Industry 4.0 is based on the digitization of manufacturing industries and has raised the prospect for substantial improvements in productivity, quality, and customer satisfaction. This digital transformation not only affects the way products are manufactured but also creates new opportunities for the design of products, processes, services, and systems. Unlike traditional design practices based on system-centric concepts, design for these new opportunities requires a holistic view of the human (stakeholder), artefact (product), and process (realization) dimensions of the design problem. In this paper we envision a “human-cyber-physical view of the systems realization ecosystem,” termed “Design Engineering 4.0 (DE4.0),” to reconceptualize how cyber and physical technologies can be seamlessly integrated to identify and fulfil customer needs and garner the benefits of Industry 4.0. In this paper, we review the evolution of Engineering Design in response to advances in several strategic areas including smart and connected products, end-to-end digital integration, customization and personalization, data-driven design, digital twins and intelligent design automation, extended supply chains and agile collaboration networks, open innovation, co-creation and crowdsourcing, product servitization and anything-as-a-service, and platformization for the sharing economy. We postulate that DE 4.0 will account for drivers such as Internet of Things, Internet of People, Internet of Services, and Internet of Commerce to deliver on the promise of Industry 4.0 effectively and efficiently. Further, we identify key issues to be addressed in DE 4.0 and engage the design research community on the challenges that the future holds.

Different from explicit customer needs that can be identified directly by analyzing raw data from the customers, latent customer needs are often implied in the semantics of use cases underlying customer needs information. Due to difficulties in understanding semantic implications associated with use cases, typical text mining-based methods can hardly identify latent customer needs, as opposite to keywords mining for explicit customer needs. This paper proposes a two-layer model for latent customer needs elicitation through use case reasoning. The first layer emphasizes sentiment analysis, aiming to identify explicit customer needs based on the product attributes and ordinary use cases extracted from online product reviews. Fuzzy support vector machines (SVMs) are developed to build sentiment prediction models based on a list of affective lexicons. The second layer is geared toward use case analogical reasoning, to identify implicit characteristics of latent customer needs by reasoning the semantic similarities and differences analogically between the ordinary and extraordinary use cases. Case-based reasoning (CBR) is utilized to perform case retrieval and case adaptation. A case study of Kindle Fire HD 7 in. tablet is developed to illustrate the potential and feasibility of the proposed method.

The importance of augmenting human-technology collaborative cognition has been envisioned as one of the fundamental ways to bolster human cognition through human-automation interaction in complex manufacturing and operational environments. The focus on collaborative cognition entails a human-automation mutual adaption strategy for augmenting team cognition and collective intelligence. This paper provides an overview of augmenting collaborative cognition from an analytic and model-based decision-making perspective. Aiming to advance basic research for understanding human cognition augmentation, the fundamental and applied aspects of creating mathematical and computational models are discussed in regard to cognitive state sensing and assessment, human-automation interaction adaption and control, as well as group decision making in human-automation systems. A research roadmap towards cyber-physical-human analysis is deliberated to reveal a variety of opportunities of developing novel methods for enhancing affective cognition and perception learning, trust dynamics modelling, human cognitive performance prediction, as well as human-automation interaction optimisation.

The subject matter of design has traditionally been focused on design of engineered (e.g., physical/mechanical) products. A multitude of profound changes and emerging trends in the design field hav...