Artificial intelligence is a transforming design practice. This research explores human-AI interaction in relation to human centred design principles in early stage design projects. Using a qualitative workshop methodology, this empirical study took a multidisciplinary team of participants from a yacht manufacturer through a series of divergent, discover phase activities that were augmented by AI tools. The results demonstrated how the advanced capabilities of AI to rapidly analyse vast quantities of data could be purposefully implemented to enhance engagement. the role off facilitator as an intermediary between the AI and participants allowed the interface between human and AI to be moderated and provided insights into effective effective use of AI during the fuzzy front end.

The integration of Model-Based Systems Engineering (MBSE) and data analytics (DA) has introduced a novel approach, Data-Driven Model-Based Systems Engineering (DDMBSE), which combines structured system modelling with data-driven insights. DDMBSE offers the potential for improvements in model optimisation, economic efficiency and the implementation of dynamic system updates based on real-time data. However, the diverse applications of DDMBSE lack a structured overview of its use cases. This paper addresses this gap by proposing a comprehensive framework for the categorisation and description of DDMBSE use cases. It provides users with a structure to navigate within DDMBSE landscape, consolidate knowledge, and identify underexplored areas for future research. This contribution establishes a foundation for advancing the implementation of DDMBSE across industries and fostering its adoption.

Bioinspiration offers an innovative approach to product design. A key challenge is selecting suitable biological features for complex engineering problems. The phenomenon of convergent evolution, where distantly related organisms independently develop similar functions, adds to this complexity. This study introduces novel meta-level design parameters to systematically select biological features with differing geometries yet similar functions. These parameters were derived through physical testing and numerical analysis of woodpecker-beak-inspired and Balanus-inspired structures, focusing on their impact resistance capabilities. These structures demonstrate potential for practical applications, such as in bicycle helmet liners.

Lightweight design is critical for improving the efficiency and sustainability of engineering applications. Laminated composites, with their high strength-to-weight ratio and tailored material properties, play a key role but introduce interlaminar stresses, particularly near free edges where delamination failures often occur. Addressing these stresses typically requires computationally expensive 3D finite element simulations, limiting their use in early design stages. This study presents a machine learning approach using Gaussian process regression and artificial neural networks to efficiently predict interlaminar stresses based on in-plane stress data from shell FE simulations. Achieving high predictive accuracy, this method enables cost-effective, early-stage composite design optimization under complex loading scenarios.

This paper presents an Integrated VDD Approach, formulated to address the lack of, and limitations associated with, work concerning the application of VDD to product families. The focus of the results obtained from the application of the Integrated VDD Approach, and the subsequent discussion, will be on the identification of break points to aid objective decision making early in the design process. Results include the identification of the most valuable common wingspan across three conventionally powered aircraft and the identification of the additional system mass which would render an aggressive electrification strategy to facilitate earlier electrification of an initially conventionally powered aircraft futile in comparison to a nominal electrification strategy.

Many developments, such as the Amazon Fire Phone and Microsoft Zune, fail in the market, often due to addressing non-existent needs or providing no added value. Therefore, it is necessary to validate these needs and benefits in the early phases of development projects. One way to do this is by using a product profile that models needs and benefits and makes them accessible for validation. According to the literature, there are nine challenges and four fields of action for developing a design support in validating these product profiles. These fields of action range from stakeholder integration, method selection, and prototyping to the interpretation of results. This publication evaluates and describes the challenges and fields of action derived through expert interviews and literature research. A total of 28 publications were analyzed, and eight expert interviews were conducted.

Today, Manufacturing companies are adopting a servitization strategy and Product-Service System model to enhance value and remain competitive. Often, this transition also means to embrace a System-of-Systems (SoS) perspective. Concurrently, companies face challenges with volatile, uncertain, complex, and ambiguous (VUCA) environments. One way to tackle VUCA is to utilize simulation modeling. However, developing SoS simulations can be complex and cumbersome. This paper extracts lessons learned from six case studies to identify effective and ineffective practices in developing simulation models. The analysis has led to nine design principles for more effective simulation modeling. Furthermore, the paper explores simulation techniques for modeling SoS and discusses effective VUCA management. Finally, the paper proposes four future research directions to advance SoS simulation research.

This paper examines the effects of prototyping on design space exploration (DSE). Based on a literature review, a study design is proposed that attempts to integrate a longitudinal view from downstream development steps in the point-in-time investigation of design fixation. This study design is tested in a pilot study, the results are presented and discussed. The observation of participants’ design fixation (DF) in downstream activities shows that the need to create prototypes limited DSE behaviour, and suggestions for further adaptation of the study design are made. Challenges related to group dynamics, bias and logistical issues highlighted the need for a more refined study design. The findings highlight the role of prototyping in limiting DSE behaviour and suggest improving metrics, refining interventions and using structured moderation to improve future DF and DSE research.

This paper presents a case study within a small manufacturing company, engaging in the early phases of co-designing Mixed Reality assembly instructions. Using generative tools through two co-design workshops, we engaged the participants in reflecting, visualising and defining their processes, needs, challenges and future ways of working in relation to new technological applications. We gained insights into the participants’ current practices and identified areas where new technologies could improve these practices. We co-designed a lock assembly instruction paper prototype to use as support for future MR development, focused on their apprenticeship training. We also uncovered other areas of technological implementation, setting the framework for co-designing a customised production system.

This paper explores the employment implications of integrating service robots in waste management. Using the scenario technique method, 14 critical influencing factors were identified and analyzed to develop a Best-Case, Worst-Case, and Trend scenario. A SWOT analysis was used to identify implications and develop measures. The findings indicate that service robots can enhance working conditions and enable service expansion but pose risks like job displacement without proper education and reskilling. The study underscores the need for regulatory frameworks, workforce adaptation, and education to ensure socially sustainable robotic integration.

A design catalog is a repository of design problems and their solutions, enabling designers to explore and discover applicable solutions for their specific design challenges. Creating such catalogs has depended on human knowledge and implicit judgment, with no systematic approach established. This study aims to develop a systematic method to create a design catalog from patent documents. We utilize a large language model (LLM) to extract problem-solution pairs described in the documents, presenting them as general purpose-means pairs. Subsequently, we create a design catalog by classifying the problems using similarity-based clustering, enhanced by the LLM’s semantic text similarity capabilities. We demonstrate a case study of creating a design catalog for martial arts devices and generating new design concepts based on the catalog to verify the effectiveness of the proposed method.

The marine industry is increasingly adopting platform and modular design strategies while facing growing sustainability regulations and emission constraints. This paper proposes an approach that integrates scope 3 upstream CO2 emissions (i.e., procurement) into a Decision Support Environment (DSE) for design space exploration of alternative modular ship design concepts. The DSE, deployed in the conceptual design stage, enables simultaneous testing of various cruise ship configurations regarding CO2 emissions using a bottom-up approach with parametric CO2 models. It leverages data-driven models from existing databases or AI-generated data exemplified in a case study on the hotel system of a cruise ship illustrates how parametric design variables influence CO2 emissions, demonstrating a preliminary result of a prescriptive study in collaboration with a major international ship manufacturer

The aim of this research is to analyze the potential of Generative Artificial Intelligence (GenAI) to support the design process and overcome creative fixation in teams during the initial problem framing, ideation and concept exploration stage. Fixation is a common problem in design, and can be exacerbated during collaborative work due to diverse issues such as team dynamics or perceived hierarchy. Current research is exploring whether AI can help teams overcome this problem or on the contrary, might actually contribute to it. Through a creative ideation workshop with design students, we investigate how AI influences team dynamics as well as the creative results. We propose a conceptual model to work with AI in a team setting.

This study addresses the challenges of applying traditional Life Cycle Assessment (LCA) during early-stage product development by proposing a Streamlined Life Cycle Assessment (SLCA) approach. Traditional LCA, while robust, is often inaccessible due to its complexity, time requirements, and cost, making it impractical for many industries. The developed SLCA approach offers a simplified alternative by leveraging tools like artificial intelligence, 3D modeling, and secondary databases. The SLCA methodology was validated through a case study on an electronic device, demonstrating a 69.77% reduction in input requirements, a 91% decrease in time spent, and an average accuracy of 90.05% compared to traditional LCA. These results highlight the potential of SLCA to enable designers to identify environmental hotspots early in the design process, fostering sustainable product development.

Scenario planning is often used in design practice to foster futures thinking, reduce uncertainties and improve decision-making. Scenarios are especially relevant for innovation activities in manufacturing companies, such as technology development, a particularly uncertain process where many trade-offs occur. This study is, to the authors knowledge, the first to empirically measure the effect of scenarios in decision-making quality, in the context of technology development. In a quasi-experiment, engineers from a manufacturing company and university students were independently asked to analyse a trade-off situation between environmental and financial aspects of a technology concept, with and without scenarios. The quality of decision-making quality for control and experiment groups was measured through a standardized questionnaire. The results show that scenarios had a positive impact in 6 of the 7 quality decision-making practices (QMDP), although the effect size is small. The results suggest that both expert and novice designers may benefit from using scenario planning when performing early-stage design activities by having awareness of the decision context, a more structured decision process, and clearer decision criteria.

Product architecture decisions are made early in the product development process and have far-reaching effects. Unless anticipated through experience or intuition, many of these effects may not be apparent until much later in the development process, making changes to the architecture costly in time, effort, and resources. Many researchers through the years have studied various elements of product architecture and their effects. By aggregating observations on the effects of architecture strategies from a selection of the literature on the topic and storing them in a systematic data set, this information can be recalled in a matrix structure which allows for the identification, comparison and evaluation, and then selection of the most desirable product architecture strategies before expending resources along any development path. This paper introduces this matrix, referred to as the Product Architecture Strategy and Effect (PASE) Matrix, how to construct one, and a demonstration of its use.

Autonomous vehicles for the last mile are a promising use case for advancing autonomous driving in real-world traffic. For this purpose, traditional car manufacturers and newcomer companies develop a new vehicle concept: the autonomous shuttle. During the development, components from the automation domain, such as the sensors, must be placed and integrated into the vehicle body. The trade-offs between the functional performance of the perception and the exterior design must be evaluated early in the design process. For this purpose, a model of the vehicle exterior is needed. In this contribution, we present a method for parametric modeling of the vehicle exterior of autonomous shuttles. We define 17 input parameters and use computer-aided design to create a virtual model of the body and the wheelhouses. In the results, we validate our method by ensuring that existing shuttles can be modeled with our approach and also analyze the limitations. The model supports decision-making in the early design phase by enabling quick iterations between sensor placement and exterior design.

Real vehicle usage rarely matches the predictions made during early phases of vehicle development and sales processes at commercial road vehicle manufacturers. The automotive industry needs multidisciplinary vehicle design methods to predict real-world vehicle operations by considering the vehicle level and the transport system level simultaneously, in a more holistic approach. The aim of this study was to analyse how realistic vehicle usage of driverless multipurpose vehicles can be modelled in Vehicle Routing Problems (VRPs) by conducting a systematic literature review. We found that real vehicle usage modelling of driverless multipurpose vehicles in VRPs mainly depended on the following elements: VRP variant, energy consumption model, energy consumption rate class, number of vehicle-specific design variables and transport system-level factors. Furthermore, we identified in the literature five classes of energy consumption rate edge behaviour in VRPs. These findings can support decision-making in the modelling process to select the most suitable combination of elements, and their level of detail for the overall modelling aim and purpose.

While a significant amount of research has documented the importance of design artefacts in design communication, relatively little work has investigated the effect of design artefact quality on the development of a shared understanding between designers. In the current work we focus specifically on sketch quality and the effect of sketch quality on the shared understanding of design dyads. A controlled study with 22 design dyads (44 designers) was conducted to understand the relationship between sketch quality and shared understanding. Results suggest that design artefact quality, measured by sketch understandability, does not predict the shared understanding of a design concept. Our findings hold implications for the fundamental ways in which we evaluate sketch quality and the importance of artefact fidelity for communicative acts.

The requirements space is increasing due to non-functional areas such as security, resilience and sustainability gaining in importance. This creates a complex and dynamic space which makes it hard for engineers to take good data driven design decisions. Increasing the quality of design decisions allows to better set up development projects and develop more successful products and services. The design can most heavily be influenced in the early design phases, where design flexibility is high and resource commitment is low. Unfortunately, the system knowledge is also low in early phases. The Engineering Graph is a concept that connects data from different internal and external sources. It allows to connect product data stored in Product Lifecycle Management systems with system models and also add external sources from the Wikimedia Knowledge Graph, World Health Organization and World Bank. This interconnected data allows the support of engineers in managing the complex and dynamic requirement space and provide high system knowledge in the early design phases to support design decisions.