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Bibliometric analysis was performed to study the development of publications related to Industry 4.0 and its key technologies in Vietnam. Comparisons with data from other ASEAN countries, and with global data have been done to identify distinctive characteristics of Industry 4.0 literature from Vietnam. The collection of 1,470 retrieved papers was analysed to answer seven research questions. Our results highlighted some valuable insights of Industry 4.0 literature in Vietnam. The number of papers in Industry 4.0 in Vietnam increased rapidly in recent years, mostly focused on Computer Science, Engineering, and Mathematics. Iran, China, and South Korea were the most productive partner countries with Vietnam in Industry 4.0. Machine learning, artificial intelligence, big data, deep learning, Internet of things, neural networks, and data mining were among the most popular research themes in Industry 4.0 in Vietnam. Vietnam ranked third among 10 Southeast Asian countries, based on the number of published papers in Industry 4.0, but the gap with the two top countries was large. Compared to the global data, the annual growth rate of Industry 4.0 papers in Vietnam, and other Southeast Asian countries was lower. Findings from this work can be helpful for other scholars in establishing potential future research lines related to Industry 4.0 in Vietnam. [ABSTRACT FROM AUTHOR]

The goal of this paper is to explain why digital innovation is so important in business organizations in order to survive in Industry 4.0. The study helps to understand the new era of Industry 4.0 and the importance of introducing digital innovation into organizations. A systematic review of the literature and studies on Industry 4.0 and digital innovation were synthesized to find answers to the research questions. To improve their manufacturing industry, organizations have implemented digital technologies such as augmented reality, robotic sensing, artificial intelligence, cloud computing, cyber physical systems, and remote sensing technologies. These technologies focused on automating logistics and supply chain systems, improving manufacturing system performance, and simplifying automated production systems. Because digital innovations save time and energy, employees can devote more time and energy to creative and innovative activities. Organizations should plan to implement digital technology in order to keep the environment healthy and sustainable while meeting the demands of customers, consumers, and the Industry 4.0 dimension. [ABSTRACT FROM AUTHOR]

The so‐called age of AI, industry 4.0, the fourth industrial revolution, etc. all attempt to conjure into existence a new technological paradigm. Should we believe the hype? This paper draws on neo‐Schumpeterian and régulation theory to widen the scope of this debate and examine techno‐economic and institutional discontinuities. In exploring these discontinuities, this paper argues, first, that growth regimes are not necessarily tenable as indicators of new paradigms, and, second, that there are (infra)structural discontinuities between the ICT/post‐Fordist era and those of the AI/platform era. Platformisation entails a distinct institutional logic, regime of accumulation (RA) and mode of social régulation (MSR). The clusters of technological and institutional changes behind this shift have not yet been sufficiently addressed by economic geography and associated literatures. In reconceptualising the shift in terms of (infra)structural discontinuity, the paper synthesises neo‐Schumpeterian and régulation theory to identify both technological and institutional changes in the régulation of capitalist accumulation. [ABSTRACT FROM AUTHOR]

As the global manufacturing industry entered its fourth revolution (4IR, or Industry 4.0), innovations such as robotics, automation, and artificial intelligence are set to take over and improving the global industries. As the number of active industrial robots worldwide is increasing every day in different applications and industries automation continues to create new types of robots with improved utility and function. In the AAC Industry, these innovations already took place for some time in different process phases and applications such as oiling, autoclaves stacking pins, handling, packing, and so forth. Through this research, advantages commonly attributed to automation and robotics include higher production rates and increased productivity, more efficient use of materials, better product quality, improved safety, shorter workweeks for labor, and reduced factory lead times. Especially reduced scrap and waste through improved accuracy, increased efficiency, and operation around the clock could be emphasized. Based on the accumulated experience of WKB Systems GmbH in the field of the forging industry, selected models, real‐life application case studies, and implementation of different robotics in the AAC industry are discussed in this paper. These examples such as linear robots for autoclaves stacking pins, articulated robots for AAC packing and sorting line, and hybrid systems for handling and packing AAC insulation boards are studied in detail. [ABSTRACT FROM AUTHOR]

To successfully implement circular economy processes into present value chains, economic feasibility of disassembly processes is essential. Current developments in science and technology, such as artificial intelligence and Internet of Things, foster steep progression in the field of robotics. In this review, the current research on robotics in disassembly is investigated by a systematic literature review. The results were clustered in a framework system distinguishing between applied and basic research on the two main streams of disassembly automation research, namely, predefined processes and adaptable, flexible automation. [ABSTRACT FROM AUTHOR]

Industry 4.0 is a term used to refer to the fourth industrial revolution, characterized by the introduction of new technologies, such as the Internet of Things, Big Data, and artificial intelligence (AI). As the number of connected devices in industrial settings grows, energy optimization of such sensors becomes increasingly essential. This paper proposes an energy optimization framework for sensor nodes in Industry 4.0. The framework is based on energy efficiency, energy conservation, and energy harvesting principles. It is designed to optimize the energy consumption of sensor nodes while maintaining their performance. The framework includes dynamic power management, scheduling, and harvesting techniques to reduce energy consumption while maintaining performance. In addition, the framework provides a comprehensive approach to energy optimization, including advanced analytics and AI to predict energy consumption and optimize energy use. The proposed model reached 96.93% sensitivity, 91.36% false discovery rate, 11.28% false omission rate, 90.12% prevalence threshold, and 91.24% threat score. The proposed framework is expected to improve the performance of sensor nodes in Industry 4.0, enabling increased efficiency and cost savings. [ABSTRACT FROM AUTHOR]

Recently, the term "Industry 4.0" has emerged to characterize several Information Technology and Communication (ICT) adoptions in production processes (e.g., Internet‐of‐Things, implementation of digital production support information technologies). Business Analytics is often used within the Industry 4.0, thus incorporating its data intelligence (e.g., statistical analysis, predictive modelling, optimization) expert system component. In this paper, we perform a Systematic Literature Review (SLR) on the usage of Business Analytics within the Industry 4.0 concept, covering a selection of 169 papers obtained from six major scientific publication sources from 2010 to March 2020. The selected papers were first classified in three major types, namely, Practical Application, Reviews and Framework Proposal. Then, we analysed with more detail the practical application studies which were further divided into three main categories of the Gartner analytical maturity model, Descriptive Analytics, Predictive Analytics and Prescriptive Analytics. In particular, we characterized the distinct analytics studies in terms of the industry application and data context used, impact (in terms of their Technology Readiness Level) and selected data modelling method. Our SLR analysis provides a mapping of how data‐based Industry 4.0 expert systems are currently used, disclosing also research gaps and future research opportunities. [ABSTRACT FROM AUTHOR]

Background: Consumer and advanced consumption culture in modern society is an era that focuses on individual personality and value, and "my own customized products", or customized marketing strategies, are actively being developed throughout the industry. Recently, IT technologies that can support personalized services such as artificial intelligence, ubiquitous systems, and marketing automation have been recognized for their potential, directly or indirectly affecting distribution industries affected by personal consumption culture. Accordingly, customized products or services, i.e., customization, are attracting attention as an effective methodology to cope with such market changes. Objectives: Among the necessities used by modern women, cosmetics account for an endless interest in beauty and maintaining physical and mental health, and as the cosmetics market expands, it is considered that the cosmetics industry needs a clearer and in‐depth study on the cosmetics submarket to satisfy consumers' diverse needs. Methods: This review paper is a literature review, and a narrative review approach has been used for this study. A total of 300 to 400 references were selected using representative journal search websites such as PubMed, Google Scholar, Scopus, ResearchGate, LitCovid, DBPia, and RISS, of which a total of 42 papers were selected in the final stage based on 2013 to 2022 using PRISMA flow diagram. Results: This study suggested to indicate the changes in the cosmetics market due to the emergence of cosmetics curation services after the coronavirus disease‐19 pandemic, advanced changes in consumer purchase patterns following the 4th Industrial Revolution, and significant future prospects of cosmetics curation services. Conclusion: As the beauty and cosmetology industry is expected to develop in the future, it will grow as a centerpiece of the beauty industry and symbolizes nationalized cultural pride. Therefore, this review article will be continuing to promote customization as a premium beauty service for dermatological condition in Republic of Korea through corporate analysis. [ABSTRACT FROM AUTHOR]

Predictive maintenance is a field of study whose main objective is to optimize the timing and type of maintenance to perform on various industrial systems. This aim involves maximizing the availability time of the monitored system and minimizing the number of resources used in maintenance. Predictive maintenance is currently undergoing a revolution thanks to advances in industrial systems monitoring within the Industry 4.0 paradigm. Likewise, advances in artificial intelligence and data mining allow the processing of a great amount of data to provide more accurate and advanced predictive models. In this context, many actors have become interested in predictive maintenance research, becoming one of the most active areas of research in computing, where academia and industry converge. The objective of this paper is to conduct a systematic literature review that provides an overview of the current state of research concerning predictive maintenance from a data mining perspective. The review presents a first taxonomy that implies different phases considered in any data mining process to solve a predictive maintenance problem, relating the predictive maintenance tasks with the main data mining tasks to solve them. Finally, the paper presents significant challenges and future research directions in terms of the potential of data mining applied to predictive maintenance. This article is categorized under:Application Areas > Industry Specific ApplicationsTechnologies > Internet of Things [ABSTRACT FROM AUTHOR]

In this paper, we describe specific deployments of the Smart Connected Worker (SCW) Edge Platform for Smart Manufacturing through implementation of four instructive real‐world use cases that illustrate the role of people in a Smart Manufacturing paradigm through which affordable, scalable, accessible, and portable (ASAP) information technology (IT) acquires and contextualizes data into information for transmission to operation technologies (OT). For case one, the platform captures the relationships between energy consumption and human workflows for improved energy productivity while workers interact with machines during semiconductor manufacturing. The platform utilizes human cognition to identify anomalous machine behavior for root cause analysis of system faults via neural network (NN) that recognize alarm postures of workers with cameras. For case two, a smart assembly line is demonstrated for state monitoring and fault detection. Machine learning (ML) models are used to recognize system states and identify fault scenarios with human intervention. For case three, the platform monitors human–machine interactions to classify manufacturing machine states for proper operations and energy productivity. Internal energy states of individual or collections of manufacturing equipment are determined via NN based algorithms that disaggregate signals associated with smart metering typically deployed at manufacturing facilities. These methods predict the real time energy profile of each machine from the total energy profile of a manufacturing site. For case four, a software defined sensor system built with scientific workflow engines is demonstrated for contextualizing data from laser surface refraction for characterization, and diagnostics in the processing of additively manufactured titanium alloy. [ABSTRACT FROM AUTHOR]

The challenge of sustainably producing goods and services for healthy living on a healthy planet requires simultaneous consideration of economic, societal, and environmental dimensions in manufacturing. Enabling technology for data driven manufacturing paradigms like Smart Manufacturing (a.k.a. Industry 4.0) serve as the technological backbone from which sustainable approaches to manufacturing can be implemented. Unfortunately, these technologies are typically associated with broader and deeper factory automation that is often too expensive and complex for the small and medium sized manufacturers (SMMs) that comprise the majority of manufacturing business in the USA and for whom their most valuable asset are the people whose jobs automation while replace. This paper describes an edge intelligent platform to integrate internet‐of‐things technologies with computing hardware, software, computational workflows for machine learning, and data ingestion, enabling SMMs to transition into smart manufacturing paradigms by leveraging the intelligence of their people. The platform leverages consumer grade electronics and sensors (affordable and portable), customized software with open source software packages (accessible), and existing communication network infrastructures (scalable). The software systems are implemented via Kubernetes orchestration of Docker containerization to ensure scalability and programmability. The platform is adaptive via computational workflow engines that produce information from data by processing with low‐cost edge computing devices while efficiently accessing resources of cloud servers as needed. The proposed edge platform connects workers to technological resources that provide computational intelligence (i.e., silicon‐based sensing and computation for data collection and contextualization) to enable decision making at the edge of advanced manufacturing. [ABSTRACT FROM AUTHOR]

This paper examines the impact of Industry 4.0 (I4.0) technologies on employees workload in contact centers. For that, we adopted the NASA task load index questionnaire to assess the workload of 100 employees from different contact centers in India that have been adopting I4.0 technologies. The collected data is analyzed through multivariate techniques. This study is grounded on concepts from the multiple resource theory. Our findings indicate positive and negative effects of I4.0 on employees workload, conditioned on the adopted technologies (i.e., Internet‐of‐Things, cloud computing, big data, machine learning/artificial intelligence, remote monitoring, and wireless sensors) and workload dimensions considered (i.e., mental demand, physical demand, temporal demand, overall performance, effort, and frustration level). Identifying I4.0's impacts on employees workload allows planning of managerial efforts to mitigate potential issues while setting clear expectations related to the digital transformation of contact centers' processes and services. [ABSTRACT FROM AUTHOR]

This paper analyses the definition and connotation of Industry 4.0 and proposes that intelligent manufacturing and innovative service are the two core elements of the Industry 4.0. The paper also discusses standardization, integrated information system, training and education, artificial intelligence applications, and the infrastructure issues for the development of Industry 4.0. [ABSTRACT FROM AUTHOR]

The concept of Industry 4.0 has been widely recognized by the world since its introduction in 2011 Hannover Industrial Expo. Industry 4.0 strategy does not only revolutionize the manufacturing system and processes but also lead to the formation of the intelligent supply chain. Currently, Industry 4.0‐based intelligent supply chain has attracted a lot of attention from academia; however, studies on performance measurement indicator of intelligent supply chain are still lacking. To fill this gap, this paper first introduces the impact of Industry 4.0 on supply chain management. After analysing key characteristics of intelligent supply chain, the paper proposes a performance measurement indicator framework consisting of seven indicators. This indicator framework enriches the theoretical knowledge of supply chain performance evaluation and provides an efficient way to improve the operational performance of intelligent supply chain management. [ABSTRACT FROM AUTHOR]

Safety management determines and implements safety policy. It is closely intertwined with industrial development. As the Fourth Industrial Revolution, Industry 4.0 presents new challenges and opportunities for safety management. The paper identifies three stages, namely, Safety 1.0, Safety 2.0 and Safety 3.0, for the evolution of safety management by considering safety principles, technologies and modes. A theoretical framework that integrates Safety 3.0 and Industry 4.0 is presented. Furthermore, knowledge graphs are developed with Citespace to explore the status of cross‐topic research on safety management and Industry 4.0. Potential opportunities for integrating Safety 3.0 and Industry 4.0 are discussed. [ABSTRACT FROM AUTHOR]

The new Fourth Industrial Revolution (IR 4.0) trend is driven by the concept of automation and artificial intelligence (AI). However, Malaysia is slightly behind Singapore in terms of adopting AI innovation among ASEAN countries. This paper aims to conduct a literature review of machine learning to overcome subjectivity and bias in risk ranking decision‐making. An introduction to machine learning concerning accident risk analysis is presented, and the challenges of its application in Malaysia are discussed. Existing machine learning features were evaluated to identify the feasible application in industrial accident analysis and ensure safety decision‐making consistency. This review observed how the IR 4.0 approaches were used in the risk analysis, especially on supervised machine learning. This study also highlights the finding from the previous works on challenges in utilizing supervised machine learning, which is the need to have publicly accessible large database from industries and agencies such as the Department of Occupational Safety and Health (DOSH) Malaysia for the development of algorithms, which can potentially improve accident risk analysis and safety, especially for Malaysian industries. [ABSTRACT FROM AUTHOR]

In the era of internet and robots, sophisticated devices and real‐time simulation, the construction sector remains highly dependent on manual labor which is also the main source of delays, lack of quality, and human injuries. The rapid development in technological areas such as mechatronics, computers, sensors, programming, human interfaces and artificial intelligence (AI) – offers huge potential for modern applications. Nowadays, additive manufacturing is rapidly growing, and it could offer efficient solutions for steel construction since it can be executed in any shape and scale. There are a few solutions in additive manufacturing technologies, which can process materials and produce components. However, there is a gap in the market for capacity to link the processed material conditions and characteristics to the production parameters in real‐time. Real‐time and cognitive re‐planning of the process parameters becomes significantly important when components with large‐scale and complex shapes are considered. This paper presents a summary of existing technologies for automatic construction process, it identifies the critical issues which hamper the adoption of new technologies in construction and describes an additive manufacturing solution currently being implemented. [ABSTRACT FROM AUTHOR]

Digitisation offers significant opportunities for the formulated product industry to transform the way it works. Recent developments in the fields of artificial intelligence, machine learning, robotics and high performance computing present disruptive and transformative methods to accelerate the R&D process. These methods include physics‐based chemical simulation, data driven models and hybrid approaches, each of which has been shown to provide new physical and chemical insights that can augment experimental R&D. However, successfully exploiting these technologies in R&D organisations at scale is challenging due to the high level of domain specialisation required and the need for novel models for the formulation domain. In this paper we give an overview of some of the digital technology demonstrators we have developed for formulated product R&D. These demonstrators focus on computational modelling methods. In particular we discuss how we tackled the issue of domain specialisation and the challenges we encountered in creating accurate models for formulation problems. © 2020 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

Although still in its early stages, the Fourth Industrial Revolution (FIR), which involves a broad range of artificial intelligence, robotics and machine learning technologies, will fundamentally change the way many of us work and relate to one another. Driven by technology, this transformation presents a range of challenges, as well as opportunities, where we might expect Human Resource (HR) professionals would lead the way. However, little is currently known in relation to how prepared HR professionals in Australia are to ready their organisations for this new era of work and associated resourcing. Consequently, this paper goes some way towards closing that gap. Specifically, the study reported here sets out to explore the levels of preparedness amongst Australian HRM professionals for the impact of the FIR on organisations, workplaces, jobs and skills, as well as on their own professional roles and competencies. The study utilised a sequential mixed methods research design with two phases, the first was qualitative focus groups (n = 5) followed by a quantitative online survey of selected senior HR practitioners (n = 150). The findings indicate that, while most believe that FIR technologies might be useful for their organisations and assist with improving job performance, increasing productivity and making jobs easier for employees, contrarily many did not intend to use them in the foreseeable future. Marginal support was also evident in relation to the potential contributions of FIR technologies to HR process enhancement and overall HR effectiveness. Most respondents were also not impressed with the lack of current Australian government FIR strategies and policies. Key points: Organisations differ in their development stages.Similarly, there is divergence amongst Australian HR professionals with respect to their understanding of the likely impacts of the Fourth Industrial Revolution, and their preparedness for them.HR professionals' skills, capabilities and competencies need to be upgraded.Governments and professional associations also need to become better prepared to address the challenges. [ABSTRACT FROM AUTHOR]

Intelligent manufacturing system is considered as the key of industrial competitiveness, which has a far‐reaching impact on the enterprise development. This paper conducts an analysis of intelligent manufacturing in small and medium‐sized enterprises (SMEs). The study provides insight on upgrading manufacturing SMEs in industry 4.0 era. [ABSTRACT FROM AUTHOR]

Industry 4.0 integrates the physical, digital, and biological realms by applying digital automation in systems, processes, and manufacturing facilities. Industry 4.0 is actively shaping the development of intelligent food processing industries in cultivated meat (CM) sector. This integration plays a crucial role in accelerating progress within the global CM sector, facilitating the achievement of its objectives related to food sustainability, security, human health, environmental concerns, and hygiene. Incorporating Industry 4.0 into CM manufacturing systems empowers upstream and downstream production processes to become more intelligent and capable of self‐optimisation. However, enabling rapid adoption of Industry 4.0 by emerging startups and small to medium‐sized enterprises in the CM industry necessitates a thorough understanding of prerequisites and evaluation of technological and biological limitations. Challenges include the substantial initial costs associated with establishing Industry 4.0 infrastructure, robust cybersecurity measures to ensure effective risk management, and acquiring skilled professionals proficient in both operational and maintenance roles. Integrating Industry 4.0 with the evolving CM sector presents an exciting opportunity to foster business‐to‐business investments across various domains, including local markets, export opportunities, and the broader global consumer ecosystem. [ABSTRACT FROM AUTHOR]

Although the fifth‐generation (5G) is not yet officially launched, researchers worldwide have turned to the sixth‐generation (6G) communications system. The 3G has opened the gap to fourth‐generation (4G). It will be the same for 5G, which will facilitate the path to 6G. The technology 5G provides a high‐level infrastructure enabling various technologies such as autonomous cars, artificial intelligence, drone networking, mobile broadband communication, and, most importantly, the Internet of Things (IoT) and the concept of smart cities. We are, therefore, in the middle of the fourth industrial revolution (Industry 4.0). However, as new technologies gain traction, networks become increasingly complex and difficult to pin down to keep networks operating at the level prescribed by evolving services. The ultimate goal of 6G is to move from the concept of the Internet of intelligent things to the new idea of the intelligent Internet of intelligent things. This article shows the features and tools of 6G technology that will help meet these traffic needs. Besides, we highlight the main feature of the 6G, in terms of architecture and services, scheduled as recommended by the International Telecommunications Union (ITU) in its current technical specifications and discussions on the latest research in this area. [ABSTRACT FROM AUTHOR]

Due to the rising dependency on digital technology, cybersecurity has emerged as a more prominent field of research and application that typically focuses on securing devices, networks, systems, data and other resources from various cyber‐attacks, threats, risks, damages, or unauthorized access. Artificial intelligence (AI), also referred to as a crucial technology of the current Fourth Industrial Revolution (Industry 4.0 or 4IR), could be the key to intelligently dealing with these cyber issues. Various forms of AI methodologies, such as analytical, functional, interactive, textual as well as visual AI can be employed to get the desired cyber solutions according to their computational capabilities. However, the dynamic nature and complexity of real‐world situations and data gathered from various cyber sources make it challenging nowadays to build an effective AI‐based security model. Moreover, defending robustly against adversarial attacks is still an open question in the area. In this article, we provide a comprehensive view on "Cybersecurity Intelligence and Robustness," emphasizing multi‐aspects AI‐based modeling and adversarial learning that could lead to addressing diverse issues in various cyber applications areas such as detecting malware or intrusions, zero‐day attacks, phishing, data breach, cyberbullying and other cybercrimes. Thus the eventual security modeling process could be automated, intelligent, and robust compared to traditional security systems. We also emphasize and draw attention to the future aspects of cybersecurity intelligence and robustness along with the research direction within the context of our study. Overall, our goal is not only to explore AI‐based modeling and pertinent methodologies but also to focus on the resulting model's applicability for securing our digital systems and society. [ABSTRACT FROM AUTHOR]

The current technology epoch—sometimes called the fourth industrial revolution (4IR)—involves the innovative application of rapidly advancing digital technologies such as artificial intelligence. Societal implications of the 4IR are significant and wide‐ranging, from life‐saving drug development to privacy loss and app addiction. A review of the information systems literature, however, reveals a narrow focus on technology‐enabled business benefits. Scant research attention has been paid to the role of humans and humanistic outcomes. To spur new research addressing these issues, formalised affordance theory is employed to develop a new 4IR conceptualization. Four groupings of affordances that capture salient 4IR action possibilities are developed within two categories: machine emulation of human cognition (expansive decision‐making and creativity automation) and machine emulation of human communication (relationship with humans and intermachine teaming). Implications are explored in the context of human‐machine coworking and the development of artificial intelligence safety regulations. Overall, the affordance conceptualization of the 4IR advances a new sociotechnical lexicon of action possibilities and their joint enactment in achieving humanistic and instrumental outcomes, enabling alignment of the scope of 4IR research with the scope of 4IR phenomena—and bringing humans back into the loop. [ABSTRACT FROM AUTHOR]

A growing number of firms are seeking to leverage emerging technologies, such as artificial intelligence (AI) and 3D printing, to enhance their innovation efforts. These seemingly distinct technologies are currently coalescing into an encompassing new technology called the digital twin. This technology allows innovative firms to create a digital replica of a physical entity that evolves over its life cycle. This article explores the implications of the digital twin for innovation theory and practice. First, we examine the connection between the digital twin and three related technologies (i.e., 3D printing, big data, and AI). Second, we create a typology of four categories of digital twins (i.e., monitoring, making, enhancing, and replicating) and illustrate their relevance for innovation management. Third, we offer a set of four case studies that exemplify this typology and illustrate how digital twins have been put into practice. Fourth, we craft a set of digital twin‐related future research directions that encompasses a broad range of innovation‐related topics, including service innovation, co‐creation, and product design. We hope that our examination of the digital twin serves as a catalyst to help advance innovation thought and practice in this intriguing new domain. [ABSTRACT FROM AUTHOR]

The phenomenon of reshoring and its impact on global trade has recently garnered the attention of not only practitioners but also policymakers and scholars. Numerous scholars have highlighted the challenges of reshoring, such as supply chain disruption, labour shortages and sustainability issues, from various perspectives. Some have pinpointed technological advancements as enablers for addressing these challenges through the deployment of cloud‐based artificial intelligence technologies and efficient remote management as part of the Industry 4.0 transformation. As Industry 4.0 shifts from being technology‐driven to value‐driven, this introductory essay for the special issue on reshoring delves into the current boundaries of our understanding and proposes recommendations for future research on this topic. [ABSTRACT FROM AUTHOR]

Summary: The rapidly growing information technologies such as cloud computing, edge computing, artificial intelligence offer cutting‐edge technical means for virtual reality of industrial manufacturing process. An efficient and accurate method for situation awareness (SA) to monitor the process of marginal layer plays an essential role to enhance the safety of industrial equipment. In this study, a manufacturing life cycle is described and characterized as operation and remote controlling of industrial machineries. The edge servers are first employed to track physical devices. Then a novel unified adaptive deep classification framework for SA with semi‐supervised classification method is developed to find unknown patterns in a huge amount of industrial data. Furthermore, a robust model based on the selection of adaptive local neighbor is proposed to find out an optimal weight and high similarity of neighbors. The combination of the proposed framework and model can realize real‐time SA of modern manufacturing the execution system. Plentiful experiments have been conducted to demonstrate the high accuracy and reliability of the proposed framework. [ABSTRACT FROM AUTHOR]

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The pharmaceutical manufacturing sector needs to rapidly evolve to absorb the next wave of disruptive industrial innovations—Industry 4.0. This involves incorporating technologies like artificial intelligence and 3D printing (3DP) to automate and personalize the drug production processes. This study aims to build a formulation and process design (FPD) framework for a pharmaceutical 3DP platform that recommends operating (formulation and process) conditions at which consistent drop printing can be obtained. The platform used in this study is a displacement‐based drop‐on‐demand 3D printer that manufactures dosages by additively depositing the drug formulation as droplets on a substrate. The FPD framework is built in two parts: the first part involves building a machine learning model to simulate the forward problem—predicting printer operation for given operating conditions and the second part seeks to solve and experimentally validate the inverse problem—predicting operating conditions that can yield desired printer operation. [ABSTRACT FROM AUTHOR]

This study evaluated the paired performance of different signal processing techniques and supervised learning models being capable of identifying subtle differences in otherwise similar acoustic signals related to detecting the fiber orientation of a polymer composite. Projection of Latent Structures models demonstrated poor predictive capabilities of the composite structure based on spectral analysis of the acoustic signal. AI based models showed great improvements to the capabilities, with artificial neural network modeling exceeding Convolutional Neural Networks for correct classification accuracies. The continuous wavelet transfer highlighted the greatest degree of differences in the signal response compared with fast Fourier Transformation or short time Fourier transformation. The use of regression‐based predictions over classification‐based was found to greatly improve the predictive capabilities of the models, especially when multiple fiber orientations were present in a sample. A time‐based analysis of spectral data showed the frequencies of the signal changed based on the orientation of the fibers. The acoustic signals for the samples with multiple fiber orientations contained individual artifacts representing components of each individual orientation. Use of the frequency domain was shown as capable of observing the targeted fiber information within the bulk material in real‐time. This work shows great promise for composite material predictions using active ultrasonics, with the potential to be implemented into in‐line systems. [ABSTRACT FROM AUTHOR]

In the Industry 4.0 era, the chemical industry is embracing broad adoption of artificial intelligence (AI) and machine learning (ML) methods. This article provides a holistic view of how the industry is transforming digitally towards AI at scale. First, a historical perspective on how the industry used AI to aid humans in better decision‐making is shown. Then state‐of‐the‐art AI research addressing industrial needs on reliability and safety, process optimization, supply chain, material discovery, and reaction engineering is highlighted. Finally, a vision of the plant of the future is illustrated with critical components of AI‐ready culture, model life cycle management, and renewed role of humans in chemical manufacturing. [ABSTRACT FROM AUTHOR]

Society is facing many challenges, including, climate change, COVID, inequity and human population growth. Some researchers suggest that integration of Circular Economy (CE) and Industry 4.0 (I4.0) concepts and approaches can help us to make progress towards sustainable societies. Integrated implementation can help to improve the design of product–service systems focused on prevention and reduction of wastage of materials, energy, human health, and ecosystems. The CE practices enable consumers to return products after use and to reuse the products with more value. Will integrated adoption of CE and I4.0 practices help society to be more sustainable? What is known about the climate change benefits of integration of I4.0 and CEs to reduce energy and resource usage? The authors sought to answer these questions, via a systematic bibliometric literature review, and network analysis of literature on I4.0 and CE for logistics and supply chain applications. The review was performed by searching the SCOPUS database for literature about I4.0 and CE. A total of 165 articles were shortlisted for in‐depth review. The literature review was complimented by bibliometric and network analyses. The review provided insights into the present and future trends in integration of I4.0 and related Artificial Intelligence (AI) tools in CE's. Based on the findings, a framework for integrating I4.0 and CE, was developed to guide CE decision‐making that will help researchers and industrialists, integrate I4.0 tools within CEs to improve logistics, resource efficiency, safety, product quality and reduce fossil‐carbon footprints. [ABSTRACT FROM AUTHOR]

As part of Industry 4.0, workflows in the process industry are becoming increasingly digitalized. In this context, artificial intelligence (AI) methods are also finding their way into the process development. In this communication, machine learning (ML) algorithms are used to suggest suitable separation units based on simulated process streams. Simulations that have been performed earlier are used as training data and the information is learned by machine learning models implemented in Python. The trained models show good, reliable results and are connected to a process simulator using a.NET framework. For further optimization, a concept for the implementation of user feedback will be assigned. The results will provide the fundamental basis for future AI‐based recommendation systems. [ABSTRACT FROM AUTHOR]

This research contributes to the debate about the relevance of Industry 4.0 technologies in improving environmental performance in the manufacturing industry. We employed a qualitative–quantitative approach in which 19 Italian operations managers were interviewed and 260 managers responded to an online questionnaire. The effects of various technologies were ranked using ordinal regression. Comments and suggestions gave context to the quantitative results. Sensors, radio‐frequency identification, artificial intelligence and analytics were found to be most relevant in improving environmental performance, whereas simulation software contributed moderately. Additive manufacturing, cobots, robots, automated mobile robots and automated guided vehicles had a negative effect, augmented reality had no effect and other technologies indirectly affected environmental performance. We also found a lack of knowledge and application as well as scepticism about technologies such as artificial intelligence and augmented reality. Finally, there was concern about the disposal of electrical and electronic waste produced by these technologies. [ABSTRACT FROM AUTHOR]

While many studies on the Fourth Industrial Revolution (FIR) focus on efficiency, productivity and economic progress, only a few have considered its ethical aspects. This article tries to contribute to filling this vacuum by providing a comprehensive approach to ethical risks at the workplace. Drawing upon Catholic social teaching (CST)-addressed to all people of good will-it analyzes ethical aspects at the workplace posited by the FIR, and more particularly by Industry 4.0, which is at the core of this revolution. CST emphasizes the dignity of the worker and the necessity to flourish at the workplace. While robots, artificial intelligence, and interconnected technologies are only instrumental, the real subject of work is the worker. This casts specific light on the ethical issues analyzed, including effects on employment, wages and inequality, human quality treatment, relational aspects, safety and health, surveillance on employees, and meaningful work. [ABSTRACT FROM AUTHOR]

Artificial intelligence technology in surgery. Artificial intelligence (AI) is one of the disruptive technologies of the fourth Industrial Revolution that is changing our work practices. This technology is in use in highly diverse industries including health care, defence, insurance and e‐commerce. This review focuses on the relevance of AI to surgery. AI will aid surgeons with diagnostic decision‐making, patient selection for surgery as well as improve patient pre‐ and post‐operative care and management. Ethical considerations of AI with respect to patient rights and data privacy are highlighted. A further challenge is how best to present to national regulators a pragmatic way to assess AI as 'software as a medical device'. This relates to the ramifications for the adoption of AI technology in clinical practice, and its subsequent public funding support and reimbursement. It is evident that AI technology has important applications in surgery in the 21st century. The establishment of a key work programme in this area will be important if surgeons are to fully utilize AI in surgery. [ABSTRACT FROM AUTHOR]