This study evaluates an augmented reality (AR) prototype aimed at enhancing user guidance in industrial procedures, focusing on tool change tasks. The variability in experience and skills among operators and maintenance technicians in handling machine-related requirements poses challenges to smooth operations. To address this, an AR guidance system was developed for a head-mounted display device, utilizing image, object, and gesture recognition to minimize user interaction and enhance system adaptiveness, ultimately reducing cognitive load. A user study employing video observations and questionnaires was conducted to evaluate the AR system’s impact on usability and cognitive load. Results indicate that the prototype effectively facilitated tool change tasks, providing a user-friendly experience with reduced cognitive load. The integration of image, object, and gesture recognition contributed to streamlined user guidance, minimizing the need for constant user interventions. Notably, participants experienced a fluid user experience with high usability and a moderate cognitive load, emphasizing the system’s potential in managing complex tasks. The study also highlighted the applicability of AR technology in fields beyond tool changes, such as troubleshooting, and identified a preference for an apprenticeship-style training approach among participants.

Time is central to many debates about hybrid work - the impact on speed and productivity, commute times, and synchronisation of work. However, we argue that time is often over-simplified in extant hybrid work literature and tends to ignore many temporal concepts that capture the inherently complex, multifaceted, subtle, and socially embedded nature of time.

To address this issue, we conducted studies of five hybrid work teams across two large organisations. The paper contributes to current research on hybrid work by (i) illustrating the various temporal concepts that may be considered in the design of hybrid work environments, as well as the impact of considering or not considering them; (ii) illustrating how these temporal concepts in a physical space can be augmented in the digital space, rather than assuming the digital space should simply represent the physical; and (iii) providing a framework for the consideration of time–aware hybrid work. We hope to spark scientific interest in studying the temporal nuances of hybrid work but also the temporal aspects of design in other areas.

As industrial digitalization progresses and the use and application of digital technology in industrial production increases, it is tempting to view technology as the answer and solution to all the challenges that arise in production. However, does relying solely on the power of digital technological systems do justice to the complexity of today’s shop floor? We argue that despite the fact that digital technologies have the capacity to process a considerable amount of data which outperforms human computing abilities, it is crucial to apply a more holistic view and widen the scope of analysis above and beyond the immediate application of digital technologies. Thus, this study aims to explore industrial shop floor practices to increase understanding of the planning and coordination patterns. To get a thorough understanding of how planning and coordination work today and to find new opportunities, a case study approach was adopted. Data was collected in one small and medium-sized manufacturing enterprise through shop floor observations and interviews with six employees during 2022-2023. Further, we applied a coordination framework, including coordination mechanisms, for data analysis. The results show that the three coordination mechanisms Objects and Representations, Roles, and Routines are abundantly present in the case study. Moreover, two additional coordinating mechanisms were identified, Digital Technology and Context, which include a contribution to the earlier coordination framework. We argue to put the challenges that operators face into the limelight, by involving them in the planning and coordination loop. 

Quality is one of the most important contributors to products’ success in the market and essential input for design and manufacturing. Historically, quality definitions evolved over time but with significant domain-specific differences. One example of these emerging differences is the human-centric, subjective approach to quality. Current Quality 4.0 models, in most cases, are derivatives from the Total Quality Management (TQM) way, solely based on hopes for Data-Driven approaches to solving problems, with the lack of a human-centric operator approach. Industry 4.0 and its associated digital technologies promise to change this notion and make formerly subjective quality dimensions measurable on a scale as input for design and manufacturing. This leads to an opportunity to bridge the current gap and streamline the Quality and Operator in a holistic, data-informed, and digital technology-enabled way. This paper introduces a Quality 4.0 transformation as a vision for the future of Human – Machine symbiosis in the context of Operator 5.0 for intelligent manufacturing systems. We discuss what needs to be added to Quality 4.0 to achieve the requirements set for Operator 5.0 This work suggests how to enrich smart manufacturing systems from a human-centric perspective with Operator 5.0 making own, informed decisions based on data, experience, and tacit knowledge.

Industry 4.0 is believed to introduce new smart digital tools which transform manufacturing processes but affect production personnel’s work practice. Operators and maintenance personnel running the everyday operations need to learn and handle new routines and systems while maintaining production efficiency. Operators today are challenged when they must handle unexpected stops caused by machine failures and the following error recovery process of automated production systems. With complex digital tools and integrated production systems the error recovery process becomes complex because there is no one-size-fits-all solution and a lack of intelligent and automated restart systems. Even if there are defined routines for industrial work and structures for managing digital technologies, it is not adapted to the individuals’ cognitive processes neither to their workplace learning. Altogether it puts high pressure on operators’ knowledge and skills of restarting machines and systems caused by errors. The aim is to explore operators and maintenance personnel cognition and skills and how their roles vary in relevant aspects of situational awareness and workplace learning. In an on-going case we studied two industrial companies that produce similar components but are working differently with production and maintenance. Through nine interviews we investigate the differences between the companies, their current work practices, and future changes. With application of a situation awarenessmodel, we capture cognition and learning including task/system factors, perception and decision making, and individual factors. Perspectives of workplace learning and knowledge sharing between personnel and relations to the systems use are applied. Results indicate that rule-based behaviours are key for both operators and maintenance personnel. These behaviours are supported by the systems and routines, but complicated errors make the systems and routines prove inadequate. In conclusion, to design appropriate digital support tools both operators and maintenance personnel behaviour need to be supported, however they need to be supported differently since their function behaviours such as routines, system use and communication vary. In addition, future skills and competences needed forsupporting complex system tasks include knowledge of computational models and simulation, knowledge of the machines and how they interrelate with systems, and logic reasoning and robotic programming of automated production systems.

In the era of Industry 4.0, emergent digital technologies generate profound transformations in the industry toward developing intelligent manufacturing. The technologies included in Industry 4.0 are expected to bring new perspectives to the industry on how manufacturing can integrate new solutions to get maximum output with minimum resource utilization (Kamble et al., 2018). Industry 4.0 technologies create a great impact on production systems and processes, however, affect organizational structures and working life conditions by disrupting employees’ everyday practices and knowledge, in which competence and learning, human interaction, and organizational structures are key. Hence, new digital solutions need to be integrated with work and learning to generate more holistic and sustainable businesses (Carlsson et al., 2021).

The core Industry 4.0 technologies are built on cyber-physical systems (CPS), cloud computing, and the Internet of things (IoT) (Kagermann et al., 2013; Zhou et al., 2018). In recent years, an array of additional technologies has been developed further, such as artificial intelligence (AI), big data analytics, augmented and virtual reality (AR/VR), cyber security, robotics, and automation. Industry 4.0 aims to create a potential for faster delivery times, more efficient and automated processes, higher quality, and customized products (Zheng et al., 2021). Hence, the ongoing transformation through the technological shift of production in combination with market demands pushes the industry and its production process.

Recent research has substantially contributed to an increased understanding of the technological aspects of Industry 4.0. However, the utilization of technologies is only a part of the complex puzzle making up Industry 4.0 (Kagermann et al., 2013; Zheng et al., 2021). The impact Industry 4.0 technologies and application s have on the industrial context also changes and disrupts existing and traditional work practices (Taylor et al., 2020), management and leadership (Saucedo-Martínez et al., 2018), learning and skills (Tvenge & Martinsen, 2018), and education (Das et al., 2020). This research has shown a growing interest in human-centric aspects of Industry 4.0 (Nahavandi, 2019), i.e., the transformative effects Industry 4.0 has on humans, workplace design, organizational routines, skills, learning, etc. However, these aspects are scarcely considered in-depth. Given this, and from a holistic point of view, there is a need to understand intelligent manufacturing practice from a human-centric perspective, where issues of work practices and learning are integrated, herein refe rred to as industrial work-integrated learning. I-WIL is a research area that particularly pays attention to knowledge production and learning capabilities related to use and development when technology and humans co -exist in industrial work settings (Shahlaei & Lundh Snis, 2022). Even if Industry 4.0 still is relevant for continuous development, a complementary Industry 5.0 has arisen to provide efficiency and productivity as the sole goals to reinforce a sustainable, human-centric, and resilient manufacturing industry (Breque et al., 2021; Nahavandi, 2019).

Given this situation, the research question addressed here is: How does state-of-the-art research of Industry 4.0 technologies and applications consider human-centric aspects? A systematic literature review was conducted aiming to identify a future research agenda that emphasizes human-centric aspects of intelligent manufacturing, that will contribute to the field of manufacturing research and practices. This question was based on very few systematic literature reviews, considering Industry 4.0 research incorporating human -centric aspects for developing intelligent manufacturing (Kamble et al., 2018; Zheng et al., 2021). The literature review study was structured by the design of Xiao and Watson’s (2019) methodology consisting of the steps 1) Initial corpus creation, 2) Finalizing corpus, and 3) Analyzing corpus, and we also used a bibliometric approach throughout the search process (Glänzel & Schoepflin, 1999). The keyword selection was categorized into three groups of search terms, “industry 4.0”, “manufacturing”, and “artificial intelligence”, see figure 1. (Not included here)

Articles were collected from the meta -databases EBSCOhost, Scopus, Eric, and the database AIS, to quantify the presence of human-centric or human-involved AI approaches in recent manufacturing research. A total of 999 scientific articles were collected and clustered based on a list of application areas to investigate if there is a difference between various areas in which artificial intelligence is used. The application areas are decision -making, digital twin, flexible automation, platformization, predictive maintenance, predictive quality, process optimization, production planning, and quality assessment.Throughout the review process, only articles that included both AI and human -centric aspects were screened and categorized. The final corpus included 386 articles of which only 93 articles were identified as human -centric. These articles were categorized into three themes: 1) organizational change, 2) competence and learning, and 3) human-automation interaction. Theme 1 articles related mostly to the application areas of flexible automation (11), production planning (9), and predictive maintenance (5). Theme 2 concerned the application areas of production planning and quality assessment (7), and process optimization (7).

Finally, theme 3 mainly focused on flexible automation (10), digital twin (3), and platformization (3). The rest of the corpus only consisted of one or two articles in related application areas. To conclude, only a few articles were found that reinforce human -centric themes for Industry 4.0 implementations. The literature review identified obstacles and opportu nities that affect manufacturing organizations to reap the benefits of Industry 4.0. Hence, I-WIL is proposed as a research area to inform a new research agenda that captures human and technological integration of Industry 4.0 and to further illuminate human-centric aspects and themes for future sustainable intelligent manufacturing. 

The manufacturing industry is constantly facing hard times employing key expertise to deploy a digital transformation of Industry 4.0 enabling technologies and applications (Lasi et al., 2014). Challenges of increased industrial digitalization pressure the industry to expand professionals’ future knowledge to be capable of new production systems, virtual manufacturing, and digital services. This requires new types of knowledge applicable to transformative work practices and for future adaptation (Ford, 2015). Becoming a competent expert for an entire working life tends to be harder for today’s professionals because of digital disruptions (Belski et al., 2016; Susskind & Susskind, 2015), and therefore they continuously need to seek new knowledge and learning as integrated part of work combined with new theoretical knowledge through academic studies in higher education. Competence development through university-industry collaboration (UIC), is a vehicle for asserting high-technological change and innovative capacities (Ankrah & Tabba, 2015; Sjöö & Hellström, 2019). Specifically, university tailormade courses that enhance the co-construction of knowledge and develop professionals’ learning and skills to augment the industrial business demands in the context of Industry 4.0 (Abelha et al., 2020). However, there seems to be a dual situation with the industry effectiveness pressure on the one hand, and the blended competence development opportunities offered by the university, on the other. This situation raises challenges with the different stakeholders’ perspectives of inter-organizational collaboration that presumes productive development. Colliding interests and conflicts on different systemic levels may occur, and inter-organizational collaborations may not per se cause benefits and learning, but rather needs to be analyzed through their inner contradictions a s power for change. Contradictions, however, are systemic, embedded in history, developing over time, and cannot be studied directly. Hence, in this study contradictions on various levels are explored, as a power for change (Engeström & Sannino, 2010;2011). Included contradictions are those manifested by the professionals in UIC and intra-organizational situations within the industry organizations. Also, contradictions concerning the professionals’ own motivation for learning related to the industry competence demands and the management’s lack of, or support for such learning initiatives. Hence, the analytical lens is the industry professionals’ knowledge construction that emerges in a work-integrated e-learning (e-WIL) practice of UIC to sustain resilient interorganizational collaboration through UIC (Hattinger & Eriksson, 2020). The study is conducted within a competence development program of short academic courses targeting industry professionals’ knowledge needs, to strengthen individual learning and co-construction of knowledge with the prospect of generating organizational transformations, i.e., the professionals’ knowledge generation in a trajectory growing from participants’ discursive manifestation of contradictions, actionable solutions towards tra nsformations. Such knowledge generation tends to be temporary, riddled with problems and conflicts, therefore, it is argued to identify systemic contradictions to be used as energizing forces and triggers for development and change (Engeström & Sannino, 20 10; 2011). With the perspectives of professionals’ motives, interactions, and experiences, the aim is to grasp inner tensions between different perspectives to reach the kernel of potential future expansive transformations, i.e., the professionals’ views concerning the industry objectives, the learning trajectory of engineering subjects, and the e -learning course design. 

Given this, the first research question asked is: What kinds of discursive manifestations of contradictions do industry professionals experience as inner learning motives of competence development for organizational purposes?To further the understanding of how professionals assert knowledge to generate actionable solutions, the second question asked is: How are these manifestations negotiated, and turned into actionable solutions and potentially expansive transformations? 

MethodThe questions investigate the professionals’ experiences of their course participation and knowledge construction on a micro-level using the cultural-historical activity theory, CHAT, (Engeström & Sannino, 2010). Engeström and Sannino (2011) have developed a methodological framework that analyzes contradictions through their discursive manifestations. Contradictions do not speak for themselves, rather they become recognized through people’s articulation of tensions and dilemmas. It is through their discursive manifestations during conversations and actions that they are made visible (Engeström & Sannino, 2011).Data from a longitudinal e-WIL project was collected for three years and consisted of twelve focus group sessions comprising a total of 119 professionals in 15 industry companies. The professionals participated in e -learning courses designed with pedagogy that stimulate work-integrated learning covering knowledge needs such as industrial automation and machine security in robotics, negotiation skills, and machining. A comprehensive analysis of the discursive manifestation of contradictions implies a transition into actionable (possible) solutions toward expansive transformations. The three steps analysis became a powerful approach for grasping learning insights between professionals and understanding the effects of e-learning design and co-construction of knowledge on a systemic level. 

Results and contributionDiscursive manifestations of contractions occurred on different levels, concerning the dual situation of the industry effectiveness pressure on the one hand, and the e-WIL course opportunity offered by the university, on the other. The most critical conflict was the lack of industry companies’ maturity to support professionals’ time and performance related to individual professional career paths that will trigger future expansive transformations. However, the professionals described actionable solutions to many of the defined dilemmas, concerning practical e-learning design problems, which were easy to overcome. With the concepts of manifestations of contradictions, actionable solutions, and expansive transformations, we have increased our understanding of knowledge and problem-solving processes emerging in UIC networks with many different stakeholders. The article contributes to a developed approach for analyzing discursive manifestations of contradictions toward expansive transformations in workplace practices. It also contributes to empirical findings of inter-organizational collaborations through an innovative work-integrated e-learning context. 

The manufacturing industry is currently under extreme pressure to transform their organizations and competencies to reap the benefits of industry 4.0. The main driver for industry 4.0 is digitalization with disruptive technologies such as artificial intelligence, machine learning, internet of things, digital platforms, etc. Industrial applications and research studies have shown promising results, but they rarely involve a human-centric perspective. Given this, we argue there is a lack of knowledge on how disruptive technologies take part in human decision-making and learning practices, and to what extent disruptive technologies may support both employees and organizations to “learn”. In recent research the importance and need of including a human-centric perspective in industry 4.0 is raised including a human learning and decision-making approach. Hence, disruptive technologies, by themselves, no longer consider to solve the actual problems.

Considering the richness of this topic, we propose an industrial work-integrated-learning research agenda to illuminate a human-centric perspective in Industry 4.0. This work-in-progress literature review aims to provide a research agenda on what and how application areas are covered in earlier research. Furthermore, the review identifies obstacles and opportunities that may affect manufacturing to reap the benefits of Industry 4.0. As part of the research, several inter-disciplinary areas are identified, in which industrial work-integrated-learning should be considered to enhance the design, implementation, and use of Industry 4.0 technologies. In conclusion, this study proposes a research agenda aimed at furthering research on how industrial digitalization can approach human and artificial intelligence through industrial work-integrated-learning for a future digitalized manufacturing.

This study focuses on managers’ perceptions of organisational capabilities for strategy formulation related to industrial digitalisation. A qualitative case study based on ten interviews in two manufacturing companies explores managers’ perceptions of industrial digitalisation. A dynamic capability framework, consisting of the organisational capabilities sensing, seizing, and transforming opportunities, is applied to recognise and analyse nuances in managers’ interpretation of prevailing organisational capabilities. Findings reveal that the studied companies have a limited maturity concerning knowledge, skills, and resources for industrial digitalisation which is needed in order to formulate a digital strategy. An additional core capability was discerned, i.e., ’seeking’. Seeking includes actions for articulating, appropriating, and involving in the very early phases of understanding and formulating a digital strategy. This article contributes to the existing dynamic capability framework by adding the core capability seeking illustrated in an elaborated and holistic ’dynamic capability loop’. The loop frames industrial digitalisation as a continuous process closely integrated with strategy formulation.