The impact of Industry 4.0 on work design

The future of work in the face of today’s unprecedented technological progress is a frequently debated, yet still ambiguous topic. Referred to as ‘Industry 4.0’, the implementation of advanced digital technologies in manufacturing enables companies to better balance the traditional trade-offs between the competitive operational priorities. Meanwhile, the adoption of the technologies may lead to significant and sometimes unexpected changes of human work. These may be positive or negative and can affect workers on the shop-floor and workers in higher-level skill domains. Not surprisingly, the anticipated changes have therefore raised many relevant questions about their causes, nature, and effects.This thesis addresses this knowledge gap and attempts to build a comprehensive understanding of how Industry 4.0 technologies impact human work in manufacturing settings. The underlying functionalities of several key Industry 4.0 technologies have been specified, along with their expected impact on relevant job characteristics, including job complexity, job autonomy, and skill variety. In addition to the technical changes, we analyzed in-depth through a socio-technical lens, the iterative design process of work during digitalization. This was to specify and understand observed changes in job characteristics of operators and manufacturing engineers, showing job simplification and job enrichment, respectively. To understand the variations of how work was designed, we analyzed how underlying organizational and individual factors shaped the design process. Where work design knowledge was lacking, the motivation of system designers turned out to be an important individual factor affecting favorable work design outcomes

Assessing human-centricity in AI enabled manufacturing systems a socio-technical evaluation methodology

The emerging interest in Industry 5.0 is consistent with the growing importance of instilling human-centricity in manufacturing technological innovations. Human-centricity concerns the creation of a human-technology symbiosis that enables the capitalization of respective human and technical capabilities for optimal system performance. While Industry 5.0 advocates the need to consider human aspects already at the design of technical systems, there is currently a lack of insights regarding the relevant performance criteria to consider when evaluating human-centric manufacturing. This paper presents an evaluation methodology for artificial intelligence (AI)-enabled manufacturing in the transition towards Industry 5.0. It adopts a multi-viewpoint assessment via an appropriate set of social, technical and operational factors to be considered when designing or implementing human-centric AI. The methodology can guide designers and decision-makers to evaluate the embedding of AI into industrial work systems, providing clarity on relevant criteria to consider when moving towards human-centricity in AI-enabled manufacturing

Human in the AI Loop in Production Environments

The integration of Artificial Intelligence (AI) in manufacturing is often pursued as technology push. In contrast, this paper looks upon the AI-human interaction from a viewpoint that considers both to play an important role in reshaping their individual capabilities. It specifically focuses on how humans can play an important role in enhancing AI capabilities. The introduced concepts are tested in an industrial case study of vision-based inspection in production lines. Furthermore, the paper highlights the need to consider relevant implications for work design for AI integration. The contribution can be of practical value for system developers and work designers in how to target at the design stage the human contribution in AI-enabled systems for production environments

The redesign of blue- and white-collar work triggered by digitalization:collar matters

The implementation of digital technologies in the context of Industry 4.0 radically changes methods of production and thereby the jobs of blue-collar workers. Although the work design effects of digitalization on the operator 4.0 have been explored in the existing literature, less is known about the simultaneous effects on white-collar work and the underlying (re)design process of human work including the factors that shape this process. To address this gap, we performed an in-depth industrial case study of an organization in the process of digitalization. Our findings confirm the concurrent impact of digitalization on blue- and white-collar work and suggest that its human implications highly depend on the extent to which, and at what moment, human factors are considered during the design and implementation process. Where work design knowledge lacked, the motivation of system designers turned out to be an important individual factor to realize favorable work design outcomes. At the organizational level, results show the importance of early involvement of system users and incorporating social performance indicators in addition to operational performance indicators in the statement of project goals. Our findings provide important empirical input for the further development of human-centric models and theories that integrate the challenges and opportunities for blue- and white-collar workers that are emerging when adopting digital technologies

Grades, Student Satisfaction and Retention in Online and Face-to-Face Introductory Psychology Units: A Test of Equivalency Theory

There has been a recent rapid growth in the number of psychology courses offered online through institutions of higher education. The American Psychological Association has highlighted the importance of ensuring the effectiveness of online psychology courses (Halonen et al., 2013). Despite this, there have been inconsistent findings regarding student grades, satisfaction, and retention in online psychology units. Equivalency Theory (Simonson, 1999; Simonson et al., 1999) posits that online and classroom-based learners will attain equivalent learning outcomes when equivalent learning experiences are provided. We present a study of an online introductory psychology unit designed to provide equivalent learning experiences to the pre-existing face-to-face version of the unit. Using quasi-experimental methods, academic performance, student feedback, and retention data from 866 Australian undergraduate psychology students were examined to assess whether the online unit developed to provide equivalent learning experiences produced comparable outcomes to the 'traditional' unit delivered face-to-face. Student grades did not significantly differ between modes of delivery, except for a group-work based assessment where online students performed more poorly. Student satisfaction was generally high in both modes of the unit, with group-work the key source of dissatisfaction in the online unit. The results provide partial support for Equivalency Theory. The group-work based assessment did not provide an equivalent learning experience for students in the online unit highlighting the need for further research to determine effective methods of engaging students in online group activities. Consistent with previous research, retention rates were significantly lower in the online unit, indicating the need to develop effective strategies to increase online retention rates. While this study demonstrates successes in presenting students with an equivalent learning experience, we recommend that future research investigate means of successfully facilitating collaborative group-work assessment, and to explore contributing factors to actual student retention in online units beyond that of non-equivalent learning experiences

Human in the Loop of AI Systems in Manufacturing

Artificial Intelligence (AI) in manufacturing is typically looked upon from the view-point of its contribution to automation. Additionally, the role of AI in augmenting human activities has been the subject of a wide range of studies with impact on practical applications in manufacturing environments. Recently, the empowering effect of human and AI actors working in synergy has attracted increased atten-tion. After outlining relevant work, this chapter considers the potential emergent outcomes of such a synergy in a way that goes beyond automation or augmenta-tion. Aimed at both developers and work designers, the present work proposes a model of human-AI interaction along with an outline of key concepts and success criteria towards making human-AI interaction more effective