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This collection on 'Artificial intelligence, big data, algorithms and Industry 4.0 in firms and clusters' is introduced exploring the themes discussed by the nine papers and grouped into three categories to uncover new dynamics and identify future research opportunities for clusters and organizations in these transformative times. The first group explores theoretical aspects of AI and its evolution in social sciences, focusing on industry 4.0, smart cities, big data, and other related topics. The second group examines the role of industrial robots in employment, productivity, and knowledge absorption in industrial districts. The third group discusses innovation in the context of local production systems, AI ecosystems, and the growth and potential of the Metaverse. [ABSTRACT FROM AUTHOR]

The objective of this paper is to examine the use and applications of reinforcement learning (RL) techniques in the production planning and control (PPC) field addressing the following PPC areas: facility resource planning, capacity planning, purchase and supply management, production scheduling and inventory management. The main RL characteristics, such as method, context, states, actions, reward and highlights, were analysed. The considered number of agents, applications and RL software tools, specifically, programming language, platforms, application programming interfaces and RL frameworks, among others, were identified, and 181 articles were sreviewed. The results showed that RL was applied mainly to production scheduling problems, followed by purchase and supply management. The most revised RL algorithms were model-free and single-agent and were applied to simplified PPC environments. Nevertheless, their results seem to be promising compared to traditional mathematical programming and heuristics/metaheuristics solution methods, and even more so when they incorporate uncertainty or non-linear properties. Finally, RL value-based approaches are the most widely used, specifically Q-learning and its variants and for deep RL, deep Q-networks. In recent years however, the most widely used approach has been the actor-critic method, such as the advantage actor critic, proximal policy optimisation, deep deterministic policy gradient and trust region policy optimisation. [ABSTRACT FROM AUTHOR]

This special issue (SI) aims to allow researchers and practitioners to share the most recent advances in Industry 4.0-related blockchain technologies from enterprise information systems perspectives. According to a recent study, Industry 4.0 and blockchain will significantly impact future enterprise information systems. In the paper entitled 'A novel service level agreement model using blockchain and smart contract for cloud manufacturing in industry 4.0', Tan et al. ([11]) proposed a method to facilitate data security. [Extracted from the article]

Industry 4.0 brings revolutionary changes to farming businesses by integrating emerging technologies such as the Internet of things (IoT), big data analytics (BDA), cloud computing (CC), and artificial intelligence (AI). These Emerging technologies are the potential enablers of data-driven smart farming. Realizing the importance of data-driven agriculture, we provide a complete picture of current literature in smart agriculture by using a review classification framework divided into four categories: (i) Smart Farming Activities, (ii) BDA Levels, (iii) BDA Models, and (iv) BDA Techniques. This work uses the preferred reporting items for systematic reviews (PRISMA) methodology to review the current literature on intelligent farming. A total of 90 papers have been identified, and content analysis was conducted to mine knowledge in the domain for 2011–2022. The primary intention of this review is to clarify the most prominent farming activity, level of analytics, BDA models, and techniques in smart farming. Finally, the findings of our review analysis are discussed, and work suggestions are addressed for further research. [ABSTRACT FROM AUTHOR]

Modern manufacturing is the world's largest and most automated industrial sector. The rise of Industry 4.0 technologies such as Big Data, Internet of Things (IoT) devices, and Machine Learning has enabled a better connection with machines and factory systems. Data harvesting allowed for a more seamless and comprehensive implementation of the knowledge-based decision-making process. New models that provide a competitive edge must be created by combining the Lean paradigm with the new technologies of Industry 4.0. This paper presents novel computer-based vision models for automated detection and classification of damaged packages from intact packages. In high-volume production environments, the package manual inspection process through the human eye consumes inordinate amounts of time poring over physical packages. Our proposed three different computer-based vision approaches detect damaged packages to prevent them from moving to shipping operations that would otherwise incur waste in the form of wasted operating hours, wasted resources and lost customer satisfaction. The proposed approaches were carried out on a data set consisting of package images and achieved high precision, accuracy, and recall values during the training and validation stage, with the resultant trained YOLO v7 model. [ABSTRACT FROM AUTHOR]

This paper reviews the trends in income inequality over the last 40 years, and proposes a new explanation for their evolution – that it is hoped will be tested empirically by many studies in the years ahead – to see whether the observed increases in inequality of the last four decades have been caused by an aggravation of its traditional causes (such as land an human capital concentration), or by a widespread increase in instability in five key areas affecting inequality, the Human Development Index and human capabilities. The five areas where a sharp increase in instability has been observed concern: (a) the financial sector; (b) industry 4.0, especially the development of robotics and artificial intelligence; (c) diseases such as ebola, aids and covid; (d) number of conflicts and (e) a growing environmental crisis. A key unanswered question in the above proposed overall explanation is whether these five crises are interconnected and whether there is a primum movens that explains this series of problematic events. [ABSTRACT FROM AUTHOR]

Artificial Intelligence has been playing a profound role in the global economy, social progress, and people's daily life. With the increasing capabilities and accuracy of AI, the application of AI will have more impacts on manufacturing and service areas in the era of industry 4.0. This study conducts a systematic literature review to study the state-of-the-art on AI in industry 4.0. This paper describes the development of industries and the evolution of AI. This paper also identifies that the development and application of AI will bring not only opportunities but also challenges to industry 4.0. The findings provide a valuable reference for researchers and practitioners through a multi-angle systematic analysis of AI. In the era of industry 4.0, AI system will become an innovative and revolutionary assistance to the whole industry. [ABSTRACT FROM AUTHOR]

Process automation and mass customisation requirements of modern manufacturing systems are driven by artificial intelligence (AI). As AI derives decisions from data, securing the data against tampering is crucial to prevent ensuing operational risks. Additionally, manufacturing systems necessitate collaboration, transparency, and trust among participants while preserving a competitive advantage. Thus, we position blockchain, an enabler of transparent and secure operations, as a security solution for AI-assisted manufacturing systems. In this conceptual viewpoint paper, we present a framework to integrate blockchain in AI-assisted manufacturing systems. We highlight the special needs of manufacturing BCs over generic BCs. We delineate the ways in which manufacturing can be a beneficiary of the synergy between AI and BC. We discuss how BC and AI can accelerate early-phase product design, collaboration, and manufacturing processes and secure supply chains against counterfeit products and for ethical consumerism. Lastly, we identify the needs of modern manufacturing systems and cite a few examples of organisational failures to underscore the importance of security while delineating the significant challenges in adopting blockchain-based solutions in the manufacturing industry. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to contribute to the scientific debate on Quality 4.0 by exploring the main theoretical themes underpinning the Quality 4.0 model and how the model may be developed. An exploratory sequential mixed methods design was employed to study two different samples of Italian manufacturing companies over two phases. For each sample, a different questionnaire was distributed to the companies' quality managers. As a result, eleven themes were elicited and tested. These themes are related to model development, top management, process mapping, data collection and integration with the enterprise resource planning system, use of artificial intelligence software, machine-to-machine data communication, product identification and traceability, document control and digital skills for quality control staff. A theoretical model for Q4.0 is proposed that encapsulates eleven themes of Q4.0 across three categories- people, process, and technology. Results could be particularly helpful for practitioners who may use them as a guideline for implementing and developing Quality 4.0 in a typical Industry 4.0 environment. [ABSTRACT FROM AUTHOR]

This paper proposes an interpretation methodology for the effect of transient process data on quality of injection molded parts. The transient process data measured in the actual processing space have been regarded as the most relevant information to manufacturing processes and product quality. However, its interpretation to pinpoint which feature in the data would affect part quality has traditionally relied on knowledge and understanding of the manufacturing process. The main objective of this method is to reduce the dependency of the transient process data analysis on process knowledge and understanding by using explainable artificial intelligence (XAI). The contribution of the 'section-wise' features in the transient process data to the quality prediction of machine learning (ML) models was investigated for the first time. The interpretation results of the effect of cavity pressure and mold surface temperature on four different quality factors represented reasonable explanations of the characteristics of the polymer materials, product geometry, and molding process. Due to the intermediate relationship of the transient process data with the user-specified process parameters and the resulting quality variables, the interpretation results can be further utilized to optimize the process and provide the optimal transient process data profile for best part quality. [ABSTRACT FROM AUTHOR]

This paper examines the interactions between Industry 4.0 (I4.0) technology, to include artificial intelligence and the Internet of Things, and industry sectors such as manufacturing, healthcare, delivery of consumer goods, and finance. The landscape of I4.0 technologies is reviewed. Interactions are studied through complex network analysis. Industry data from CrunchBase is used to study the application of I4.0 technology in different industries. The network of 543 industry categories as nodes had 1141 relationships. There were 26 key I4.0 nodes that emerged as network hubs. The relative use of I4.0 technology of companies was identified and interconnections between them were measured by network nodes and edges. Temporal analysis was obtained by dividing companies by date of formation. Some of the I4.0 technologies began to appear around 2000. Younger firms were found to have integrated significantly more I4.0 technologies, as expected. The research provides a review of I4.0 technologies by industry that can be used by management to see what competition is doing, and to generate ideas for maintaining or gaining competitive advantage. [ABSTRACT FROM AUTHOR]

This document is a special issue of the International Journal of Production Research focused on collaborative production and maintenance in the context of big data and Industry 4.0. It discusses the impact of the fourth industrial revolution on manufacturing, highlighting the importance of interconnectedness, automation, and data-driven decision-making. The document emphasizes the need for proper analysis of the vast amount of data collected through smart sensors and the potential for collaboration between humans, machines, and data to improve production and maintenance processes. The special issue includes seven selected papers that address various aspects of collaborative production and maintenance, such as fault diagnosis, maintenance scheduling, and maintenance policy evaluation. The papers offer valuable insights, innovative approaches, and practical applications in the field. The editors hope that this special issue will inspire further research and collaboration in the era of big data and Industry 4.0. [Extracted from the article]

The fourth industrial revolution shapes today's private and industrial environments, implying increased digitalization, connectivity, and artificial intelligence. Wearable devices support digital communication by displaying information and monitoring health-related aspects by measuring vital signs. Even though various wearables for measuring vital signs are already used in private life, they have not yet found their way into the production environment. This could be due to poor data quality or a lack of acceptance among employees regarding the usability of wearables during work activities. This paper aims to evaluate the accuracy and usability of selected wearable devices in manual assembly. Therefore, two user studies were conducted in a rebuilt production environment. The first study focuses on the data accuracy of the heart rate measurement of different wearables during manual assembly. In the second study, the usability of the selected wearables is evaluated with the thinking-aloud method during a manual assembly task and questionnaires. [ABSTRACT FROM AUTHOR]

With the emergence of the Industrial Revolution 4.0, modern technologies such as cloud computing, artificial intelligence, and big data are profoundly transforming the education ecosystem. The development of education is not only faced with huge challenges but also contains rare opportunities. New concepts such as deep learning, adaptive learning, and blended learning do not break through the inherent barriers of learning theory, and they also expedite the restoration of the educational technology ecosystem. Based on the ASSURE model, this paper systematically discusses the new development concept of educational technology. Heinich, Russell, and Molenda proposed the ASSURE model in 1989. The "A" stands for Analyze Learner, the first "S" is assumed as Objectives and State Standards; the second "S" for Selecting Media, Strategies, Materials, and Technology; the "U" for Utilizing Materials, Media, and Technology; the "R" stands for Required Learner Participation; and the "E" is assigned for Revise and Evaluate. It is widely used in classroom instruction, online learning, and organizational training. Educational technology must embrace new technology, renew the concept of educational development, shape the new pattern of educational ecology, and better serve the needs of cultivating innovative talents. [ABSTRACT FROM AUTHOR]

In the context of Industry 4.0, integrating Digital Twin (DT) technology stands out as a critical challenge for enhancing manufacturing processes and productivity. The combination of DT and Artificial Intelligence (AI) provides a significant benefit for improving processes in real-time. Industries are actively researching these technologies to keep pace with the rapid evolution of technology, utilizing virtual representations for efficient real-time monitoring and control. The present paper proposes a new approach that relies on DT technology for monitoring, optimizing manufacturing processes and enabling quality control through Deep learning (DL). The proposed methodology involves creating a digital replica of the physical system and utilizing DL models for quality control purposes. This approach improves automation and productivity while maintaining high levels of quality assurance in factories. DL is deployed within the DT for gathering data from the physical system and making predictions regarding product quality. The approach is illustrated by considering an experimental industrial prototype. The results obtained are particularly intriguing, demonstrating heightened predictive accuracy in assessing product quality and real-time issue resolution. Overall, the findings underscore the significant and interesting impact of DT technology with DL on manufacturing processes in the context of Industry 4.0. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to develop an implementation guideline for manufacturing companies integrating ISO 9001 requirements and Industry 4.0 technologies. The implementation guideline follows the Plan-Do-Check-Act model of ISO 9001 standard. We interviewed 25 experts and observed the quality management documentation of seven European manufacturing companies. Results emerged using thematic content analysis and we gained further insights from interviewees, allowing us to propose suggestions garnered from the interviews. The guideline demonstrates how requirements can be integrated with Industry 4.0 technologies such as radiofrequency identification tags, smart sensors, the internet of things, smart human interface, enterprise resource planning modules, artificial intelligence to improve a typical ISO 9001 quality management system. Some issues emerged relating to the fear of losing jobs, validation of quality control equipment based on artificial intelligence, and decision-making and problem-solving processes. The implications of this research are mainly related to the opening of a new debate concerning the integration between ISO 9001 and Industry 4.0 as well as the proposal, to our knowledge, of the first tentative integration. Manufacturing companies can use the guideline for integrating their quality management system with Industry 4.0 technologies while considering also the practical suggestions provided by the experts. [ABSTRACT FROM AUTHOR]

This paper investigates how the twin transition (digital & green) unfolds within firms by relating investments in digital technologies to the propensity of eco-innovating production processes and models. Drawing on a heterogeneous theoretical background, digital technologies can be hypothesised to enable eco-innovation across the board. However, a greater eco-innovation impact is expected from Artificial Intelligence and from bundling digital investments. Using the new Permanent Census of Firms of the Italian National Statistical Office, these hypotheses are tested on a large sample of more than 150,000 firms. Results confirm that the contribution of digital technologies to a firm's eco-innovation is mainly driven by investments in AI application areas, while investments in other digital technologies work more selectively. Moreover, new eco-innovative production processes and models benefit from bundling investments in different digital technologies, but with differences among firms of different size. [ABSTRACT FROM AUTHOR]

In the third industrial revolution (IR), mechatronics engineering has emerged, which is the merger of mechanical and electronics engineering along with information technology to an extent. In the current industrial revolution (fourth), the amalgamation of mechanical with Artificial intelligence (AI) has been used to simulate human behaviour in machines. In the earlier decades, the application of AI in mechanical engineering has expedited and the increased volume of related research papers is a proof of this fact. The broad domain of mechanical engineering has a lot of potential for machine learning applications in order to detect errors, minimise rejection, improve product quality, optimise a system, and so on, as it has not been intensely explored although. This article’s goal is to give a structured evaluation of works that employ vision systems and to attract researchers to analyse its potential integration with machine learning (ML) techniques from the industry 4.0 viewpoint. Current trends and research gaps in the area of manufacturing in collaboration with ML are also discussed at appropriate places. A general procedure to carry out research in the field of mechanical engineering is also presented. The results of various studies highlighted in this paper confirm that the implementation of AI in ME has provided an optimum solution to many mechanical problems that improved product quality and minimised the associated cost. Finally, the authors have led a direction to empower researchers in order to explore various areas in mechanical engineering from Industry 4.0 and AI perspective and enable possible topics for future research. The article concludes by providing challenges that may be faced in the coming future about the use of machine learning in mechanical engineering problems. [ABSTRACT FROM AUTHOR]

Changes in average wage are usually attributed to technological change in the past industrial revolutions. With the emergence and gradual explosion of artificial intelligence (AI) in the 4th industrial revolution, this paper applies panel vector autoregressive technique, with annual data from the World Bank and global economy from 2004 to 2017, to examine the effect of artificial intelligence on average wages in Southern Africa. Findings from the study show that artificial intelligence has a significant negative relationship with average wages but is positively associated with gross domestic product per capita (GDPC), unemployment and inflation. The study also finds inflation and GDPC to be positively associated with average wage. Based on the finding, policy direction focusing on wage stabilisation, redistribution of income, advance learning and skill development training that promote competitiveness to computerisation is recommended. [ABSTRACT FROM AUTHOR]

Bottleneck detection and management plays a significant role in the context of Industry 4.0, wherein process chains have become more intricate. The dynamic nature of process chains shifts the bottleneck location, which requires an integrated methodology capable of identifying current as well as predicting future bottlenecks. The paper proposes a cognitive digital twin (CDT) with a novel explainable artificial intelligence (XAI) model. The proposed CDT is capable of (i) detecting existing bottlenecks, (ii) detecting data anomalies and process chain anomalies (iii) estimating shifting bottlenecks due to anomalies, (iv) predicting near future bottlenecks, and (v) the XAI model supports operational and strategic decision making. The usefulness of proposed CDT is demonstrated and validated experimentally on an industry 4.0 compliant learning factory. The proposed novel CDT effectively addresses the process chain bottlenecks (existing, shifting, and future) while the XAI model enhances transparency and trustworthiness for practical implementation. [ABSTRACT FROM AUTHOR]

Innovation and transformative changes in products, manufacturing technologies, business strategies, and manufacturing paradigms have profoundly changed the manufacturing systems. In addition to being environmentally, economically socially sustainable, manufacturing systems are increasingly using intelligent technologies to be even more resilient, responsive, and adaptable. A new Adaptive Cognitive Manufacturing Systems (ACMS) paradigm, its drivers, enablers, and characteristics, including cognitive adaptation, is presented. Classification and definitions of four types of adaptability in manufacturing systems are included. Human-centric collaboration of workers and intelligent machines and applications, and the future of work in cognitive adaptive manufacturing systems are outlined. Cognitive Digital Twins (CDT), their features, evolution, and their use to support humans in intelligent, collaborative manufacturing settings are discussed. Industrial applications and case studies are used to illustrate the presented concepts and paradigms. Challenges and future research directions to achieve the ACMS paradigm and implement more intelligent, more adaptive, and sustainable manufacturing systems are presented. The presented novel concepts and technologies make significant contributions to the fast-evolving field of manufacturing systems. This pioneering research sheds light on many important future research topics and provides a road map and motivation for researchers in this field. [ABSTRACT FROM AUTHOR]

Close and novel forms of collaboration among supply chain stakeholders within and beyond industry boundaries are essential for implementing a circular economy. Industry 4.0 technologies, as recognised enablers for circular systems, can support such collaboration. This study explores the intersection of the circular economy, supply chain collaboration, and Industry 4.0. First, we derive an analytical framework that contextualises circular supply chain collaboration. Second, based on this framework, we conduct a systematic literature review that explores how Industry 4.0 technologies can enable collaboration mechanisms. The review shows that information sharing and joint planning and decision-making are the most common collaboration mechanisms studied. Simultaneously, the Internet of Things, Blockchain, and Cloud Systems are the most discussed technologies to enable those mechanisms. The most prominent review sample patterns are synthesised into three archetypes for Industry 4.0-enabled circular supply chain collaboration. Furthermore, the study derives nine promising questions for future research along six main dimensions: Digital Twins and Artificial Intelligence, joint knowledge creation and innovation, collaboration in product design and planning, systemic collaboration across industry boundaries, contextualisation with barriers for implementing circular supply chains, and transferability of applications across industries. [ABSTRACT FROM AUTHOR]

Integration of diverse technologies fuels business-model transformation, evolving new applications. Industry 4.0 utilizes emerging-technologies like IoT, AI to make next-generation manufacturing-processes more efficient and agile. Cyber physical systems (CPS) augmented with social, thinking technologies effectuate cyber physical social thinking systems (CPST). Distributed Ledger Technologies (DLT) like Blockchain are synergistic with architectural-elements of such industry-blueprints. Extant literature explores contextual-roles of distinct technology-components as-well-as their limited-combinations; clearly necessitating consideration of integrated-applications. This contemporary conceptual paper: (i) concisely analyses potential-synergies amongst Blockchain, CPST technologies; (ii) proposes novel integrated architecture; (iii) outlines underlying challenges, opportunities and performance considerations; bearing benefits beyond traditional-ecosystems to societal-stakeholders. [ABSTRACT FROM AUTHOR]

Today's global supply chain (SC) is grappling with unprecedented challenges, which rendered conventional SC resilience (SCR) inadequate. In the wake of the Industry 4.0 (I4.0) age, investment in digital SCR (DSCR) relying on I4.0 technologies can potentially enhance organizations abilities to detect, avoid, respond to, and recover from disruptions promptly and efficiently. Literature most focuses on traditional SCR, while DSCR remains incipient. From this, this paper conducts a comprehensive literature review on the SCR, I4.0, and investment (INV) interplays to identify potential research gaps and avenues. It is revealed the integration of SCR-I4.0-INV as a complex and multifaceted process that requires holistic approaches. Some research gaps include the need for empirical studies on DSCR impacts, the role of organizational culture in supporting digital transformation, and investment and resilience trade-offs. This research provides insights for decision-makers and policymakers seeking to develop strategies for promoting resilient SCs in the digital transformation era. [ABSTRACT FROM AUTHOR]

Climate change, the growth in world population, high levels of food waste and food loss, and the risk of new disease or pandemic outbreaks are examples of the many challenges that threaten future food sustainability and the security of the planet and urgently need to be addressed. The fourth industrial revolution, or Industry 4.0, has been gaining momentum since 2015, being a significant driver for sustainable development and a successful catalyst to tackle critical global challenges. This review paper summarizes the most relevant food Industry 4.0 technologies including, among others, digital technologies (e.g., artificial intelligence, big data analytics, Internet of Things, and blockchain) and other technological advances (e.g., smart sensors, robotics, digital twins, and cyber-physical systems). Moreover, insights into the new food trends (such as 3D printed foods) that have emerged as a result of the Industry 4.0 technological revolution will also be discussed in Part II of this work. The Industry 4.0 technologies have significantly modified the food industry and led to substantial consequences for the environment, economics, and human health. Despite the importance of each of the technologies mentioned above, ground-breaking sustainable solutions could only emerge by combining many technologies simultaneously. The Food Industry 4.0 era has been characterized by new challenges, opportunities, and trends that have reshaped current strategies and prospects for food production and consumption patterns, paving the way for the move toward Industry 5.0. [ABSTRACT FROM AUTHOR]

As globalisation and competition grow rapidly, adopting a flexible manufacturing philosophy becomes more critical for manufacturers. Cellular Manufacturing System (CMS) is among one of the most favourable ones which has been deeply studied in academia since this philosophy was coined in 1925. To design the manufacturing system efficiently and effectively, the management team needs to have a comprehensive representation of the definitions, classifications, solution approach to cellular manufacturing design problems. In this study, exact methods, heuristic approaches, metaheuristic techniques, and artificial intelligence strategies for cellular manufacturing design are reviewed. A comparison of solution approaches is discussed and based on this analysis, some suggestions for future research are proposed. Moreover, we tried to highlight the importance of investigating sustainability decisions related to CMS problems, and integration of supply chain decisions with CMS decisions which have not been sufficiently studied to identify a research direction for researchers to model and analyse those issues. Finally, we briefly discuss the placement of CMS as one of the major production systems in the industry 4.0 evolution paradigm. Reviewing the adoption of industry 4.0 techniques by CMS sheds light on the contribution of methodological approaches in transition to intelligent CMS. [ABSTRACT FROM AUTHOR]

Lean is a philosophy that helps organizations develop a continuous improvement culture by eliminating waste and minimizing the use of resources. On the other hand, Industry 4.0 is a complex high-tech model that contributes to flexible organizations using different technologies such as IoT, cloud computing, blockchain, virtual reality, and Artificial Intelligence. The concept of integrating Lean Principles and Industry 4.0 is still very recent and limited, and the literature lacks a bibliometric perspective. The integration is challenging in terms of contentment-wise and is a broad multi-dimensional research area. This study aims to analyze Lean Industry 4.0 or Lean 4.0 and Lean digital transformation literature using bibliometrics, identifying its state of art, benefits, challenges, and scientific gaps. In this study, articles published between 2010 and 2022 from the Scopus database are analyzed to identify significant publications, journals, authors, institutions, and countries based on the number of publications and frequently used keywords and subject areas. This study contributes to developing the state of art of Lean Industry 4.0 and Lean digital transformation and helps researchers and practitioners direct their future studies. The literature performance is measured based on both the number of publications and the number of citations; this bibliometric analysis covers most of the published papers in both research domains. [ABSTRACT FROM AUTHOR]

The increasing level of automation in the fourth industrial revolution has received a global interest in research and political landscape in recent years. While some research advances have been made in the developed world with regard to its implications for unemployment, next to no study has so far sought to establish if there is any statistical relationship between automation and unemployment in developing countries where unemployment is very high. The purpose of this study is to examine the effect of automation on unemployment in Southern Africa. Findings from the study show that automation has a significant positive relationship with unemployment rate. The study also finds foreign direct investment and business cycle (higher production output change) to be negatively associated with unemployment. Based on these findings, it is recommended that proper investment in learning and skill development training aimed at making individuals more competitive with regard to automation be pursued to increase the prospect of employment. [ABSTRACT FROM AUTHOR]

I demonstrate that China is building an Augmented National innovation system or ANIS, with semiconductor industry and AI at the core. There are two dimensions along which China's NIS has been augmented. One is to move toward a more egalitarian innovation system in accordance with the goal of creating a harmonious, moderately prosperous economy and society. And the other is to include the AI and semiconductor base for high technology for the 4th Industrial Revolution. The Chinese ANIS has important regional and geoeconomic implications for the future. [ABSTRACT FROM AUTHOR]

The implementation of artificial intelligence and 4.0 technologies helps business leaders make better decisions to reduce costs, improve team performance, and increase productivity. It also enhances the quality of work life of workers, allows the optimization of the information obtained from the equipment in real time, in this way even unscheduled maintenance can be avoided. A systematic review of the literature regarding the applications and benefits that artificial intelligence has given to companies is presented, the information was obtained from databases such as Ebsco, Scopus, MDPI, ScienceDirect. Analyses, concepts, and contributions that have benefited the understanding of the need to embrace new technologies 4.0 and artificial intelligence are presented, for the benefit of companies and workers. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) empowers traditional firms to transform into Industry 4.0, enabling them to compete in an era of rapid technological advancements. However, AI adoption remains limited among Canadian firms. This research aims to identify the key dimensions of AI-driven digital transformation (AIDT) and develop a grounded theory that provides a rich and nuanced understanding of how the AIDT process unfolds within Canadian SMEs. The study reveals that the AIDT process is shaped by the interplay of five core dimensions: evaluating transformation context, auditing organisational readiness, piloting the AI integration, scaling the implementation, and leading the transformation. The first four dimensions follow a sequential, stage-like progression, while the fifth dimension is recurring and omnipresent, exerting a continuous impact on the other phases. AIDT is characterised as a path-dependent, slow evolutionary change spectrum that demands firms adapt by developing their sensing, seizing and reconfiguration capacities to evolve and sustain their evolutionary fitness. The study explores several theoretical and managerial implications that arise from the findings. [ABSTRACT FROM AUTHOR]

Maintaining high-quality standards has consistently been the main goal of industries. With rising demand and customisation, industries must strike a balance between cost, manufacturing time, and quality. The technological advancements of Industry 4.0 have allowed the implementation of accurate quality prediction frameworks in the manufacturing lines. For quality prediction in manufacturing, machine learning, and artificial intelligence offer several benefits, but there are also a number of limitations that must be taken into consideration. The current study aims to highlight the aforementioned benefits and drawbacks. To do this, a literature review on the area of quality prediction and monitoring in Industry 4.0 manufacturing lines is conducted. The results demonstrate that the merits of the reviewed methods are many but six significant drawbacks must be accounted for the successful implementation of the studied quality prediction frameworks. The current study can serve as a 'map' for production managers in industries as well as experts in the field of manufacturing as they weigh the benefits and drawbacks of popular quality prediction models, as it provides information needed to determine to what extent these methods can be applied to new or existing manufacturing lines. [ABSTRACT FROM AUTHOR]

The Fourth Industrial Revolution (4IR) looms large on the horizon. It is characterised by the fusion of cyber, physical, and human realms, with the heart of it powered by artificial general intelligence (AGI). This revolution brings profound economic, societal, and ethical implications for humanity. Addressing its intricate challenges necessitates a shift toward higher-order and higher-dimensional empathy and creativity for humanity-centred design innovations. Traditional design thinking and methodologies, while once effective, now fall short amidst the complexities of the 4IR. Future designers must adeptly wield creative artificial intelligence for humanity-centred design and execute robust entrepreneurship and leadership to ensure a human-led design process towards the future. [ABSTRACT FROM AUTHOR]

This study addresses the emerging concept of Industry 5.0, which aims to tackle societal concerns associated with the ongoing digital industrial transformation. However, there is still a lack of consensus on the definition and scope of Industry 5.0, as well as limited understanding of its technological components, design principles, and intended values. To bridge these knowledge gaps, the study conducts a content-centric review of relevant literature and synthesizes evidence to develop an architectural design for Industry 5.0. The findings reveal that Industry 5.0 represents the future of industrial transformation, offering potential solutions to socio-economic and environmental issues that were inadequately addressed or exacerbated by Industry 4.0. The study provides managers, industrialists, and policymakers with a comprehensive overview of Industry 5.0, including its technological constituents, design principles, and smart components, emphasizing the importance of stakeholder involvement and integration for effective governance of digital industrial transformation within this framework. [ABSTRACT FROM AUTHOR]

An emerging field of scholarship in Artificial Intelligence (AI) and computing posits that AI has the potential to significantly alter political and economic landscapes within states by reconfiguring labor markets, economies, and political alliances, leading to possible societal disruptions. Thus, this study examines the potential destabilizing economic and political effects AI technology can have on societies and the resulting implications for domestic conflict based on research within the fields of political science, sociology, economics, and artificial intelligence. In addition, we conduct interviews with 10 international AI experts from think tanks, academia, multinational technology companies, the military, and cyber to assess the possible disruptive effects of AI and how they can affect domestic conflict. Lastly, the study offers steps governments can take to mitigate the potentially destabilizing effects of AI technology to reduce the likelihood of civil conflict and domestic terrorism within states. [ABSTRACT FROM AUTHOR]

The information and communication technology (ICT) field is rapidly advancing and has developed several disrupting technologies, including artificial intelligence, virtual reality, big data, cloud computing solutions, and the Internet of Things (IoT). This new automation is pervasive throughout production or manufacturing. It allows the integration of the next generation commuting system popularly called Cyber-Physical Systems (CPS) to join the physical and simulated worlds, marking the arrival of industry 4.0, commonly referred to as a fourth industrial revolution. The term "Industry 4.0" refers to a wide range of technologies applied in the manufacturing industry, encompassing everything from product design to Supply Chain Management (SCM). To contribute to the execution of research in industry 4.0, the current study investigates the specific application of fourth industrial revolution technologies in smart manufacturing systems. First, a theoretical outline of various applications of Industry 4.0 is presented for smart manufacturing systems. Second, CPS-enabled smart (design, control, monitoring, and machining) is introduced through distinct illustrative scenarios. Third, based on these illustrative scenarios, new advancements and their potential applicability through Industry 4.0 in manufacturing industries are discussed. Finally, various challenges and prospects are recognized for Industry 4.0 and explored to assist manufacturing sectors in achieving further growth. [ABSTRACT FROM AUTHOR]

Quality is considered an important aspect of any organization, and due to the influence of technology, the competition in the international market is increasing day by day. The industry 4.0 paradigm is transforming organizations into intelligent systems and raising the concepts in the supply chain such as Logistics 4.0, Procurement 4.0. Consequently, there is a need for an organized process to replace the legacy quality control process with quality 4.0 in the present scenario. This study relies on previous research, studies, and organization expert's opinions to jointly propose a revised quality control procedure based on a mix of numerous Industry 4.0 technologies such as radio frequency identification devices, sensors, internet of things, autonomous robots, smartphone, virtual reality, artificial intelligence, and many more. A re-designed quality control process is established, and its benefits are addressed. The proposed redesign can significantly improve price, time, traceability, automation, human effort, information availability, and ambiguity. The proposed re-engineering method discusses factors involved in quality control and the impact of Industry 4.0 technologies on different activities of the quality process. The recommended procedure is based on business process reengineering, which helps to improve the traditional method and anticipate the new approach. [ABSTRACT FROM AUTHOR]

In recent years, membrane technologies are widely utilized in water and wastewater treatment processes. However, controlling and improving these systems still need to be investigated and, therefore, are attracting increasing amounts of attention from researchers worldwide. Industry 4.0 has increased in importance over the past few years, and artificial intelligence (AI) technology has demonstrated its strength in supporting decision-making in various fields, including environmental systems and especially membrane processes. AI allows for cost-effective operation of systems, including better planning and tracking as well as comprehensive understanding of resource-loss in realtime, then maximizing revenue capture and water quality satisfaction. This study therefore aims to provide a comprehensive review of the current application of AI-based tools in simulating membrane processes as well as the feasibility of applying these models to other fields in which membranes are to be used in the future. The existing conventional mathematical models are illustrated along with their advantages and shortcomings. The definition and classification of state-of-the-art AI models, as well as the benefits of these over conventional models, are also discussed. Furthermore, the basic principle of membrane processes and current application of AI-based technologies in simulating the performance of these membrane systems are systematically reviewed. Finally, the implications and recommendations for future studies are discussed. [ABSTRACT FROM AUTHOR]

Machine Vision enhanced by Artificial Intelligence can improve product and process monitoring through inspection. However, applying these solutions to the industrial environment requires a transdisciplinary effort and requisites interoperability. This study proposes a road map to reduce implementation uncertainties. A systematic review of the literature was conducted, considering Machine Vision applications with Artificial Intelligence techniques for Industrial Inspection. Natural Language Processing was used to select the potential documents, by narrowing down those that were not aligned with the desired topic. The remaining documents were analyzed and compared with the proposed road map. This allowed the validation of the proposed concepts confirming their applicability. The results show that the road map is valid for current industrial inspections using Artificial Intelligence and Machine vision providing the support for the development of smarter machines into Industry 4.0 functionalities. [ABSTRACT FROM AUTHOR]

Advanced Construction Robot System is an important and promising application domain in robotics. Such social needs are also accelerating the importance of the advanced construction robot system. Advanced Construction Robot System requires various technologies for improving autonomy, safety, and work efficiency. [Extracted from the article]

The technological choices facing the manufacturing industry are vast and complex as the industry contemplates the increasing levels of digitization and automation in readiness for the modern competitive age. These changes broadly categorized as Industry 4.0, offer significant transformation challenges and opportunities, impacting a multitude of operational aspects of manufacturing organizations. As manufacturers seek to deliver increased levels of productivity and adaptation by innovating many aspects of their business and operational processes, significant challenges and barriers remain. The roadmap towards Industry 4.0 is complex and multifaceted, as manufacturers seek to transition towards new and emerging technologies, whilst retaining operational effectiveness and a sustainability focus. This study approaches many of these significant themes by presenting a critical evaluation of the core topics impacting the next generation of manufacturers, challenges and key barriers to implementation. These factors are further evaluated via the presentation of a new Industry 4.0 framework and alignment of I4.0 themes with the UN Sustainability Goals. [ABSTRACT FROM AUTHOR]

This study supplies contributions to the existing literature with a state-of-the-art bibliometric review of sustainable industrial and operation engineering as the field moves toward Industry 4.0, and guidance for future studies and practical achievements. Although industrial and operation engineering is being promoted forward to sustainability, the systematization of the knowledge that forms firms' manufacturing and operations and encompasses their wide concepts and abundant complementary elements is still absent. This study aims to analyze contemporary sustainable industrial and operations engineering in Industry 4.0 context. The bibliometric analysis and fuzzy Delphi method are proposed. Resulting in a total of 30 indicators that are criticized and clustered into eight study groups, including lean manufacturing in Industry 4.0, cyber-physical production system, big data-driven and smart communications, safety and security, artificial intelligence for sustainability, the circular economy in a digital environment, business intelligence and virtual reality, and environmental sustainability. [ABSTRACT FROM AUTHOR]

In the last decade, a rising number of opportunities for IT-based solutions in manufacturing have emerged. Often these opportunities are linked to terms like Industry 4.0, Industrial Internet of Things, Cyber-Physical Production Systems or Artificial Intelligence in Manufacturing and address different fields of action to achieve improvements in manufacturing. These include increased productivity, improved energy and resource efficiency, predictive maintenance or quality management support. However, current implementations of corresponding improvement measures often lack a common structure of the underlying IT systems, so that each of the measures is often planned and implemented as a stand-alone solution. Possible consequences include redundancies within the IT infrastructure and software functions as well as high efforts for later extensions. Against this background, an approach for a system architecture to implement different IT-based support measures based on a shared versatile system has been developed in order to gain synergy potentials and foster the reuse of software functions. Furthermore, an approach for the planning and implementation is given. This includes the design of the system architecture as well as the conception and implementation of the IT-based improvement measures. An exemplary application case within a factory for pneumatic valves demonstrates the applicability of the approaches. [ABSTRACT FROM AUTHOR]