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 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 addresses work-integrated learning from a workplace learning perspective.Two companies within the manufacturing industry (turbo machinery and aerospace) together with a multi-disciplinary research group explore the opportunities and challenges related to applications of artificial intelligence and human intelligence and how such applications can integrate and support learning at the workplace.The manufacturing industry is currently under extreme pressure to transform their organizations and competencies to reap the benefits of industry 4.0. The main driverf or industry 4.0 is digitalization with disruptive technologies such as artificial intelligence, internet of things, machine learning, cyber-physical systems, digital platforms, etc. Many significant studies have highlighted the importance of human competence and learning in connection to industry 4.0 in general and disruptive technologies and its transformative consequences in particular. What impact have such technologies on employees and their workplace?

There is a lack of knowledge on how artificial intelligent systems actually take part in practices of human decision making and learning and to what extent disruptive technology may support both employees and organizations to “learn”. The design  and use of three real-world cases of artificial intelligence applications (as instances of industry 4.0 initiatives) will form the basis of how to support human decision making and scale up for strategic action and learning. Following a work-integratedapproach the overall research question has been formulated together with the two industry partners: How can artificial and human intelligence and learning, interact tobring manufacturing companies into Industry 4.0? An action-oriented research approach with in-depth qualitative and quantitative methods will be used in order to make sense and learn about new applications and data set related to a digitalized production.The contribution of this study will be three lessons learned along with a generic model for learning and organizing in the context of industry 4.0 initiatives. Tentative findings concern how artificial and human intelligence can be smartly integrated into the human work organization, i.e. the workplace. Many iterations of integrating the two intelligences are required. We will discuss a preliminary process-model called “Super8”, in which AI systems must allow for providing feedback on progress as well as being able to incorporate high-level human input in the learning process. The   practical implication of the study will be industrialized in the collaborating