The introduction of new digital technologies in industrial work systems and increasing implementation of Cyber Physical Systems are evoking new and unknown challenges and opportunities related to aspects of human work and organisation. To ensure human wellbeing and overall system productivity, there is a need for interdisciplinary methods and approaches for dealing with the challenges and taking advantage of the opportunities. In this paper, we present a conceptual framework for designing Industry 4.0 enabled work systems, which serves to accommodate this need. The framework combines elements and principles of Design- and Lean thinking methodologies and Human Factors and Ergonomics, thus making it a practical, systematic, and iterative, human centred approach. We use examples from a retrospective industrial case study to illustrate elements of the framework and provide several implications for practitioners.

Robotic systems need to achieve a certain level of process safety during the performance of the task and at the same time ensure compliance with safety criteria for the expected behaviour. To achieve this, the system must be aware of the risks related to the performance of the task in order to be able to take these into account accordingly. Once the safety aspects have been learned from the system, the task performance must no longer influence them. To achieve this, we present a concept for the design of a neural network that combines these characteristics. This enables the learning of safe behaviour and the fixation of it. The subsequent training of the task execution no longer influences safety and achieves targeted results in comparison to a conventional neural network.

The dawn of the fourth industrial revolution, mostly known through the German initiative “Industrie 4.0”, builds on a set of technologies emerging from software and information and communication technologies (ICT); paired with the growth of the Internet-of-Things (IoT), the so-called “smart products” are expanding on the market. These smart products integrate data collection and processing capacities. Additionally, the collected data have their own lifecycle, and can be classified as sensitive data. In that sense, companies developing hardware products may need support to step in “smart products” development. Digital transformation strategy is a possible overall support for companies. However, regarding smart product development and IoT data management, no studies to date have addressed formalized guidelines to support companies. This article proposes a set of guidelines focusing on IoT data management to support hardware companies in their transformation towards smart products. The proposed guidelines are exemplified on a fitness industry case which is using wearable devices collecting body metrics, considered as sensitive data.

Traditionally, data has been presented in textual format and the interaction with the user confined to the keyboard or touch screen to input data and the screen to deliver information. However, with the advent of a data-driven society, an opportunity for more natural and efficient ways of presenting data and interacting with it has emerged.

Although the area of Human-Computer Interaction has existed for a long time, its focus has always been on the interaction with an artefact (the computer). Today instead, we face the challenge of interacting with an intangible object: data. As a result, a key requirement emerges: How do we make legible the enormous amounts of data produced per day to ordinary people?

Designers, able to devise natural and smooth interaction experiences, should play a relevant role in this new scenario. However, they might lack the basic technical knowledge required to understand the possibilities of these new systems.

In this paper we present a brief how-to manual for an undergraduate course on data materialisation: the process of transforming an intangible object (data) in an artefact that can be interacted with in a physical way.

The status of Smart Home Appliances (SHAs) in the smart home industry has been raised as major components of a smart home. To design a good SHA, diverse stakeholders in the smart home ecosystem need to have a common understanding of smart homes and SHAs. However, the studies on what they are, especially those reflecting practitioners’ point of view, have been scarce.

In this research, we conducted systematic literature reviews and practitioner interviews using the concept mapping method to identify the attributes of SHAs depending both on academia and industry. In addition, we suggest a framework of the smart home ecosystem which describes how those attributes are revealed in the relationships among SHAs, users, and external stakeholders.

Since all of the interviewees are Korean SHA development professionals, the findings from this research could have been limited to the current status of the Korean SHA industry. Nonetheless, we expect that this research would contribute to building a consensus on what smart homes and SHAs are and provide the designers and developers of SHAs with systematic perspectives on smart homes.

Designing products with a focus on self-explanatory assembly can reduce the use of procedural instructions and the associated problems. This paper describes how different groups of students, in two different design-engineering courses designed or redesigned products in an attempt to make the assembly of the product self-explanatory. The design outcomes are discussed in relation to the design context and linked to existing theory on design for meaning.

One of the main problems in the implementation of Industry 4.0 is the assessment of the added technical and economic value of the new digital and other technological opportunities. This can already be observed during the elaboration of solution alternatives during the development of new industry 4.0 work systems. In order to support the developers of these systems in their decision-making, this contribution introduces a quantitative evaluation approach based on the combination of an extension of the CPM/PDD approach with an industry 4.0 maturity model and an identification scheme for process losses.

As Industry 4.0 will greatly challenge employee mental workload (MWL), research on objective wearable MWL-monitoring is in high demand. However, numerous research lines validating such technology might become redundant when employees eventually object to its implementation. In a pilot study, we manipulated two ways in which employees might perceive MWL-monitoring initiatives. We found that framing the technology in terms of serving intrinsic goals (e.g., improving health) together with an autonomy-supportive context (e.g., allowing discussion) yields higher user acceptability when compared to framing in terms of extrinsic goals (e.g., increasing productivity) together with a controlling context (e.g., mandating use). User acceptability still panned out neutral in case of the former, however - feeding into our own and suggested future work.

Industry 4.0 is a great opportunity and a great challenge for enterprises. Nowadays, how to adjust the strategy according to the new situation to deal with the opportunities and challenges brought by Industry 4.0 is a hot topic. The paper investigates what is Industry 4.0 and compares the different fourth industry revolution programs in various countries and takes advantage of bibliometric to investigate which technologies play an important role in Industry 4.0. What is more, the status quo, challenges and development directions faced in various fields in the industrial 4.0 era are elaborated. The paper not only combs the research direction for researchers, but also presents the status quo of practical application for enterprises. The paper contributes to showing a leading role for theory and practice of industry 4.0.

The engineering design community needs to development tools and methods now to support emerging technological and societal trends. While many forecasts exist for technological and societal changes, this paper reports on the findings of a workshop, which addressed trends in engineering design to 2040. The paper summarises the key findings from the six themes of the workshop: societal trends, ways of working, lifelong learning, technology, modelling and simulation and digitisation; and points to the challenge of understanding how these trends affect each other

With the rapid success of the digital enterprises in the 21st Century, industrial manufacturing is expected to be approaching the fourth industrial revolution, coined Industry 4.0 (I4.0). The instrumental technology that will drive this evolution is the integration of the physical and digital factory into one cyber physical system. There is consensus among academics and industry alike that there will be an integral paradigm shift in how offerings will be developed and manufactured. While there is much confidence that the future factory will have unprecedented capabilities to satisfy complex customer demands, there is little agreement on how individual organisations can utilise these trends. This paper presents a literature review identifying reoccurring themes and trends of I4.0 and their expected effect on future manufacturing. Central characteristics, challenges and opportunities are identified and discussed. The findings can provide support in developing actionable strategies for industry to direct I4.0 endeavours.