Alison Olechowski

Technology-Based Design and Sustainable Economic Growth

This paper seeks to analyze how design creates economic value. The literature on knowledge-based economic development has primarily focused on innovation as the analytical lens, whereas design is the original action that leads to innovation. Despite the fundamental importance of design, existing design research has offered few insights and little guidance for national strategies due to the lack of focus on and analysis of design in an economic context. This paper addresses such gaps by linking design research and economic development theory. We first elaborate on the relationship among design, invention and innovation, describing the necessity of design activity for invention and innovation. Our analysis of the fundamental characteristics of design across contexts sheds light on the strategic importance of the accumulative nature of technology-based design for sustaining economic growth. Through the lens of technology-based design, we further quantitatively compare Singapore and three similarly-sized countries (South Korea, Finland and Taiwan). Based upon interview data, we also qualitatively examine Singapore’s national strategy focusing on design. The quantitative and qualitative results align well with the Singaporean government’s use of design as a strategic lever to pursue innovation-driven economic growth, and also reveal its achievements and shortfalls which indicate possible directions for strategic adjustment. & 2012 Elsevier Ltd. All rights reserved. 1. Innovation, invention, and design Innovation is the critical driver of economic growth (Schumpeter, 1934; Solow, 1956), especially in advanced economies which have approached the frontier of knowledge and thus face limited opportunities to adapt exogenous technologies for production (Porter, 1990). Because of its clear importance, there have been numerous studies of how regions and nations can foster innovation through managing such factors as R&D manpower and spending (Mowery and Rosenberg, 1998; Griliches, 1998), industrial environment and competitive dynamics (Rosenberg, 1963; Porter, 1990), government policy and institutional environment (Lundvall, 1992; Nelson, 1993; Freeman, 1995), etc. In particular, the growing body of research on design has added greatly to our knowledge of the innovation process (Baldwin and Clark, 2000; Dym et al., 2005; Weisberg, 2006). However, despite their relevance and importance, the findings and theories from design research have been overlooked in innovation policy and economic development studies (Hobday et al., 2012). This paper supplements the preceding economic ll rights reserved. mit.edu (A.L. Olechowski), development studies on innovation alone by addressing design as the specific activity which results in innovation. In doing so, we build upon prior work which treats design as the process through which innovations emerge (Aubert, 1985; Walsh, 1996), and focus on technology-based design for its specific advantage over other types of design in sustaining economic growth. To our best knowledge, we are the first to link design research and economic development theory. In so doing, the work leads to new insights for national strategies for an innovation-driven economy. Innovation, as defined by Schumpeter (1934), is ‘‘new combinations’’, and also – in the language of economics – ‘‘the setting up of a new production function.’’ Schumpeter’s concept of innovation includes technical, marketing and organizational activities. According to Solow (1957), technology-based innovation accounts for more than 80% of long term economic growth and has been the emphasis of most studies on ‘‘innovation’’. Technology innovation refers to the introduction of a new product, improvement in quality, and a new method of production, etc. (Hagedoorn, 1996). Innovation comes after invention and is invention that has successfully diffused in use, achieving real economic and social impact. Both invention and innovation emerge through a design process. Design is defined herein as a human process that uses knowledge to produce novel objects that are appreciated by or are useful to other humans. Inventions are creatively designed by humans with new mechanisms and/or new functions. The most J. Luo et al. / Technovation 34 (2014) 663–677 664 recognizable inventions historically, such as the steam turbine, the electric generator, the light bulb, the car and the computer, were all ‘‘designed’’ and are thus ‘‘design output’’. However, not all design efforts will necessarily result in invention, as some efforts result in less novelty than judged necessary for the label of invention. In a similar sense, not all inventions (despite their useful novelty) have sufficient benefits or are communicated in a way to result in adequate efforts to achieve diffusion and thus become an innovation. The relationship between innovation, invention and design output is shown in Fig. 1. Design activities create the possibilities for invention and innovation, but do not guarantee them. The design output may be inventions or not, and in turn inventions may become innovations or not. However, innovation scholars on occasion overlook the design process, largely because the design process is difficult-to-anticipate and even difficult to recognize objectively. In contrast, the term ‘‘design’’ is used more often than ‘‘innovation’’ and ‘‘invention’’ by technologically-based practitioners, simply because design is the specific action which humans pursuing innovation actually perform. Thus, when one thinks about enhancing innovation, promoting design activities is more actionable than the narrative focus of innovation. In turn, design capability enables continual delivery of new products, services, and solutions, so is important as a strategic asset for a firm, region or nation to build up in order to compete in a knowledge-based global economy. Mastering it will give firms or regions sustainable competitive advantage (more detailed explanations are in Section 2.3). Therefore, focusing on promoting design activities and building up national design capability as explicit national strategies allows one to be more specific about what can be done for innovation. When considering ‘‘design’’, many studies combine various kinds of design in questionable ways; for example combining engineering design with industrial or aesthetic design (Candi and Saemundsson, 2008) and sometimes combining what ‘‘CAD (Computer Aided Design) technicians’’ do with engineering design (Walsh, 1996). This ambiguity has limited the potential for effective actions to be taken. Following a survey and synthesis of the broader deign research literature in Section 2, we link design to an economic context as is necessary for innovation, and doing so allows ‘‘technology-based design’’ to appear fundamentally most valuable for driving and sustaining economic growth. We use ‘‘technology-based design’’ instead of an equivalent term ‘‘engineering design’’ (Dym et al., 2005) in order to explicitly emphasize the intensive use of scientific and technological knowledge and techniques in such processes. On that basis, we further use ‘‘technology-based design’’ as the analytical lens to examine national attempts to move towards an innovation-driven economy. We particularly examine Singapore, assisted with a comparison with Taiwan, Korea and Finland. All four of these countries have been heavily involved in moving into

Improving the systems engineering process with multilevel analysis of interactions

Abstract The systems engineering V (SE-V) is an established process model to guide the development of complex engineering projects (INCOSE, 2011). The SE-V process involves decomposition and integration of system elements through a sequence of tasks that produce both a system design and its testing specifications, followed by successive levels of build, integration, and test activities. This paper presents a method to improve SE-V implementation by mapping multilevel data into design structure matrix (DSM) models. DSM is a representation methodology for identifying interactions between either components or tasks associated with a complex engineering project (Eppinger & Browning, 2012). Multilevel refers to SE-V data on complex interactions that are germane either at multiple levels of analysis (e.g., component versus subsystem) conducted either within a single phase or across multiple time phases (e.g., early or late in the SE-V process). This method extends conventional DSM representation schema by incorporating multilevel test coverage data as vectors into the off-diagonal cells. These vectors provide a richer description of potential interactions between product architecture and SE-V integration test tasks than conventional domain mapping matrices. We illustrate this method with data from a complex engineering project in the offshore oil industry. Data analysis identifies potential for unanticipated outcomes based on incomplete coverage of SE-V interactions during integration tests. In addition, assessment of multilevel features using maximum and minimum function queries isolates all the interfaces that are associated with either early or late revelations of integration risks based on the planned suite of SE-V integration tests.

Technology readiness levels at 40: A study of state-of-the-art use, challenges, and opportunities

The technology readiness level (TRL) scale was introduced by NASA in the 1970s as a tool for assessing the maturity of technologies during complex system development. TRL data have been used to make multi-million dollar technology management decisions in programs such as NASAs Mars Curiosity Rover. This scale is now a de facto standard used for technology assessment and oversight in many industries, from power systems to consumer electronics. Low TRLs have been associated with significantly reduced timeliness and increased costs across a portfolio of US Department of Defense programs. However, anecdotal evidence raises concerns about many of the practices related to TRLs. We study TRL implementations based on semi-structured interviews with employees from seven different organizations and examine documentation collected from industry standards and organizational guidelines related to technology development and demonstration. Our findings consist of 15 challenges observed in TRL implementations that fall into three different categories: system complexity, planning and review, and validity of assessment. We explore research opportunities for these challenges and posit that addressing these opportunities, either singly or in groups, could improve decision processes and performance outcomes in complex engineering projects.

Improving the Systems Engineering Process with Multi-Domain Mapping

The systems engineering V (SE-V) is the standard model to guide development of complex engineering projects (INCOSE 2011). The SE-V involves decomposition and integration of system elements through a sequence of tasks that produces both design and testing specifications. This paper explores a new method to improve SE-V implementation by applying multi-domain mapping (MDM) and design structure matrix (DSM) models in a novel way for analysis of both the system architecture and the system integration tasks. We illustrate our preliminary work using this method with data collected during the early development stage of a large engineering project in the offshore oil industry, including the component DSM, integration task DSM, and corresponding domain mapping matrix (DMM). We discuss findings in terms of data collection, aggregation, visualization, and potential insights for addressing system integration challenges.