To be successful in innovation, organisations need to be dynamically adaptable to novel situations to avoid getting ‘left behind’. Yet, they face vast uncertainties stemming from unforeseeable technological shifts or future user and market behaviour, making strategic decision-making on innovation an extremely difficult task. Decision-makers thus increasingly try to control or shape the future, rather than foresee it. This includes thinking ahead and generating potential pathways that will make an innovation viable. This captures the essence of designerly ways of thinking in reasoning toward ‘what might be’. Extant literature has been reviewed that discusses alternative strategies how this future-oriented thinking can be applied to become better at selecting novel ideas for development. We observe parallels between divergent thinking, abductive reasoning, analogising and lateral thinking suggested by different authors in this process. The paper continues to propose how these key mechanisms can be embedded within an existing framework for decision-making under uncertainty, the ‘OODA Loop’, which has seen increasing uptake in such decision-making scenarios.

Free contributors have successfully shown the potential in large/complex software co-creation in the Free and Open Source Software Movement, triggering many discussions and exploration ventures from academia to industry and to the government. Though many research efforts explored whether the same level of co-creation efforts could take place broadly in the hardware realm, very few research studies focus on profit-seeking hardware projects initiated by companies. In fact, the specific nature of being tangible and profitable makes company-led open source hardware projects suspicious to be really “open” to contributors. Community has been identified as the critical driver in many open projects. By reviewing the evolution of company-community interactions over time and different community behaviors in different open development context, authors in this paper hope to identify best community- company interaction forms for open source hardware companies. Using grounded theory and case studies, we construct a framework to describe and identify company community's different behaviors and different roles.

Extended product warranties, leasing, and pay-per-use schemes are few examples of how manufacturing companies are shifting their focus from selling products to offering “solutions”, combining products and services to maximise customer value. The idea of optimising a system towards the best ‘value’ is both “intuitive and sensible”, and processes such as Value Driven Design (VDD), have been proposed to supplement traditional engineering design methodologies. Yet, value-driven initiatives struggle to communicate their benefits and to demonstrate how value models can solve actual problems. The aim of this paper is to shed light on the evolution of the VDD toolbox, mapping research clusters, applications and case studies from the perspective of how much (and how well) recent contributions have answered the five top-level questions of the VDD research agenda published in 2012. The results of the systematic literature review reveal the emergence of innovative modelling constructs, methods, and tools that help VDD in taking a leap forward in supporting organizations and teams in achieving the long-sought objective of designing for 'value'.

The ever-increasing competitiveness, due to the market globalization, has forced the industries to modify their design and production strategies. A key point is the development of products that fulfil the individual customer needs as close as possible. ETO companies manufacture new products according to the customer technical requirements given in the request for proposal.

Computational Design Synthesis is the research area focused on activities to automate the design phase in the production of products such ETO structures. In this context, Knowledge Based Engineering applications are usually applied to automate design routines and to implement a multidisciplinary product design. Knowledge should be elicited and formalized, so that it can allow the past cases retrieval and the connection between customer specifications and the product configuration tasks. This paper proposes an approach for the rapid definition of the product structure related to a ETO product, including the early cost evaluation in configurations. The research scope aims at defining a framework to support the knowledge repository, which is the Knowledge Based used to design new products and estimate their costs.

Today, information and knowledge as competitive factors influence the success of companies as much as traditional production factors like human resources or physical capital. However, the reuse of design knowledge still represents a major challenge for engineering organizations. That is, because barriers exist hindering a successful knowledge reuse. On the basis of a literature review, the research depicted in this paper analyses the relation between single information conveying design knowledge and barriers hindering a successful knowledge reuse. Developing a model-based approach, we propose a micro logic containing three steps and underlying methods enabling practitioners to identify situation-specific barriers within their organization. We illustrate the industrial application of the approach in a case study at a mining machinery OEM.

Machine learning has shown its potential to support the knowledge extraction within the development processes and particularly in the early phases where critical decisions have to be made. However, the current state of the research in the applications of the machine learning in the product development are fragmented. A holistic overall view provides the opportunity to analyze the current state of research and is the basis for the strategic planning of future research and the actions needed. Hence, implementing the systematic literature survey techniques, the state of the applications of machine learning in the early phases of the product development process namely the Requirements, functional modelling and principal concept design is reviewed and discussed.

In this article, a new approach to interactive optimization in industrial design is presented in which, for the first time, implicit preference acquisition methods are integrated. Suitable methods for preference acquisition will be selected, adapted and combined with an own PSO-inspired algorithm. The application of implicit preferences as well as the combined application of implicit and explicit preferences in an interactive optimization represents the main novelty of this contribution since this has not yet been carried out according to the current state of knowledge.Two case studies will be used to test this new approach with regard to convergence and acceptance, and a comparison will be made between the three different kind of optimization (implicit, explicit as well as a combination of both) in terms of their results.

One of the main tasks of today's data-driven design is to learn customers' concerns from the feedback data posted on the internet, to drive smarter and more profitable decisions during product development. Feature-based opinion mining was first performed by the computer and design scientists to analyse online product reviews. In order to provide more sophisticated customer feedback analyses and to understand in a deeper way customer concerns about products, the authors propose an affordance-based online review analysis framework. This framework allows understanding how and in what condition customers use their products, how user preferences change over years and how customers use the product innovatively. An empirical case study using the proposed approach is conducted with the online reviews of Kindle e-readers downloaded from amazon.com. A set of innovation leads and redesign paths are provided for the design of next-generation e-reader. This study suggests that bridging data analytics with classical models and methods in design engineering can bring success for data-driven design.

Studies of design activity have been dominantly reporting on different aspects of the design process, rather than the content of designing. The aim of the presented research has been the development and application of an approach for a fine-grain analysis of the design content communicated between designers during the team conceptual design activities. The proposed approach builds on an engineering design ontology as a foundation for the content categorisation. Two teams have been studied using the protocol analysis method. The coded protocols offered fine-grain descriptions of the content communicated at different points in the design session and enabled comparison of teams’ approaches and deriving some generalisable findings. For example, it has been shown that both teams focused primarily on the use of the developed product and the operands within the technical process, in order to generate new technical solutions and initial component design. Moreover, teams exhibit progress from abstract to concrete solutions as the sessions proceeded and focused on the functional requirements towards the end of the sessions.

In order to meet the quality standards required in today's product development process, the designer must be able to draw on the knowledge contained in standards at all times. However, in today's digital work environment, these are usually only available in paper or PDF form. To support the designer during the product development process, a research project examine how knowledge from standards can be made available digitally and integrated into his working environment. This paper presents a concept with a RESTful service as a central knowledge base, which provides knowledge in the form of microservices. The implementation is carried out using welding assemblies as an example. To achieve the high-quality requirements and to implement them, the standard contents had to be prepared in a machine-interpretable and cross system way.

The importance of affordance in Engineering design is well established. Artifacts that are able to activate spontaneous and immediate users’ reactions are considered the outcome of good design practice.

A huge effort has been made by researchers for understanding affordances: yet these efforts have been somewhat elusive. In particular, they have been limited to case studies and experimental studies, usually involving a small subset of affordances. No systematic effort has been carried out to list all known affordance effects. This paper offers preliminary steps for such an ambitious effort.

We propose a set of three different approaches of Natural Language Processing techniques to be used to extract meaningful affordance information from the full text of patents: 1) a simple word search, 2) a lexicon of affordances and 3) a rule-based system.

The results give in-depth measures of how rare affordances in patents are, and a fine grain analysis of the linguistical construction of affordances. Finally, we show an interesting output of our method, that has detected affordances for disabled people, showing the ability of our system to automatically collect design-relevant knowledge.

Knowledge is a crucial factor in state-of-the-art product development. It is often provided by stakeholders from divers disciplinary and individual backgrounds and has to be integrated to create competitive products. Still, it is not fully understood, how knowledge is generated, transformed, transferred and integrated in complex product development processes. To investigate the dynamic interrelations between involved stakeholders, applied knowledge types and related artefacts, researchers at the TU Berlin conducted and evaluated a student experiment to study basic phenomena of development projects. In relation to research methods and instruments applied in this experiment, various improvement opportunities were identified. In this paper, the experimental setting and its results are critically analysed from a social science perspective in order to generate improved research design. Based on the results of this analysis, a first set of methods and instruments from social sciences are identified that can be applied in further experiments. The goal is to develop a methodological toolbox that can be used to approach research on knowledge dynamics in product development.

There are three product design contexts that may significantly affect the design of a product and customer preferences towards product attributes, i.e. customer context, market context, and usage context factors. The conventional methods to gather product usage contexts may be costly and time consuming to conduct. As an alternative, this paper aims to automatically identify product usage contexts from publicly available online customer reviews. The proposed methodology consists of Preprocessing, Word Embedding, and Usage Context Clustering stages. The methodology is applied to identify usage contexts from laptop customer reviews, which results in 16 clusters of usage contexts. Furthermore, analyzing the review sentences explains the separation of “playing games” –which is more related to casual gaming, and “gaming rig” –which implies high computing power requirements. Finally, comparing customer review with manufacturer's product description may reveal a discrepancy to be investigated further by product designer, e.g. a customer suggests a laptop for basic use, although the manufacturer's description describes it for heavy use.

A key question regarding business model innovation/development for circular economy is “how to make it happen in practice”? By systematically reviewing 92 approaches from circular economy and sustainability literature and practice, this research identifies requirements and proposes a holistic and systemic process for business model innovation for circular economy. This conceptual process model was consolidated based on the integration of the unique elements of sixteen existing process models. It comprises three-stages (sense, seize, transform) based on a dynamic capabilities view, and envisions 33 activities, 21 deliverables, 88 techniques/tools and 13 enablers or catalyzers for change. Besides enabling the view of processes and procedures with behavior and learning skills required to inspire circular economy thinking in business model innovation, it highlights the importance of 'formalized' decision-making procedures and includes activities to integrate sustainability thinking and to support the identification of required changes in product innovation/development.

Vast amounts of information and knowledge is produced and stored within product design projects. Especially for reuse and adaptation there exists no suitable method for product designers to handle this information overload. Due to this, the selection of relevant information in a specific development situation is time-consuming and inefficient. To tackle this issue, the novel approach Intentional Forgetting (IF) is applied for product design, which aims to support reuse and adaptation by reducing the vast amount of information to the relevant. Within this contribution an IF-operator called Cascading Forgetting is introduced and evaluated, which was implemented for forgetting related information elements in ontology knowledge bases. For the evaluation the development process of a test-rig for studying friction and wear behaviour of the cam/tappet contact in combustion engines is analysed. Due to the interdisciplinary task of the evaluation and the characteristics of semantic model, challenges are discussed. In conclusion, the focus of the evaluation is to consider how reliable the Cascading Forgetting works and how intuitive ontology-based representations appear to engineers.

For a company it is necessary to know, which products can be configured using carry-over-parts or the same technology. This can become quite relevant in the context of automobile electrification, where complex mechatronic systems are used. Consisting of various mechanical components, these systems perform the required function while being actuated by electronically controlled motors. To solve this, a novel mechanism for data driven portfolio analysis based on product platforms using knowledge-based systems is proposed in this paper. Further, the mechanism is tested by applying it to an electrical motors' case study. Using this method, all possible combinations of a product platform are calculated and finally displayed in different product portfolios. Additionally, all the non-feasible motor designs are removed from the solutions portfolio using the acquired knowledge base and performing design checks. The latter are employed for penalising and eliminating from the pareto-front of the designs, which violate the thermal, mechanical and acoustic constraints. The generated product portfolio can be used further to expand the systems engineering collaboration and support decision-making.

In the last few years there has been a noticeable change in the development of headlamp systems in the field of vehicle lighting technology. Starting with adaptive front-lighting systems via Matrix LED systems, high-resolution headlamps will provide more safety in road traffic in the near future.

For the implementation of high-resolution headlamps various spatial light modulators and light generating technologies can be applied. The emitted light of the light source is directed via an illumination optics onto the modulator and a projection optics is applied to image the spatial light modulator into the traffic area. The formerly mechatronic systems are thus increasingly become opto- mechatronic systems. Therefore, the optic design must be taken into account in the early development phase of these systems.

In this paper we present a methodical approach to describe the optic design for optomechatronic systems. This approach can be used to develop efficient and high-intensity optomechatronic systems using various spatial light modulators and light generating technologies. Conclusively we demonstrate an exemplary application of the methodology on a high-resolution projection module.

ABSTRACT

While extensive modelling - both physical and virtual - is imperative to develop right-first-time products, the parallel use of virtual and physical models gives rise to two interrelated issues: the lack of revision control for physical prototypes; and the need for designers to manually inspect, measure, and interpret modifications to either virtual or physical models, for subsequent update of the other. The Digital Twin paradigm addresses similar problems later in the product life-cycle, and while these digital twins, or the “twinning” process, have shown significant value, there is little work to date on their implementation in the earlier design stages. With large prospective benefits in increased product understanding, performance, and reduced design cycle time and cost, this paper explores the concept of using the Digital Twin in early design, including an introduction to digital twinning, examination of opportunities for and challenges of their implementation, a presentation of the structure of Early Stage Twins, and evaluation via two implementation cases.

Current trends in product development are digital engineering, the increasing use of assistance tools based on artificial intelligence and in general shorter product lifecycles. These trends and new tools strongly rely on available data and will irreversibly change established product development processes. One example for such a new data driven tool is the plausibility check of linear finite element simulations with Convolutional Neural Networks (CNN). This tool is capable of determining whether new simulation results are plausible or non-plausible according to numeric input data. The digitalization and the increased use of data driven tools employing algorithms known from Artificial Intelligence also shifts the roles of many involved engineers. This paper describes and highlights this transition from current product development processes to a data driven / simulation driven product development process. Particularly, the shifts and changes of different roles and domains are illustrated and an example for changing roles in the design and simulation department is described. Furthermore, required adjustments in the design process are derived and compared to the current status.

Deep learning methods have been applied to randomly generate images, such as in fashion, furniture design. To date, consideration of human aspects which play a vital role in a design process has not been given significant attention in deep learning approaches. In this paper, results are reported from a human- in-the-loop design method where brain EEG signals are used to capture preferable design features. In the framework developed, an encoder extracting EEG features from raw signals recorded from subjects when viewing images from ImageNet are learned. Secondly, a GAN model is trained conditioned on the encoded EEG features to generate design images. Thirdly, the trained model is used to generate design images from a person's EEG measured brain activity in the cognitive process of thinking about a design. To verify the proposed method, a case study is presented following the proposed approach. The results indicate that the method can generate preferred designs styles guided by the preference related brain signals. In addition, this method could also help improve communication between designers and clients where clients might not be able to express design requests clearly.