Innovative health technologies offer much to patients, clinicians, and health systems. Policy makers can, however, be slow to embrace innovation for many reasons, including a less robust body of evidence, perceived high costs, and a fear that once technologies enter the health system, they will be difficult to remove. Health technology funding decisions are usually made after a rigorous health technology assessment (HTA) process, including a cost analysis. However, by focusing on therapeutic value and cost-savings, the traditional HTA framework often fails to capture innovation in the assessment process. How HTA defines, evaluates, and values innovation is currently inconsistent, and it is generally agreed that by explicitly defining innovation would recognize and reward and, in turn, stimulate, encourage, and incentivize future innovation in the system. To foster innovation in health technology, policy needs to be innovative and utilize other HTA tools to inform decision making including horizon scanning, multicriteria decision analysis, and funding mechanisms such as managed agreements and coverage with evidence development. When properly supported and incentivized, and by shifting the focus from cost to investment, innovation in health technology such as genomics, point-of-care testing, and digital health may deliver better patient outcomes. Industry and agency members of the Health Technology Assessment International Asia Policy Forum (APF) met in Taiwan in November 2023 to discuss the potential of HTA to foster innovation, especially in the Asia region. Discussions and presentations during the 2023 APF were informed by a background paper, which forms the basis of this paper.

This exploratory study set out to pilot use of a Risk Innovation approach to support the development of advanced biopreservation technologies, and the societally beneficial development of advanced technologies more broadly. This is the first study to apply the Risk Innovation approach — which has previously been used to help individual organizations clarify areas of value and threats — to multiple entities involved in developing an emerging technology.

Despite the potential to enhance efficiency and improve quality, AI methods are not widely adopted in the context of product development due to the need for specialized applications. The necessary identification of a suitable machine learning (ML) algorithm requires expert knowledge, often lacking in companies. Therefore, a concept based on a multi-criteria decision analysis is applied, enabling the identification of a suitable ML algorithm for tasks in the early phase of product development. The application and resulting advantages of the concept are presented through a practical example.

Automotive OEM introduced Product-Service Systems in the past 20 years, challenging their traditional business model. A qualitative study was developed to characterise the decision-making process across 6 case studies, and similar patterns across different enabled the identification of lessons learned and possible future implications. All PSS initiatives were introduced following an Agile/Lean experimental approach, but the opportunistic nature of trials casts doubts in future validity. New testing methods that generate more robust conclusions need to be developed.

With the shift from mechanical value delivery to mechatronic value delivery, development environments are becoming more complex. Intuitive decision-making in development management is becoming increasingly challenging. Meanwhile, the use project management software is spreading, bringing about a new level of project data for development projects, holding to potential to enhance human decision making. To this end, the paper presents an extension to factor analysis of mixed data, which can facilitate usage of exploratory data analysis to improve decision-making in development project planning.

Trade-offs involving multiple criteria that cannot be satisfied at the same time are ubiquitous in engineering design activities. Navigating trade-off decisions can be challenging, especially when it comes to sustainability-related decisions in early-stage projects. Through a systematic literature review, we unravel the challenges related to sustainability trade-offs in technology development, concept design, and other front-end of innovation activities. The challenges, which were evaluated by experts from industry and academia, range from technical and organisational to psychological aspects.

This paper discusses approaches for tradespace analysis, exploration, and visualization to address multi-objective decision-making. Next, computational tools for early-stage tradespace analysis to enhance programmatic decision-making are introduced via a vehicle design example to demonstrate the effectiveness and capability of the method. Using a smaller sample of technologies in this problem a synthetic tradespace spans the space of potential and available solutions and provides an opportunity for design engineers to develop an insight into possible technologies and solutions within the tradespace.

Scholars in economics, psychology, and business have recently defined narrative as the underlying mechanism by which humans internally process information and drive a decision forward. In this paper, we study narrative's use in design across Design Society publications. We discuss how narrative's role as the driver of design decision-making is an important, but missing, element of the design literature. We explain how engineers will be expected to move the design process forward despite facing decisions where the information is simultaneously too much to process, conflicting, and incomplete.

To achieve higher functionality in mechatronic systems, the management of disturbance factors plays a crucial role. For this purpose, a method was developed in prior works to address this management via the optimisation of product structures. However, this method lacks applicability due to the complexity of one step of the method. It is the goal of this paper to present a software tool, utilizing cluster-analysis to sort components into assemblies, with which this step is supported. Additionally, the method is generally adapted to address a wider spectrum of phenomena in mechatronic systems.

Designing an equally usable and emotionally appealing product remains a challenge for product developers, not least due to conflicting goals. Product developers need to constantly map the affective user requirements to the product, whereby the requirements for the emotional and usable product design often cannot be equally addressed. The systematic approach presented can help product developers in conflicting decision-making situations to represent these affective user requirements by selecting and prioritising context-relevant influencing factors using multi-criteria decision-making methods.

This study explores challenges in decision-making for product design due to insufficient cost transparency because of product variety across the value chain. Utilizing a literature review and a case study on a company, it delves into issues such as value chain consideration, product family assessment, linking effects to specific product levels, and converting measured effects. Highlighting the critical need to address these challenges for decision-making. Future research should focus on a comprehensive costing framework, explore effect interdependencies, and expand the value chain analysis.

We present a data-driven approach to support decision-making in CAD modelling and to improve design for manufacturing. Based on automated estimated production planning, information is provided on possible design actions and their impact. A study was conducted on perspectives on and visualizations in CAD modelling. Requirements for a user interface of the described support system were identified. The results serve as basis for further research and development on the interaction of engineering designers with data-driven decision-making support in CAD modelling.

In the field of 3D model reconstruction, manifold methods have been developed that derive CAD models from 3D scan data. Opposed to classical CAD modelling, where surface and solid modelling exist, a further diversification of modelling techniques is observed, caused by different methods to build up the geometry. This research introduces a new classification, the so-called Level of Complexities. It can be applied to the complete Reverse Engineering process chain and lays the foundation for further research on how to match requirements arising from all process steps and downstream applications.

Multiple industries have hailed lightweighting promise to reduce the mass of their product at equivalent or improved performance. Lightweighting as a strategy encompasses lightweight end-product desired attributes and through-life processing decisions. Assessment of lightweighting gathers information for decision-making towards the optimization of these strategies. An exploratory study, using systems thinking is conducted, to identify requirements of lightweighting and its assessment in terms of holistically defining its impact on the sustainability of its background system, the Earth.

In the age of digitalization, navigating through vast amounts of data is a challenge. Augmented analytics, which often goes unnoticed by employees, has the potential to support effective decision-making. This study examines the impact of digital nudging on editors' cognitive load and behavioral change towards augmented analytics, providing insights into behavior change design. Combining theory with expert interviews and workshops, this study results in five nudging strategies. The findings reveal varied triggers influencing behavioral change, emphasizing stakeholder involvement in the process.

This study presents a search method for a solution space that aligns with a designer's design intent. The proposed method uses multiobjective optimization to determine the size of the narrowed solution space and the weakness of the constraint relationships between the design variables. The suitability of the proposed method is tested by applying it to the design problem of an electric motor for an EV, aiming to provide designers with solution spaces that offer a high degree of freedom in the later design stages and that have weaker constraint relationships among the design variables.