The digitization of the manufacturing industry, 10 years after the introduction of the Industry 4.0 concept, is still one of the most demanding tasks for the companies, especially for SMEs. As one of the biggest barriers in new business model implementation, the lack of strategy and workforce skills is frequently mentioned in the literature. The high level of investments it requires and the perception of high risks with unclear future benefits can be avoided with readiness factor calculation. This paper presents a novel model for readiness factor calculation, oriented to process planning and based on decision support systems. The model enables the definition of the optimal strategic plan for the digitization with the use of decision support systems (analytic hierarchy process) and through the use of statistical methods implemented within the model it minimizes the influence of human subjectivity and quantification of qualitative criteria. This innovative approach enables the understanding of the transition process to new technology-enabled business models, in this case oriented towards process planning. The useability and reliability of the model is proven in a case study of a metal machining company.

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.

The field of product development (PD) has an excellent opportunity to achieve the benefits of lean principles. The literature on lean product development (LPD) is growing and is extensive, but it needs to be more systematic. A distinct, comprehensive, and up-to-date review of LPD literature is necessary. Motivated by studying and understanding contemporary themes and the current trends in the LPD research area, this article reviewed 85 previously published papers across three scientific databases from 2011 to 2022. Using literature review methodology, we determined for every article: research motivation or gap filled, key findings and significant contributions, and suggestions for future research. That helps structure the field of research into nine domains according to the gaps the authors wish to fill. The results indicate that over the past decade, the LPD literature has shown trends towards synergy of LPD with green, sustainability, circular economy, industry 4.0, and digitalisation. Finally, the article proposes six future research directions.

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.

This article aims to enhance existing understanding of incorporating sustainability aspects during the product development and seeks to fill the gaps regarding the relationship between LPD and sustainability aspects. An up-to-date literature review w...