Today’s businesses face a critical need to balance sustainability, environmental responsibility, and operational excellence. Integrating lean and green methodologies holds promise for achieving this balance but presents challenges in real-sector implementation. Despite extensive research in the scientific community, a universally accepted model for integrated lean and green implementation is lacking. Limited research also explores the impact of lean tools on environmental performance indicators. This study aims to identify key economic and environmental performance indicators and investigate how they are influenced by lean tools. The primary objective is to develop a decision-making model facilitating lean tool implementation based on a company’s priorities for economic and environmental performance. The methodology involves a literature review, complemented by questionnaires and interviews with experts from academia and the real sector. The findings show that commonly used lean tools positively influence economic and environmental indicators. The decision-making model, tailored to chosen criteria priorities, was successfully validated using simulated and real company data, which makes it a potentially valuable resource for companies navigating integrated lean and green methodologies. While the obtained results show promise, it is important to highlight the limitations arising from the regional focus on Croatia and the inclusion of a subset of the manufacturing industry.

In the quest for industrial efficiency, human performance within manufacturing systems remains pivotal. Traditional time study methods, reliant on direct observation and manual video analysis, are increasingly inadequate, given technological advancements. This research explores the automation of time study methods by deploying deep learning models for action segmentation, scrutinizing the efficacy of various architectural strategies. A dataset, featuring nine work activities performed by four subjects on three product types, was collected from a real manufacturing assembly process. Our methodology hinged on a two-step video processing framework, capturing activities from two perspectives: overhead and hand-focused. Through experimentation with 27 distinctive models varying in viewpoint, feature extraction method, and the architecture of the segmentation model, we identified improvements in temporal segmentation precision measured with the F1@IoU metric. Our findings highlight the limitations of basic Transformer models in action segmentation tasks, due to their lack of inductive bias and the limitations of a smaller dataset scale. Conversely, the 1D CNN and biLSTM architectures demonstrated proficiency in temporal data modeling, advocating for architectural adaptability over mere scale. The results contribute to the field by underscoring the interplay between model architecture, feature extraction method, and viewpoint integration in refining time study methodologies.

Accelerated technology developments caused by Industry 4.0 create problems in its implementation. One of the most important factors that hinder the transition of companies is ignorance and, therefore, the fear of new technologies present among employees. Learning factories have proven to be one of the best solutions for introducing employees to the technologies of Industry 4.0. Croatia is significantly behind in implementing the features of Industry 4.0, especially compared to more developed countries. To facilitate the transition of the Croatian industry to Industry 4.0, it is necessary to acquaint existing and future employees with its technologies through learning factories. There is currently only one learning factory in Croatia, which is too few. This paper presents the process of design and establishment of a learning factory at the Faculty of Mechanical Engineering and Naval Architecture in Zagreb, which facilitates research work and education of students and employees with Industry 4.0.

Robotic pipe welding represents a key and rapidly evolving technology for the automation of pipe and pipe-joint welding processes with standard, intersecting, and complex geometries. This review analyses 84 studies published over the past three decades, categorising them into four primary research areas: general pipe welding, intersecting pipes, boiler and tube-to-tubesheet welding, and control and modelling. Two separate comparative analyses were conducted: one within intersecting pipe research and another within the control and modelling category. The aggregated findings reveal consistent, complementary patterns: simulation and laboratory experiments clearly dominate validation methods, while industrial-scale evaluations remain scarce. The results further demonstrate that control strategies, sensor integration, and validation levels are strongly interconnected, collectively determining system performance, reliability, and practical applicability. Despite significant progress, challenges remain, including system integration complexity, limited robustness in variable industrial environments, insufficient real-time adaptive control, and inconsistent quantitative performance evaluation. Further research should prioritise the development of digital twins, human–robot collaboration, multi-sensor fusion, reinforcement learning-based adaptive control, and scalable industrial deployment. This review provides an overview of current progress and outlines key directions for developing intelligent and reliable robotic pipe welding systems.