Six-Gear Roadmap towards the Smart Factory

SSA-SL Transformer for Bearing Fault Diagnosis under Noisy Factory Environments

Among the smart factory studies, we describe defect detection research conducted on bearings, which are elements of mechanical facilities. Bearing research has been consistently conducted in the past; however, most of the research has been limited to using existing artificial intelligence models. In addition, previous studies assumed the factories situated in the bearing defect research were insufficient. Therefore, a recent research was conducted that applied an artificial intelligence model and the factory environment. The transformer model was selected as state-of-the-art (SOTA) and was also applied to bearing research. Then, an experiment was conducted with Gaussian noise applied to assume a factory situation. The swish-LSTM transformer (Sl transformer) framework was constructed by redesigning the internal structure of the transformer using the swish activation function and long short-term memory (LSTM). Then, the data in noise were removed and reconstructed using the singular spectrum analysis (SSA) preprocessing method. Based on the SSA-Sl transformer framework, an experiment was performed by adding Gaussian noise to the Case Western Reserve University (CWRU) dataset. In the case of no noise, the Sl transformer showed more than 95% performance, and when noise was inserted, the SSA-Sl transformer showed better performance than the comparative artificial intelligence models.

On Deploying Blockchain Technologies in Supply Chain Strategies and the COVID-19 Pandemic: A Systematic Literature Review and Research Outlook

The emergence of a new pandemic, known as COVID-19, has touched various sections of the supply chain (SC). Since then, numerous studies have been conducted on the issue, but the need for a holistic review study that highlights the gaps and limits of previous research, as well as opportunities and agendas for future studies, is palpable. Through a systematic literature review on blockchain technology (BCT) deployment in supply-chain management (SCM) concerning the COVID-19 pandemic, this research seeks to add to the content of previous studies and to enlighten the path for future studies. Relevant papers were found using a variety of resources (Scopus, Google Scholar, Web of Science, and ProQuest). Seventy-two articles were systematically selected, considering the PRISMA procedure, and were thoroughly analyzed based on BCT, methodologies, industrial sectors, geographical, and sustainability context. According to our findings, there is a significant lack of empirical and quantitative methodologies in the literature. The majority of studies did not take specific industries into account. Furthermore, the articles focusing on the sustainability context are few, particularly regarding social and environmental issues. In addition, most of the reviewed papers did not consider the geographical context. The results indicate that the deployment of BCT in several sectors is not uniform, and this utilization is reliant on their services during the COVID-19 pandemic. Furthermore, the concentration of research on the impacts of the BCT on SCM differs according to the conditions of various countries in terms of the consequences of the COVID-19 pandemic. The findings also show that there is a direct relationship between the deployment of BCT and sustainability factors, such as economic and waste issues, under the circumstances surrounding COVID-19. Finally, this study offers research opportunities and agendas to help academics and other stakeholders to gain a better knowledge of the present literature, recognize aspects that necessitate more exploration, and drive prospective studies.

Industry 4.0 HUB: A Collaborative Knowledge Transfer Platform for Small and Medium-Sized Enterprises

Industry 4.0 brings opportunities for small- and medium-sized enterprises (SMEs), but SMEs are lacking Industry 4.0 knowledge, and this might result in a challenge to support SMEs’ competitiveness and productivity. During recent years, the European Commission and some government initiatives have been fostering the transition toward Industry 4.0 for SMEs through the creation of Digital Innovation Hubs, the Plattform Industrie 4.0, and some other initiatives. Nonetheless, the authors consider that the lack of knowledge is still a risk toward Industry 4.0 transformation for SMEs. New ways to improve Industry 4.0 knowledge management and especially the knowledge transfer must be developed. When SMEs start the transition to Industry 4.0, first of all, they do not want to start from scratch, and secondly, it can be easy to get lost in the multitude of technologies and tools that are available in today’s market. There is a gap in which to provide a collaborative Industry 4.0 knowledge transfer platform or hub designed for SMEs. Therefore, this research aims to enhance Industry 4.0 knowledge transfer through the development of a collaborative, web-based knowledge transfer Industry 4.0 platform. The outcome of this research is a developed platform that will be referred to as Industry 4.0 HUB.