used features. That can inform future product development decisions, such as deleting rarely used features or revising the user interface to make the features more accessible.

The adoption of digital twins is currently gaining momentum across industries, as companies aim to reap the benefits of various types of digital twins. Given the many different shapes and forms of digital twins (see sidebar, “Digital twins in three dimensions”), and the different starting points of each organization, a clear strategy is needed to help prioritize where to focus digital-twin development and what steps to take to capture the most value.

How to start and succeed on your digital-twin journey

Embarking on a digital-twin journey can look daunting at first sight, especially since the breadth and depth of use cases can span the entire corporate landscape, including product portfolio choices, business model design, R&D, manufacturing, and through-life support.

This versatility can also be a strength, however, as it allows companies to start small and expand the scope, sophistication, and value-chain coverage of their digital-twin projects over time. The experience of companies that have applied digital twins in their own product operations leads to a few simple rules that can greatly increase your odds of success.

Define your aspirations

Be aware of digital-twin best practices. Do your homework and seek out perspectives on best practices and future trends in digital-twin technology. Assess and prioritize the elements of your vision. Evaluate the potential of digital-twin-related opportunities and prioritize them into an implementation road map.

Be clear about the business case. Quantify the value offered by different digital-twin opportunities and determine the minimum level of model sophistication required to generate that value. Successful projects focus on short development times and rapid ROI.

Test the waters by prototyping select use cases. Run a series of hackathons (possibly supported by digital-twin specialists) to assess your capabilities’ baseline, develop solution prototypes, refine, and adjust the initial concepts. This step calibrates the approach and prevents you from losing time and resources by attempting an impossible plan. It is part of a broader value assurance move aimed at bringing the entire project to a successful conclusion.

Know your strengths

Perform a maturity assessment. Understand your current digital product development capabilities along six main dimensions: development methodologies, PLM governance, data strategy, business processes, system complexity, and collaboration. Understanding the areas where you are most advanced and where you are lagging behind will help prioritize areas of investment for a balanced implementation of a digital twin and its use cases.

Access to appropriate talent and capabilities can make or break a digital-twin initiative. Many organizations need to develop additional expertise in areas such as advanced simulation and modeling or data analytics for user experience design.

Plan a step-by-step, agile implementation

Invest several months in developing a minimum viable product (MVP). Incubate a cross-functional, agile team dedicated to bringing priority use cases to life and building digital capabilities in the process. The MVP is now the must-do approach to maximize value gains from the start rather than waiting until the program is finalized before experiencing the first benefits.

Perform an MVP retrospective to pivot or persevere. Derive lessons from the first MVP phase to confirm your digital-twin aspirations or pivot them based on the findings (for example, the validity of use cases, complexity of implementation, and maturity of the organization). This is the second value assurance move that enables you to further calibrate the implementation plan and revise the scope to avoid generating sunk costs.

Scale up the digital-twin initiative and accelerate ROI. Optimize and standardize implementation based on insights from the MVP phase. Define an (internal or external) recruiting and capability-building strategy. Build an operating model to enable rapid scaling of successful approaches. The most advanced organizations typically consider digital-twin technologies a core strategic capability.

By following these simple best practices, you will be able to reap the benefits of digital twins in a scalable, progressive way. Are you ready?

Mickael Brossard is a partner in McKinsey’s Paris office, where Sebastien Chaigne is an associate partner; Jacomo Corbo is a partner in the London office; Bernhard Mühlreiter is a partner in the Vienna office; and Jan Paul Stein is an associate partner in the Munich office.

The authors wish to thank Roberto Argolini, Elia Berteletti, Kimberly Borden, Akshay Desai, Hannes Erntell, Alessandro Faure Ragani, Anna Herlt, Mark Huntington, Mithun Kamat, Michele Manzo, and Alessandro Mattozzi for their contributions to this article.

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Presentation Master's thesis - Martin Ilić - psychological research methods

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Abstract reasoning, the ability to solve complex problems by taking away the unnecessary

details (Clement et al., 2007) in order to derive a rule used in solving similar, novel tasks, is

an essential intelligent behaviour that AI deep learning models are generally not yet capable

of yet appears early on in humans. This thesis investigates whether the Emergent Symbol

Binding Network (ESBN; Webb et al., 2021) is a possible candidate for studying the

mechanisms that underlie how abstract visual reasoning (AVCR) ages and develops in

humans. By manipulating ESBN’s architecture when performing two AVR tasks – identity

rules and distribution-of-three - we tested if it could simulate two main developmental

phenomena, i.e. – that higher working memory capacity and improved inhibition control

promote AVR development. Results showed the ESBN failed to simulate the working

memory phenomenon, while the inhibition control phenomenon could not be tested due to the

model’s near-perfect task accuracy. This makes the ESBN an inadequate model for explaining

AVR development, a finding further research should corroborate.