This study explores the application of competency mapping models, incorporating in knowledge management for consulting firms. It evaluates 15 different models, focusing on their suitability for consulting contexts based on data collection, advantages, risks, and limitations. The findings indicate that AI and ML-enhanced competency mapping models are particularly more effective in consulting firms. Finally, the article proposes three key applications of these models for improving knowledge management in consulting firms via empowering communities and collaboration.

Despite the recognized importance of datasets in data-driven design approaches, their extensive study remains limited. We review the current landscape of design datasets and highlight the ongoing need for larger and more comprehensive datasets. Three categories of challenges in dataset development are identified. Analyses show critical dataset gaps in design process where future studies can be directed. Synthetic and end-to-end datasets are suggested as two less explored avenues. The recent application of Generative Pretrained Transformers (GPT) shows their potential in addressing these needs.

The utilization of data in design is a crucial aspect of shaping the product and service development. Despite the lack of extensive research on this subject, this study aims to bridge the gap by introducing the ‘D³IKIT’, a data-driven design process and toolkit. Through workshops, this process and toolkit offer a practical method for creating innovative product and service concepts using data and machine learning. Developed and tested with the participation of 42 individuals, the ‘D³IKIT’ provides valuable insights for both practitioners and academics.

The optimization of mechanical behavior in safety systems during crash scenarios consistently poses challenges in vehicle development. Hence, a reinforcement learning-based approach for optimizing restraint systems in frontal impacts is proposed. The trained agent, which adjusts five parameters simultaneously, is capable of minimizing loads on a seen and unseen anthropomorphic test device on the co-driver position and is thus able of transferring knowledge. A hundred times higher rate of convergence to reach a similar optimum compared to a global optimization algorithm has been achieved.

The growing emphasis on sustainability integrates eco-design and life cycle analysis into product development. Despite the value of LCAs, data limitations lead to assumptions, impacting accuracy. This study compares an estimation-based LCA with a data-driven approach, focusing on a laser machine's operational phase. The significant influence of resource consumption during operation underscores the necessity of optimization. Applying a data-driven approach reveals a 24% difference compared to the estimation-based method, emphasizing the challenges in obtaining accurate data for effective LCAs.

Recent clinical trials have successfully slowed Alzheimer's disease dementia progression, but only in early-stage patients. Society must therefore shift to early diagnosis. By framing this is as an engineering design challenge, we argue that a systems approach will identify solutions by providing the means to validate dementia medical technologies from multiple levels and perspectives: society, government, public health, healthcare, and patient ecosystems. We show that new data-enabled design methods can facilitate these different granularities of thinking and outline the need for designers.

Cyber-Physical-Systems provide extensive data gathering opportunities along the lifecycle, enabling data-driven design to improve the design process. However, its implementation faces challenges, particularly in the initial data capturing stage. To identify those, a comprehensive approach combining a systematic literature review and an industry survey was applied. Four groups of interrelated challenges were identified as most relevant to practitioners: data selection, data availability in systems, knowledge about data science processes and tools, and guiding users in targeted data capturing.

Despite its transformative impact, a systematic approach to Smart PSS development remains elusive. Addressing this, the study introduces a dynamic conceptual model named DHSmart and its accompanying canvas, adaptable to various contexts and technological advancements. Notably, it offers a structured approach to designing ‘Smart’ in Smart PSS, capturing the interplay between data, humans, and smart systems while directing digitalisation that achieves competitive advantage. It also serves as a unifying framework, enabling meaningful interdisciplinary contributions in theory and practice.

This article introduces a Human-centred Engineering Design (HcED) practice, which values human aspects. This practice engages deeply into (1) human geometry and motion for specific tasks, (2) product and manufacturing complexities through rapid prototyping, and (3) the broader human task context. This cross-disciplinary method combines ergonomics, AM, sensor applications, and multiple design practices. The framework provides concrete tasks to drive innovative designs in engineering. The study, grounded in design research case studies, led to five new Paralympic Rowing world records.

Human-centred approaches within the design cycle are crucial to enhance usability and inclusivity of products. However, the qualitative nature of traditional human factors evaluation can create bottle necks, prompting the need for more data driven methods. A framework for data-driven human factors is presented, looking to integrate mixed-method approaches. Case studies illustrate its usage in real-world scenarios and challenges are summarised, calling for robust data collection methods, balancing of mixed methods, a need for explainable systems, and interdisciplinary expertise.

This study proposes a diary method enabled with IoT sensors for user research in design. It addresses the limitations of diary methods by incorporating sensor data to trigger user self-reports. The focus is on how sensor data influences self-reports and designers' perceptions. Results show that sensor-enabled diaries offer more diverse content and overview of users’ lives and designers perceived the proposed method potentials, suggesting significant potential for IoT in user research.

Data-driven design is believed to be empowered by machine learning (ML) with advanced pattern classification and prediction. However, research on how ML can be used to support automotive human-machine interface (HMI) design is lacking. We presented a case study of truck HMI design to understand the current data use and expectations of ML in the design process. Findings show decentralized data practices, the role of expertise in decision-making, and the envisioned reactive use of ML, where we underscore the implications for advancing human-ML collaboration in designing future truck HMI systems.

Selecting right positions for composite-integrated sensors for monitoring cure during manufacturing and loads during product use presents challenges for engineering design. Since an optimal sensor placement (OSP) methodology for both phases is not emphasised enough in literature, a new methodology is proposed. This methodology is based on a Genetic Algorithm and strain gauges, temperature sensors and interdigitated electrode sensors for cure monitoring and physics-informed neural network-based load detection. Additionally, it includes sensor node positions optimization in a sensor network.

Data about customer experiences would be critical in smart product-service systems. Research is desired on how to establish a framework for Smart Experience Design based on customer experience data so that determining what kinds of customer data are needed and how these data are acquired effectively can be supported. This paper presents a framework and a method to design customer experience personalization services based on customer experience evaluation data obtained in real time and accumulated together with context data. Two illustrative cases are provided to demonstrate the validity of the framework of smart experience design based on customer experience data.

Data-driven design is expected to change design processes and organizations in significant ways. What actions should design managers take to ensure the best possible outcomes in this new data-driven design environment? This paper employs an interdisciplinary literature survey to distill key impacts that data-driven design may have on designers, design teams, organizations and product users. Findings reveal that designers may need a broader set of skills to be successful. For data-driven design to be most effective, design managers will be challenged with many integration tasks, including the integration of AI-based tools into design teams, the closer integration of interdisciplinary teams, the integration of qualitative design thinking methods with new data-driven design paradigms, and the integration of data and algorithms into traditional human-centred design practice, in an effort to overcome cognitive limitations and augment human skill. This paper identifies gaps in the literature at the intersection of data-driven design and design management, design thinking, and systems thinking.

In the midst of Industry 4.0 where digitalisation is stimulated through the Internet of Things (IoT), Big Data, and machine learning technologies, an increasing volume of valuable data has been acquired from sensors and interconnected devices. This data-driven paradigm can enable organisations to create new or improved products and services, build long-term customer relationships in a value co-creation manner, adapt to continuous business reconfiguration or address societal challenges such as sustainability. Scientific research addressing Data-driven design has increased steadily in the last few years. However, despite this, there is still a need for a comprehensive understanding of data-driven design processes. Thus, through a systematic literature review, we review the data-driven design activities observed in the new product and service development and types of data utilised in New Product Development (NPD) process. This paper contributes to design research and through reviewing the current landscape of Data-driven design identifies ten data-driven design activities and four-dimensional aspects in NPD process.

Companies offer products in different variants to reach more customers. This increases internal variety and cost. However, reducing those cost is difficult due to complexity. Complexity arises from: combinatorics; many design variables interacting with each other; coupling of technical and economical perspectives. This paper presents an approach based on (1) building a complex system model of modular models; (2) identifying the potential for standardization from a technical perspective; (3) cost-optimizing the degree of standardization. A product family of electric vehicles was optimized.

Design of software in the automotive domain often involves simulation to allow early software parametrization. Modeling complex systems or components impacted by the software in an analytical way can be time-consuming, require domain knowledge and executing the analytical models can result in high computational effort. In specific applications, these challenges can be overcome by applying machine learning based simulation. This contribution presents results of a case study in which powertrain components are modeled data-driven with artificial neural networks to support design space exploration

Maintenance is an essential aspect to keeping production facilities running and safe. However, without an overview of the maintenance impact on production, gaining clarification of the impact of maintenance is difficult. This paper introduces modularization of maintenance based on the dimensions of maintenance: physical, action, and process. The approach is applied in a case study where maintenance decisions are improved and faster than prior to the introduction of the modularized maintenance.

Digital engineering is increasingly established in the industrial routine. Especially the application of machine learning on geometry data is a growing research issue. Driven by this, the paper presents a new method for the classification of mechanical components, which utilizes the projection of points onto a spherical detector surfaces to transfer the geometries into matrices. These matrices are then classified using deep learning networks. Different types of projection are examined, as are several deep learning models. Finally, a benchmark dataset is used to demonstrate the competitiveness.