Search Results (7)

The evolution of Industry 4.0 and the emerging paradigm of Industry 5.0 have introduced disruptive technologies that are reshaping modern manufacturing environments. Among these, Digital Twin (DT) and Industrial Metaverse (IM) technologies are increasingly recognized for their potential to enhance Occupational Health and Safety (OHS). However, a comprehensive understanding of how these technologies integrate to support OHS in manufacturing remains limited. This study systematically explores the transformative role of DT and IM in creating immersive, intelligent, and human-centric safety ecosystems. Following the PRISMA guidelines, a Systematic Literature Review (SLR) of 75 peer-reviewed studies from the SCOPUS and Web of Science databases was conducted. The review identifies key enabling technologies such as Virtual Reality (VR), Augmented Reality (AR), Extended Reality (XR), Internet of Things (IoT), Artificial Intelligence (AI), Cyber-Physical Systems (CPS), and Collaborative Robots (COBOTS), and highlights their applications in real-time monitoring, immersive safety training, and predictive hazard mitigation. A conceptual framework is proposed, illustrating a synergistic digital ecosystem that integrates predictive analytics, real-time monitoring, and immersive training to enhance the OHS. The findings highlight both the transformative benefits and the key adoption challenges of these technologies, including technical complexities, data security, privacy, ethical concerns, and organizational resistance. This study provides a foundational framework for future research and practical implementation in Industry 5.0. Full article

With the advancement of global sustainable development goals and the introduction of the ‘dual-carbon’ strategy, intelligent manufacturing (IM) has become an important pathway to promote the transformation and upgrading of enterprises. However, the ways in which IM enhances environmental, social, and corporate governance (ESG) performance, along with its potential mechanisms, remain unexplored. This study employs a two-way fixed-effects model with panel data from 4417 Chinese listed firms spanning the period 2009–2022 to examine these relationships. It is found that IM significantly improves corporate ESG performance. Robustness tests confirm the reliability of these results, and mechanism analysis highlights the mediating effects of information transparency, green technology innovation, and supply chain collaborative innovation. Furthermore, the heterogeneity analysis indicates that IM has a notably stronger effect in high-carbon-emission sectors, state-owned enterprises, and high-tech industries. This suggests that policymakers should design differentiated policies based on industry and firm characteristics to promote the adoption of IM and foster sustainable development strategies. This research contributes to expanding the theoretical understanding of how IM affects ESG while also providing empirical evidence for enterprises and governments to promote green transformation. Full article

The deep integration of intelligent technology and the manufacturing industry is a crucial driving force for promoting green and low-carbon development, which is a key strategy for achieving sustainable development. Using panel data from 30 provinces in mainland China from 2010 to 2022, this study measures the level of intelligent development and the total factor carbon emission performance (TFCEP). Additionally, a mediating effect model is constructed to explore the impact of intelligent manufacturing (IM) on carbon emission performance (CEP) and its underlying mechanisms. The findings reveal that (1) the intellectualization of the manufacturing industry significantly enhances CEP, a conclusion that remains robust under various tests; (2) the impact of IM on CEP varies by regional geographical locations, the degree of economic agglomeration (EA), and whether the province is a low-carbon pilot area; and (3) the mechanism analysis indicates that IM improves CEP by promoting EA. Given that China is the world’s largest manufacturing country and the largest carbon emitter, analyzing the impact of its IM on CEP provides valuable theoretical insights and practical experiences for China and other manufacturing countries aiming to achieve a win–win situation of sustainable economic development and environmental improvement. Full article

It is no longer possible for China’s economy to grow by relying on the rapid expansion of manufacturing. On the one hand, China’s previous rough manufacturing development pattern seriously harmed the environment. On the other hand, China’s manufacturing productivity and international competitiveness have decreased as a result of the disappearance of demographic dividends and growing labor costs. China’s manufacturing firms must simultaneously increase productivity while lowering environmental pollution. This study, which takes intelligent manufacturing pilot demonstration projects as a quasi-natural experiment, investigates the impact of intelligent manufacturing (IM) on environmental, social and governance (ESG) performance using data from 2149 listed manufacturing firms in China from 2009 to 2021. The results indicate that ESG performance of the listed firms could be improved using IM. The heterogeneity test reveals that IM in non-state-owned firms helps to improve ESG performance at the 1% significance level, while the effect is not significant in state-owned firms. Moreover, the effect in eastern China is significant at the 1% level and at the 5% level in western China, but not significant in central and northeastern China. The two channels through which IM improves corporate ESG performance are promoting innovation investment and improving the quality of the information environment. This study also verifies that both internal and external supervision could strengthen the positive impact of IM on corporate ESG performance, which provides empirical evidence for strengthening the supervision of manufacturing firms. The conclusions of the study reveal the internal force of manufacturing firms to improve ESG performance and also provide theoretical support for their implementation of IM projects. Full article

With the rapid development of new-generation information technologies such as big data, cloud computing, Internet of Things, and mobile internet in traditional manufacturing, the development of intelligent manufacturing (IM) is accelerating. Digital twin is an important method to achieve the goal of IM, and provides an effective means for the integrated development of design and manufacturing (R & M). In view of the problems of long installation and debugging cycles, and process parameters requiring multiple trial and error in the research and development (R & D) process of laser melting deposition (LMD) equipment, this paper focuses on building an LMD equipment model based on digital twin technology. It involves performing virtual assembly, motion setting, collision inspection, and PLC debugging, thereby providing an innovative method and insights for improving the R & D efficiency of the IM of LMD equipment. Full article

The data-oriented paradigm has proven to be fundamental for the technological transformation process that characterizes Industry 4.0 (I4.0) so that big data and analytics is considered a technological pillar of this process. The goal of I4.0 is the implementation of the so-called Smart Factory, characterized by Intelligent Manufacturing Systems (IMS) that overcome traditional manufacturing systems in terms of efficiency, flexibility, level of integration, digitalization, and intelligence. The literature reports a series of system architecture proposals for IMS, which are primarily data driven. Many of these proposals treat data storage solutions as mere entities that support the architecture’s functionalities. However, choosing which logical data model to use can significantly affect the performance of the IMS. This work identifies the advantages and disadvantages of relational (SQL) and non-relational (NoSQL) data models for I4.0, considering the nature of the data in this process. The characterization of data in the context of I4.0 is based on the five dimensions of big data and a standardized format for representing information of assets in the virtual world, the Asset Administration Shell. This work allows identifying appropriate transactional properties and logical data models according to the volume, variety, velocity, veracity, and value of the data. In this way, it is possible to describe the suitability of relational and NoSQL databases for different scenarios within I4.0. Full article

Since the 1970s, the application of microprocessor in industrial machinery and the development of computer systems have transformed the manufacturing landscape. The rapid integration and automation of production systems have outpaced the development of suitable human design criteria, creating a deepening gap between humans and systems in which human was seen as an important source of errors and disruptions. Today, the situation seems different: the scientific and public debate about the concept of Industry 4.0 has raised awareness about the central role humans have to play in manufacturing systems, the design of which must be considered from the very beginning. The future of industrial systems, as represented by Industry 4.0, will rely on the convergence of several research fields such as Intelligent Manufacturing Systems (IMS), Cyber-Physical Systems (CPS), Internet of Things (IoT), but also socio-technical fields such as social approaches within technical systems. This article deals with different human social dimensions associated with CPS and IoT and focuses on their conceptual evolution regarding automated production systems’ sociability, notably by bringing humans back in the loop. Hereby, this paper aims to take stock of current research trends to show the importance of integrating human operators as a part of a socio-technical system based autonomous and intelligent products or resources. Consequently, different models of sociability as a way to integrate humans in the broad sense and/or the develop future automated production systems have been identified from the literature and analysed. Full article