Link back to DTU Orbit Citation (APA):Oehmen, J., Olechowski, A., Kenley, C. R., & Ben-Daya, M. (2014). Analysis of the effect of risk management practices on the performance of new product development programs. Technovation, 34(8), 441-453. DOI: 10.1016441-453. DOI: 10. /j.technovation.2013 Analysis of the effect of risk management practices on the performance of new product development programs Highlights Investigates the association between risk management practices and new product development program performance  Based on extensive empirical data collected through survey  Presents new framework to define risk management success in NPD programs  Identifies six categories of risk management practices associated with success  Out of 95 analysed risk management "best practices", only 30 show significant associations with success. AbstractRisk management is receiving much attention, as it is seen as a method to improve cost, schedule, and technical performance of new product development programs. However, there is a lack of empirical research that investigates the effective integration of specific risk management practices proposed by various standards with new product development programs and their association with various dimensions of risk management success. Based on a survey of 291 new product development programs, this paper investigates the association of risk management practices with five categories of product development program performance: A. Quality Decision Making, B. High program stability; C. Open, problem solving organization; D. Overall NPD project success and E. Overall product success. The results show that six categories of risk management practices are most effective: 1. Develop risk management skills and resources; 2. Tailor risk management to and integrate it with new product development; 3. Quantify impacts of risks on your main objectives; 4. Support all critical decisions with risk management results; 5. Monitor and review your risks, risk mitigation actions, and risk management process; and 6. Create transparency regarding new product development risks. The data shows that the risk management practices are directly associated with outcome measures in the first three categories (improved decision making, program stability and problem solving). There is also evidence that the risk management practices indirectly associate with the remaining two categories of outcome measures (project and product success). Additional research is needed to describe the exact mechanisms through which risk management practices influence NPD program success. KeywordsRisk management, new product development, program management Manuscript (Final Revision): Effect of Risk Management Practices

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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 NASA's 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.2

The technology readiness level (TRL) scale was developed at the National Aeronautics and Space Administration (NASA) in the 1970s as a standardized technology maturity assessment tool for use in complex system development. Today, TRL assessments are used to make multimillion‐dollar decisions at NASA and beyond, yet anecdotal evidence suggests that there are challenges associated with TRL use in practice. In this paper, we systematically uncover the practitioners' view, first via 19 interviews with employees from seven organizations. We identify 15 challenges of TRL implementations in three categories: system complexity, planning and review, and validity of assessment. Next, we prioritize these challenges via a survey of TRL practitioners, using a best‐worst choice experiment. Finally, we identify best practices and proposed extensions to address the challenges. We find that system complexity challenges are most critical to TRL users, despite being addressed in the literature. We posit that addressing these opportunities could result in substantial improvements to decision processes and outcomes in complex engineering projects.

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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.

Kimberly is an enthusiastic Engineering Science student at the University of Toronto specializing in Biomedical Systems Engineering. She is a passionate proponent of women in STEM. She is an awardwinner in engineering competitions across Canada and beyond in areas including super-resolution microscopy, machine learning solutions for health care, and space missions for microbiology research.

This paper seeks to analyze how design creates economic value. The literature on knowledgebased 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 innovationdriven economic growth, and also reveal its achievements and shortfalls which indicate possible directions for strategic adjustment.Keywords: Technology-based design; invention; innovation; design capability; economic growth. Research Highlights• This study links design research and economic development theory, and offers insights for national strategy for economic growth.• The paper first identifies that the accumulative nature of technology-based design is strategically important for sustaining economic growth.• The paper evaluates potential metrics for assessing technology-based national design capability, and applies the currently feasible ones to comparing four similarly-sized countries.• The paper assesses Singapore's national design strategy for economic growth through the lens of technology-based design. Innovation, Invention, and DesignInnovation 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 fin...