The increased prevalence of human-AI collaboration is reshaping the manufacturing sector, fundamentally changing the nature of human work and training needs. While high automation improves performance when functioning correctly, it can lead to problematic human performance (e.g., defect detection accuracy, response time) when operators are required to intervene and assume manual control of decision-making responsibilities. As AI capability reaches higher levels of automation and human–AI collaboration becomes ubiquitous, addressing these performance issues is crucial. Proper worker training, focusing on skill-based, cognitive, and affective outcomes, and nurturing motivation and engagement, can be a mitigation strategy. However, most training research in manufacturing has prioritized the effectiveness of a technology for training, rather than how training design influences motivation and engagement, key to training success and longevity. The current study explored how training workers using an AI system affected their motivation, engagement, and skill acquisition. Specifically, we manipulated the level of automation of decision selection of an AI used for the training of 102 participants for a quality control task. Findings indicated that fully automated decision selection negatively impacted perceived autonomy, self-determined motivation, behavioral task engagement, and skill acquisition during training. Conversely, partially automated AI-enhanced motivation and engagement, enabling participants to better adapt to AI failure by developing necessary skills. The results suggest that involving workers in decision-making during training, using AI as a decision aid rather than a decision selector, yields more positive outcomes. This approach ensures that the human aspect of manufacturing work is not overlooked, maintaining a balance between technological advancement and human skill development, motivation, and engagement. These findings can be applied to enhance real-world manufacturing practices by designing training programs that better develop operators’ technical, methodological, and personal skills, though companies may face challenges in allocating substantial resources for training redevelopment and continuously adapting these programs to keep pace with evolving technology.

Advancements in artificial intelligence (AI) have ushered in the era of the fourth industrial revolution, transforming workplace dynamics with AI's enhanced decision-making capabilities. While AI has been shown to reduce worker mental workload, improve performance, and enhance physical safety, it also has the potential to negatively impact psychosocial factors, such as work meaningfulness, worker autonomy, and motivation, among others. These factors are crucial as they impact employee retention, well-being, and organizational performance. Yet, the impact of automating decision-making aspects of work on the psychosocial dimension of human-AI interaction remains largely unknown due to the lack of empirical evidence. To address this gap, our study conducted an experiment with 102 participants in a laboratory designed to replicate a manufacturing line. We manipulated the level of AI decision support—characterized by the AI's decision-making control—to observe its effects on worker psychosocial factors through a blend of perceptual, physiological, and observational measures. Our aim was to discern the differential impacts of fully versus partially automated AI decision support on workers' perceptions of job meaningfulness, autonomy, competence, motivation, engagement, and performance on an error-detection task. The results of this study suggest the presence of a critical boundary in automation for psychosocial factors, demonstrating that while some automation of decision selection can nurture work meaningfulness, worker autonomy, competence, self-determined motivation, and engagement, there is a pivotal point beyond which these benefits can decline. Thus, balancing AI assistance with human control is vital to protect psychosocial well‑being. Practically, industry and operations managers should keep employees involved in decision making by adopting partial, confirm‑or‑override AI systems that sustain motivation and engagement, boosting retention and productivity.

Multi-product assembly lines are commonly used for their flexibility in mass-customized production, but their complexity makes identifying the root causes of defects difficult. This research addresses the limitations of traditional methods for analyzing the root causes of defects in this context. It introduces a four-step methodology for preparing data and conducting descriptive analysis of defect root causes. Product families are created by segmenting production data from the company's information systems using AHC algorithms or company knowledge. Defect rates are then calculated for each product group and the transitions between product sequences. Finally, a CART decision tree is used to generate rules that lead to defective clusters. These rules are used for descriptive analysis of defect root causes and are seen as improvement opportunities for multi-product assembly lines. The methodology was applied to two case studies using real production data. This led to the identification and validation of the root causes of defects by the partner companies. Nevertheless, limitations must be taken into account, e.g., the reliance on expert judgment for the identification of root causes, the sensitivity of the data used, and the necessity of regenerating the decision tree for each new analysis.

This paper introduces a novel framework for replicating manual processes in digital twins. The proposed approach allows the modeling of manual process variability, a key aspect often overlooked in the literature. Our research highlights the potential of AI to tailor the digital twin to specific contexts influenced by human factors. The model's accuracy can be improved with each simulation synchronization cycle through supervised machine learning, which enhances the alignment of the virtual and physical processes. The proposed digital twin framework aims to avoid discrepancies from the physical counterpart, disregarding decisions based on fixed parameters that do not evolve over time and ensuring a higher degree of realism of the virtual replica. Ultimately, we aim to design the digital twin as a realistic and effective decision-making aid for all human participants engaged in the digital twin loop.

Non-critical medical equipment manufacturers are looking for alternative ways of collecting data as IT departments in hospitals will not provide access to their local network. Weightless could solve this problem with a low-cost alternative, a new wireless architecture with long-range capabilities indoors. This paper presents experimental data on the use of the Weightless Protocol inside buildings. Our results have shown that walls and floors are important obstacles and have more impact on the range limitations than distance. Actual coverage with Weightless is better than expected for all test locations.

This paper measures the relative influence of a set of determinants on firms' adoption of advanced planning and scheduling systems (APS). Our theoretical model was tested on data collected from 61 senior managers in the wireless communication sector. Our findings confirm the importance to consider the characteristics of business relationships when deciding to adopt an APS system and demonstrate that adaptive planning and optimization systems are generally implemented in tightly coupled supply networks. Findings also confirm that manufacturing firms and retailers generally implement an ERP system before deciding to exploit the unique capabilities of an APS to optimize their activities with their upstream supply chain partners.

ERP systems could be considered as the most difficult systems to be assimilated. Very little research, however, has been realised to investigate the determinants of success of the ERP assimilation process. There has been also a dearth in research about ERP projects in general and ERP post-implementation and assimilation in particular in developing countries. Using a case study methodology grounded in the diffusion of innovation theory, this study tries to understand the factors that could improve or challenge the ERP systems assimilation process in three Tunisian companies. Our findings highlighted the importance of several factors such as top management support, strategic alignment, IT expertise, training, communication, organizational culture, and coercive pressures.

In this paper, we propose a network propagation model that quantifies the diffusion phenomenon of an ERP in industrial network via a simulation approach. The proposed simulation model is influenced by external and internal factors which take into account the network structure. This approach allowed us to describe ERP systems diffusion with an epidemiological angle, and keep track of periodical development of the diffusion phenomenon for each enterprise within a business network.

Observations in industrial sectors indicate that companies that evolve in an industry in which a specific ERP system has been adopted by a number of members are more likely to adopt the same software. In this paper, we investigate two main effects influencing this diffusion pattern: the exposure of a firm in the network to its neighbours and the power of a firm within the network. To perform this analysis, we propose two models: a direct model that characterized the influence of immediate related ties, as well as an indirect model that characterized the influence of ties of ties. Network ties are here defined by interlock between board directorates. The statistical analysis of Canadian firm's data suggests that network ties, especially indirect exposure, influence the diffusion of ERP systems. The influence of direct and indirect exposure and firm power also appear to differ significantly from one ERP system to another.

This paper presenst a real-time execution system to support adaptive manufacturing strategies. We propose a functional architecture integrating optimisation and simulation techniques with Enterprise Resource Planning and Manufacturing Execution Systems. The model allows the implementation of real-time decision-making components and feedback-loop mechanisms. A demonstrative example based on a real life production scenario is presented to illustrate how such integration can be achieved.

In remanufacturing projects, work space constraints can limit the simultaneous processing of any pair of jobs requiring the same physical location and often force stronger limitations on the project schedule than precedence constraints. This paper introduces a new priority heuristic rule where the impact of work space constraints is measured in terms of the total minimum remaining project duration. This measure is used to determine the resource allocation at every scheduling decision time point based on the currently active precedence and work space constraints imposed on the schedulable activities. Our experimental results based on a set of 15 real remanufacturing projects shows that the proposed procedure performs better than well reputed heuristic rules.