Search

Before and aftermath of COVID-19 has created multiple waves of digital transformation adoption in supply chain management. The power of potential benefits is realised as a multitude however the transformation phases are not sustainable. This editorial special issue has edited papers of scholarly research contributions to the theory and practice of digital transformation aspects in the field of logistics and supply chain management. Based on the summary of each contribution and an extended literature review on the theme, this editorial paper proposes a novel framework that could facilitate the logistics industry at times of disruptive technologies when firms are struggling to cope with the challenges created due to the COVID-19 pandemic and the recovery. [ABSTRACT FROM AUTHOR]

This paper aims to address in-depth the state of the art of current literature relating to digital supply chain management. A set of 100 papers deduced from the most relevant scientific databases from 2005 to 2020 has been analyzed and synthesised. The analysis and evaluation of these papers have enabled identifying the difference between traditional and digital supply chains, the emerging technologies' impact on supply chain performance, and the current state of digital supply chain management using the SWOT matrix. Besides, it provides a holonic roadmap framework for future study. This work's originality lies in the holonic architecture considering the digital supply chain's hierarchy multiscale and multilevel (business strategies). This holonic architecture supports supply chain performance under the industry 4.0 ecosystem by considering strategic, tactical, and operational strategies. Therefore, the developed framework helps to enable the necessary transition towards the achievement of digital supply chain performance based on the holonic architecture. The proposed study is beneficial for both practitioners and academics as it describes the critical aspects of the supply chain transformation and represents a solid background that outlines the necessity of incorporating a strategical viewpoint for digital supply chains adoption. [ABSTRACT FROM AUTHOR]

In recent years, the way that maintenance is carried out has evolved due to the incorporation of digital tools and Industry 4.0 concepts. By connecting to and communicating with their production system, companies can now gather information about the current and future health of the equipment, enabling more efficient control through a process called predictive maintenance (PdM). The goal of PdM is to reduce unplanned downtimes and proactively address maintenance needs before failures occur. However, it can be challenging for industrial practitioners to implement an intelligent maintenance system that effectively manages data. This paper presents a methodology for developing and implementing a PdM system in the automotive industry, using open standards and scalable data management capabilities. The platform is validated through the presentation of two industry use cases. [ABSTRACT FROM AUTHOR]

This review examines which Industry 4.0 (I4.0) technologies are suitable for improving Lean Six Sigma (LSS) tasks and the benefits of integrating these technologies into improvement projects. Also, it explores existing integration frameworks and discusses their relevance. A quantitative analysis of 692 papers and an in-depth analysis of 41 papers revealed that 'Analyze' is by far the best-supported DMAICs phase through techniques, such as Data Mining, Machine Learning, Big Data Analytics, Internet of Things, and Process Mining. This paper also proposes a DMAIC 4.0 framework based on multiple technologies. The mapping of I4.0 related techniques to DMAIC phases and tools is a novelty compared to previous studies regarding the diversity of digital technologies applied. LSS practitioners facing the challenges of increasing complexity and data volumes can benefit from understanding how I4.0 technology can support their DMAIC projects and which of the suggested approaches they can adopt for their context. [ABSTRACT FROM AUTHOR]

A supply and demand mismatch, or imbalance of the amount of supplies in the market, is always an issue and can happen all the time. Capacity sharing is an effective way to address this problem, and the capacity sharing platform facilitates the optimal matching between multiple capacity buyers and sellers. In the context of Industry 4.0, many industries are adopting intelligent algorithms to assist in decision-making. This paper presents an optimal or near-optimal matching algorithm to cope with a large volume of capacity-sharing problems. The fairness of the matching solution is captured by including three objectives from platform, sellers and buyers. In this paper, a 2-dimensional crossover and an order-first mutation are developed and employed with genetic algorithms (GA), including GA and NSGA-II. Additionally, a novel repair mechanism is proposed by considering various constraints to transform infeasible solutions into feasible ones. Two matching schemes are studied based on whether orders from buyers can be split or not. The results show that both algorithms based on traditional GA and NSGA-II are effective for different schemes. In addition, it is found that GA has better performance in the case of 'more sellers' and NSGA-II shows better performance in the 'more buyers' case. [ABSTRACT FROM AUTHOR]

Digital Twins (DTs) hold significant promise in addressing the challenges faced by food supply chains (FSCs). This paper aims to provide critical insights into the potential for Digital Twins to meet the key challenges of the FSC and establish a comprehensive conceptual framework for their implementation. Following a systematic literature review (SLR), the study identified 81 peer-reviewed, high-quality papers published over the last decade (2012–2023). The typology-driven thematic analysis emphasises the emergent nature of DTs within FSCs, highlighting their key characteristics including monitoring, real-time simulation, and scenario analysis. The identified characteristics, applications, implementation drivers and barriers of Digital Twin form the basis for a novel conceptual framework for implementing DTs in FSCs. Leveraging insights from Innovation Adoption Theory and the Technology-Organization-Environment (TOE) framework, the study outlines a structured five step implementation process divided into three stages. Notably, technology assessment and performance evaluation emerge as two innovative steps necessary for the successful implementation of DTs specifically, not previously considered by the theory. The study identifies promising avenues for future research. These findings provide invaluable guidance to researchers and practitioners seeking to embrace the potential of Digital Twin within the food industry. [ABSTRACT FROM AUTHOR]

Technological transformations based on 4.0 technologies are a reality. Despite this, there are few studies on their growth-enhancing role at regional level. This paper aims to fill this gap. By conceptually unpacking the two main technological transformations taking place – Industry 4.0 and the digital service economy – and identifying empirically the regions where they prevail, the paper examines whether the two transformations, despite of their profound different nature, are both conducive to regional growth in the period 2007–2018. Empirical results interestingly show that Industry 4.0 and automation technology adoption are associated with regional economic growth especially in those regions where such specific transformation prevails; differently, the effects from digitalisation spread all over European regions and regions where the digital service economy transformation prevails do not enjoy significant growth advantages with respect to others. Targeted regional policies are therefore called for by each transformation. [ABSTRACT FROM AUTHOR]

This paper offers an in-depth examination of the ergonomics of human-centred assembly systems in Industry 4.0, where manual tasks remain essential. The use of advanced technologies such as motion capture (MOCAP) and virtual reality (VR) is analysed as ways to enhance system efficiency and improve worker well-being. The paper highlights the importance of optimising assembly system performance while considering both economic and human factors. Metrics to assess ergonomic risk and productivity are discussed based on human-centred technologies, and existing operational research models are explored to analyse how human factors could be considered in optimising system performance. Additionally, the paper explores potential future directions and how they could play a role in Industry 4.0. [ABSTRACT FROM AUTHOR]

In this study, we develop a new paradigm, Circular Lean Six Sigma 4.0 (CLSS4.0) to promote manufacturing sustainability. This paper aims to provide a practical and holistic view of the drivers and barriers that can help companies design an integrated CLSS4.0 model. The paper is based on a qualitative exploratory study using multiple case studies within 12 Moroccan manufacturing firms conducted through semi-structured interviews with top executive managers. The results show that the drivers are related to expected operational and environmental performance, increasing customer requirements, gaining competitive advantage and market growth while barriers are related to insufficient tangible (finance, human and equipment) and intangible (skills and techniques) resources, data privacy, technical issues and management support. The proposed framework identifies the assessment of drivers and barriers and their attributes as a starting point for managers to lead the CLSS4.0 transformation, thereby contributing to its successful implementation. To the best of our knowledge, this study is among the very first studies to discuss the CLSS4.0 drivers and barriers. It could be useful to managers as a diagnostic tool to assess their ability to implement CLSS4.0 before investing in the initiative. [ABSTRACT FROM AUTHOR]

Rapid advances in new-generation information technologies have been the main driving force for the transformation of manufacturing enterprises in Industry 4.0. Digital twin (DT), as a key technology to promote intelligent manufacturing, has shown great potential for manufacturing enterprises to create an industrial intelligence-driven production equipment through in-depth integration of cyber-physical systems. However, the lack of a systematic effective DT modelling method with a supporting evaluation metric is the most important factor restricting the application of DT in manufacturing enterprises. To bridge the gap, this paper proposes a novel multi-level modelling and fidelity evaluation (MLM&FE) method of DT for creating smart production equipment in manufacturing enterprises, which could help enterprises establish an industrial intelligence-driven production environment to quickly respond to changes in the customised global market, thus greatly improving competitiveness of the enterprises. Specifically, this paper firstly designs a reference framework for DT-enhanced smart production equipment, on which an MLM&FE architecture is proposed. Then, key implementation methodologies and tools for MLM&FE are introduced from the perspective of data space modelling, virtual space modelling, knowledge space modelling, model integration and evaluation. Finally, the developed smart production equipment prototype demonstrates the feasibility and effectiveness of DT MLM&FE. [ABSTRACT FROM AUTHOR]

This paper analyzes how Industry 4.0 transforms supply chain collaboration and design. Therefore, we identify and analyze 151 academic articles. The study uncovers holistic strategic changes Industry 4.0 and associated technologies imply for cross-company collaboration in supply chains. These include changes in the initiation of buyer-supplier transactions, information exchange and handling, and how Industry 4.0 transforms relationships and cooperation. Subsequently, implications for aspects concerning supply chain design, such as ecosystem development are given. The systematic literature review is the first to analyze the body of literature on supply chains in Industry 4.0 to synthesise how cross-company collaboration is transformed in detail. The paper provides a comprehensive overview of the current state of research and develops several suggestions for future research and managerial practice, such as the role of risk-benefit sharing and information sharing, allowing to develop new forms of collaborative value creation in Industry 4.0. [ABSTRACT FROM AUTHOR]

In the context of Industry 4.0, many production scenarios utilise sensors to monitor manufacturing processes, resulting in massive data that can be leveraged to build machine-learning models to improve production efficiency and quality. In semiconductor manufacturing, where many sensors are employed to monitor the production process, the resulting multi-sensor data poses challenges for constructing a virtual metrology (VM) model to assess wafer quality. Furthermore, building separate prediction models for each sensor can result in a loss of redundancy present in the multi-sensor data. Therefore, this paper proposes a multi-source AdaBoost with a cross-weight sampling method for predicting the critical dimension required in VM. Compared with the existing AdaBoost algorithm in regression, the proposed method adapts well to multi-source data by incorporating a cross-weight sampling distribution. Thus, when updating the sampling weight distribution, the impact of both individual and multi-source data is considered to re-sample high-quality observations. Based on the numerical results from the synthetic datasets and case study in semiconductor manufacturing, the proposed method outperforms benchmark methods. Additionally, owing to the redundancy of multi-sensor data, the proposed method demonstrates robust performance regardless of the noise level in the semiconductor VM data. [ABSTRACT FROM AUTHOR]

Validated quantitative models for lean supply chain planning (LSCP) are still scarce in the literature, particularly because conventional push systems have not been widely integrated and tested with pull systems in sustainable and resilient environments in the Industry 4.0 context. Hence the main contribution of this paper is to develop an optimisation model that is able to contribute to the LSCP with the combination of push and pull strategies. Here we present an integrated just-in-time (JIT) production system with material requirement planning (MRP) for a SC that takes a traditional five-level structure based on a mixed-integer linear programming model (MILP) dubbed as LSCP 4.0. The model is able to simultaneously plan the production and inventory of materials and finished goods to satisfy demand from forecasts and firm orders. The selection of alternative suppliers as a proactive measure to face disruptive events is also considered. Furthermore, sustainable practices are included in the objective function for profit maximisation by considering CO2 emissions. This proposal is tested in the footwear sector. The results demonstrate that the combined use of JIT and MRP through a quantitative approach improve performance in leanness, sustainability and resilience by decreasing the bullwhip effect at different SC levels. [ABSTRACT FROM AUTHOR]

Blockchain is an emerging technology with the potential to radically transform logistics in the highly complex environment of the agri-food supply chain. The key challenges of the agri-food sector include overcoming the limitations (time, cost and quality) to developing global and efficient transparency and traceability of food, eliminating expensive intermediaries, and ensuring no disruption between suppliers and retailers. This paper describes a practical approach by not only using smart contracts to integrate various stakeholders but also incorporating a Public Participation Geographic Information System platform to ensure traceability of the operations conducted in an agri-food supply chain. As a factor in improving distribution and minimising intermediaries, third-party logistics participation is included in this research. Through a case study, the entities and interactions between processes within a blockchain system based on a smart contract are identified. The findings reveal an innovative blockchain approach to logistics in the agri-food supply chain. [ABSTRACT FROM AUTHOR]

The Fintech and Industry 4.0 paradigms, and the combination of the two, have gained increasing attention from the scientific community in recent years. This paper aims to explore the convergence of these two research themes by framing their interplay through a systematic literature review covering the period of 2017 to August 2021 and involving 78 papers. The bibliometric approach made it possible to extract a large amount of useful information which was previously scattered and to present it in a systematic way. The analysis of the co-occurrence network of keywords identified four distinct clusters, corresponding to the dominant research themes in the area, which suggested the design of a research agenda. The agenda-driven questions indicated which current aspects of Fintech and Industry 4.0 need additional insight. In that regard, the environmental and social responsibility aspects of the firms in the sectors were found of particular importance. Theoretical and practical implications of this study as well as answers provided to a set of initially posed research questions were also discussed. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the relationship between organizational culture (OC) profiles and the adoption of Industry 4.0 (I4.0) design principles. We surveyed 153 middle and senior managers from Brazil and India, whose manufacturing companies have been implementing I4.0. Participants provided answers regarding the perceived OC characteristics that prevail in their companies, which were assessed based on the Competing Values Framework. We also collected data on the adoption levels of I4.0 design principles, whose analysis was conducted through the utilization of multivariate data analysis techniques. Our results indicated that, depending on the I4.0 design principle, OC profiles (Clan, Adhocracy, Market and Hierarchy) may either corroborate or impair its adoption, allowing the verification of the hypothesized relationships. The understanding of the association between OC profiles and I4.0 design principles provides practitioners with arguments to identify possible problems during their digital transformation. As changes in OC require significant efforts and are usually time-consuming, companies that can anticipate those issues might face a smoother transition towards the Fourth Industrial Revolution, hence, obtaining competitive advantages. [ABSTRACT FROM AUTHOR]

In the context of great changes in the food industry, the aim of this research is to investigate if and how the implementation of 4.0 enabling technologies can enhance the economic, environmental and social sustainability of the food sector. A systematic literature review, using a combination of 12 keywords, was carried out on Scopus database with defined inclusion and exclusion criteria in order to answer four selected research questions. Overall, 50 relevant papers were retrieved and analysed by Mendeley and Excel with descriptive statistics. VOSviewer was used for co-occurrence and co-authorship analysis. Results illustrate that the interest in the topic has grown, in particular in Italy, and resume the benefits achievable by the implementation of 4.0 technologies in food industries. Social impacts are new job positions, ergonomic design of workplaces, changes in educational institutions, improved nutrition and better animal welfare. Positive aspects are related even to economic growth, improving food chain performances and decreasing companies' costs. Finally, it allows energy, water, CO2 emissions and food savings. Overall, the work provides a helpful overview to food manufacturers and producers, recommending the introduction of I4.0 technologies to positively influence the sustainable development of the sector and remain competitive in the market. [ABSTRACT FROM AUTHOR]

Demand shocks and fluctuations underscore the need for new approaches to coordinate collaboration between firms to scale up production. This paper proposes an approach to formalise product and process requirements via a collaboration ontology and applies semantic reasoning techniques for process formation. Our approach contributes to production research by providing flexibility in coordinating firms engaged in demand-driven collaboration. The proposed approach has four core dimensions: (1) The Collaboration ontology builds on a set of product assembly requirements, process steps, their input/output resources and semantic rules; (2) the ontology reasoner derives resource dependencies between the steps; (3) the java tool interprets resource dependencies as possible transitions in Business Process Management Notation (BPMN); (4) a workflow engine executes the generated product assembly process. The approach and the ontology were validated in an industrial aerospace tendering scenario demonstrating its practical relevance for firms seeking demand-driven collaborations to react to production changes. Finally, we position and explain our contributions to the body of knowledge in collaborative production engineering. [ABSTRACT FROM AUTHOR]

Following the recent advances in Industry 4.0 and the upcoming Industry 5.0, the use of multiple UAVs for indoor tasks has risen, particularly in real-time remote monitoring, wireless coverage, and remote sensing. As a result, UAVs can be viewed as proactive problem solvers and can support Internet of Things (IoT) platforms by collecting and monitoring data cost-effectively and efficiently, leading to better decision-making. Moreover, sophisticated drone operations require specialised software and data processing abilities. However, the utilisation of drones has been mainly focused on outdoor environments, thus creating a literature gap regarding indoor navigation and operation. Therefore, the design and development of a method for remote planning and control of drones based on the utilisation of AR is presented in this paper. The proposed method is based on the utilisation of drones for remote monitoring. The suggested approach involves engineers designing a sequence of actions and transmitting them wirelessly to the drone, eliminating the need for human intervention. Thus, the proposed method contributes towards enabling engineers visualise the drone path with the use of Augmented Reality and provides the flexibility of adding multiple way points. The applicability of the developed framework is tested in a laboratory-based machine shop. [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]

Industries are currently struggling with ageing workforce in modern production systems associated with Industry 4.0. The industrial socio-demographic problem is more and more present as the increasing of the ageing population results in the prolongation of the working life and the consecutively in the ageing of the workforce in industries. This paper aims to conduct a systematic literature review on the challenges and concerns of ageing operators, including the physical, cognitive, ergonomic, and well-being conditions of the ageing workforce in the Industry 4.0 environment. The ScienceDirect, Scopus, Web of Science and PubMed scientific databases were used to survey the studies and selected using PRISMA guidelines. This paper was structured and analysed by clusters: Ageing, Industry 4.0, Human Factors, and Ergonomics. These clusters were developed as research lines: Ageing as the socio-demographic challenge, Industry 4.0 as the technological development, Human Factors as the individual characteristics of the operator, and Ergonomics as the work environment. Thus, human--centric approaches and ideas are discussed with the insights and issues of Industry 4.0 technologies, Human Factors, and Ergonomics to achieve a sustainable system at the engineering and social level. [ABSTRACT FROM AUTHOR]

SCADA systems are used in industries to perform control and monitoring of industrial processes in real-time. The advent of the Internet of Things (IoT), and the Industrial IoT (IIoT) in particular, has brought new disruptive technology that has the potential to drive the industrial digitization agenda well beyond the capabilities demonstrated by conventional SCADA systems. Industries, particularly manufacturing industries and utilities keep thriving to be competitive and the adoption of new technologies in the form of IIoT is set to improve efficiency and productivity through enhanced real-time data analytics and production availability. This paper examines the opportunities and challenges presented by integrating IIoT into existing SCADA systems. Potential solutions to the challenges are presented together with future research outlooks that have a bearing on the SCADA/IIoT integration efforts. [ABSTRACT FROM AUTHOR]

The fourth industrial revolution promotes the numerous applications of the industrial internet of things to transmit integrated information. Scheduling multi-data of a distributed network involves many complications in maintaining network performance. This paper introduces a linear mixed model approach to multi-data integration and transmission in an industrial enterprise network. This paper includes the computational scheme and its performance evaluation of the linear mixed model. The proposed computation scheme incorporates the following contributions: i) a simulation of an industrial enterprise network configured with distributed network domains of multi-data. ii) Development of a linear mixed model approach to integrated multi-data for transmission through the shared network links. iii) The performance evaluation of the proposed scheme using the R squared value of Bayesian linear regression. The proposed scheme realizes the type of data and their level of integration at shared network links of an industrial enterprise network. The expected outcome is to exhibit healthy throughput in scheduling both level 1 and level 2 integration of multiple data types promising to shape the path toward Industry 4.0 applications. [ABSTRACT FROM AUTHOR]

Interaction between humans and robots in the workplace garners interest in recent years due to the introduction of Industry 4.0 and Industry 5.0 frameworks. A scoping review was performed aimed at investigating the effect of robot design features on their human counterparts. In the analysis of the 32 identified articles, the robot design features used in the literature are shown along with the effects on the operators. Results showcased the many to many relationships between robot design features and effects on operators. Robot appearance, for example, and capabilities play a role in the operators' perception and expectations of their capabilities based on the task and subsequently perceived reliability and safety. Communication capabilities between operators and robots is an integral part for teamwork and performance as it can affect work processes. The paucity of papers empirically addressing human robot interaction as a system is consistent with results from previous literature, indicating the need for more research. The results of this investigation can prove useful in the form of advice to designers and practitioners, such as the operator's involvement in implementation, knowledge on robots' capabilities and training. Research gaps identified are discussed, as well as future research directions. [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]

In this paper the case study of the application of Failure Mode and Effects Analysis (FMEA) on the testbed "Smart Factory Line" has been described. The main goals of this study have been to verify feasibility and applicability of FMEA on such complex technical system and to show real possibilities of integration of Industry 4.0 principles and technologies with selected methods of quality management. The significant part of the paper is formed by extensive literature review of the related topics such as mass customization and individualization, Industry 4.0, Quality 4.0, fault management and their selected principles, methods and technologies, putting the stress on the team cooperation of experts of different specializations. The attention also has been put on the describing testbeds as platforms for the academy world and practice connection. This case of the practical integration of the smart factory testbed design and its products with the new FMEA approach based on the harmonized AIAG and VDA manual has brought new views on the practical realization of smart factories, on importance of building Quality 4.0 and on requirements for interdisciplinary education of the technicians and quality specialists for Industry 4.0. [ABSTRACT FROM AUTHOR]

The requirement from IMO regarding an environment-friendly approach has prompted ship owners and shipbuilders to make sure their vessels complying the regulations. Green shipyards, incorporating best practices and technology, can reduce th polution generated during vessel construction. This paper investigates the current trends of green shipbuilding and proposes potential practices of green shipyard. A systematic review was conducted using publication databases such as Scopus, Science Direct, and Taylor & Francis from 2010 to 2022. It can be concluded that most of the studies have been discussing the Industry 4.0 (I4.0) technologies to be implemented in the shipyard where 16 potential practices and technology-based in the green shipyard were figured out. This study also found that automation is the most discussed topic in I4.0 technologies followed by an alternative fuel (LNG/LPG fuelled engine) and 3D printing and design. Furthermore, the study on green shipbuilding attracted the attention of China and Spain as these countries have published most papers. This study will help Malaysian shipbuilders and stakeholders to have a better view regarding the green shipyard concept and related practices and technology. [ABSTRACT FROM AUTHOR]

This paper analyzes the joint adoption of Industry 4.0 (I4.0) technologies and Total Productive Maintenance (TPM) practices in manufacturing firms. For that, we surveyed 335 practitioners from firms currently implementing TPM and I4.0, located in sixteen countries. The collected dataset was analyzed using sets of partial correlation analyses, obtained when controlling the effect of three contextual variables, all assessed at the firm level: (i) socio-economic context, (ii) technological intensity, and (iii) size. Pairs of TPM practices and I4.0 technologies with significant positive correlations in all partial correlation sets indicate positive trends in the adoption of elements in the pairs, regardless of context, and may be viewed as indicators of TPM practices and I4.0 technologies more prone to be integrated. Our results identified 67 pairs of I4.0 technologies and TPM practices meeting the significance criterion. Four TPM practices (fostering operator ownership, standardization of AM checks, setting 3M—machine/man/material—conditions, and constant search for the next generation of technology) and two I4.0 technologies (Internet-of-Things, and big data) appeared in 26 of the 67 pairs. The study unveiled trends in the integration of I4.0 and TPM, pointing to pairs whose joint adoption is predominant and indicating pathways to the digitalization of maintenance. [ABSTRACT FROM AUTHOR]

The fourth Industrial Revolution is in full swing, the whole world is pondering the same question: Where is the core breakthrough? With the continuous breakthrough and extensive application of cloud computing, big data, artificial intelligence and other next-generation information technologies, One sign has emerged: digital economy is becoming a new driving force for global economic growth. The impact of digital transformation on enterprises is to improve the quality and efficiency、Improve information quality, Or spread out limited resources and reduce productivity, There is no consensus among academics. To fill that gap, this paper aims to sort out the internal mechanism of how digital transformation improves the output quality of the technological innovation of enterprises from a theoretical perspective, and to establish a digital transformation index at the micro level. On this basis, this study empirically examines how digital transformation affects the output quality of technological innovation.we adopts panel data. It uses data collected from 159 manufacturing enterprises in China. By adopting the methods of PLS structural equation and fuzzy set qualitative comparative analysis (fsQCA), it empirically tests the research model and conducts a mediation analysis through regression analysis. The findings indicated that, Digital transformation can help manufacturing enterprises to improve the output quality of their technological innovation; digital infrastructure plays a significant mediating role between digital transformation and the output quality of technological innovation; the investment of manufacturing enterprises in software and hardware digital infrastructure have a positive impact on the output quality of technological innovation. There is a complementary relationship between the two. The paper was conducted in the context of Chinese culture and can be generalized to other Asian countries. We believe that the research conclusions of this study have important practical significance for how manufacturing enterprises can effectively carry out digital transformation and improve the output quality of technological innovation. [ABSTRACT FROM AUTHOR]

This paper aims at examining the role played by Industry 4.0 (I4.0) on the relationship between knowledge management (KM) practices (i.e., knowledge acquisition, knowledge dissemination, and responsiveness to knowledge) and innovation performance (represented by process and product innovation). For that, 153 practitioners from manufacturing firms in India and Brazil were surveyed. The data were analysed through multivariate data techniques. This study was grounded on the concepts from the socio-technical systems theory. Our findings indicated that I4.0 design principles positively moderate the relationship between KM practices and innovation performance. In particular, this moderation seems to be more prominent for product innovation performance, although it was also found for process innovation performance. I4.0 design principles determine the expected mindset and behaviours in companies undergoing digital transformation. Our research showed that the effect of KM practices on innovation performance may be boosted when I4.0 design principles are extensively integrated into organisations. [ABSTRACT FROM AUTHOR]

According to [Dolgui, Alexandre, and Jean Marie Proth. 2010. Supply Chain Engineering: Useful Methods and Techniques. Vol. 539. Springer.], advancing tactical levels in production systems has led to the disappearance of static scheduling in favour of dynamic scheduling. Additionally, the evolving challenges in the supply chain paradigm have significantly impacted the organisation of production systems. This shift has moved scheduling issues from the tactical to the strategic level, resulting in linear organisations encompassing scheduling decisions. [Proth, Jean Marie. 2007. "Scheduling: New Trends in Industrial Environment." Annual Reviews in Control 31 (1): 157–166. .] emphasised that real-time scheduling in production systems has become a pivotal area of research. He presented several open problems for researchers to address in this context, including (1) the development of real-time algorithms capable of handling multiple operations on the same product and unrelated resources, (2) adapting previous schedules with certain modifications, (3) addressing unforeseen actions that arise randomly in real-time planning, and (4) exploring cyclic scheduling problems with size limits as alternative solutions to heuristic approaches. This paper reviews the evolving trends in light of J.M. Proth's predictions and advice within the aforementioned domains. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) provides new opportunities to improve manufacturing lines' performance and in-plant logistic processes. The digital milk-run system represents the new frontier to optimize material handling strategies but is still not fully exploited to address material distribution depending on the time slots required by the manufacturing lines. Therefore, to fill this gap, this paper investigates the actual integration of the milk-run system with an IoT system. An analytical model for planning a dynamic routing strategy for tugger trains to deliver the materials to different workstations of a production line has been developed. The proposed model provides a materials distribution consistent with the time slot required by the manufacturing line, ensuring the minimisation of the total distance of the routes. An algorithm developed in Python is proposed to solve the NP-hard problem (nondeterministic polynomial time problem). The model has been applied to a real case of a worldwide automotive company to validate and prove its efficacy and efficiency. Indeed, compared to the current in-plant logistic strategy, (i) the inventory stock of each workstation was ensured, (ii) the average utilization rate of the tugger trains' fleet was improved, and (iii) the daily path was minimized. [ABSTRACT FROM AUTHOR]

In the production planning and control field, assessing the performance of a manufacturing system is a multi-dimensional problem in which neglected dimensions may lead to hidden inefficiencies and missed opportunities for gaining a competitive advantage. In this paper, a new data formalization method is proposed to model a manufacturing system by simultaneously considering value creation and technical, economic, and environmental performance. The proposed method combines the principles of Material Flow Analysis and a new data structure that exploits some characteristics of the Multi-layer Stream Mapping and the Enterprise Input-Output methods to obtain a data-driven approach, typical of Industry 4.0. The proposed method can deal with complex systems and allows to consider also non-value-added activities such as transport and inventories. The implementation of the method and its advantages are shown through a numerical example based on a recycled plastic pipeline manufacturing system. The method shows positive synergies and mutual benefits between sustainable production, lean principles, and data-driven approaches and technologies of Industry 4.0. The method improves the alignment of environmental, technical, economic, and value creation information between operational and strategical levels, removing redundancies in data collection, conditioning, and processing activities, thus reducing partial information, hidden risks and opportunities, and inconsistencies. [ABSTRACT FROM AUTHOR]

Exploring the impact of digital finance on environmental pollution is of great significance for realising regional sustainable development and promoting digital finance strategies. This paper uses the balanced panel data of 274 prefecture-level cities in China collected between 2011 and 2018, empirically studies the impact of digital finance on environmental pollution, and analyses its direct, mediating, nonlinear and heterogeneity effects. The results show that digital finance reduces environmental pollution and the effect gradually increases with time. The sub-indicators of digital finance significantly reduce environmental pollution, among which the usage-depth has the greatest impact on environmental pollution, while digitalisation level has the least. Digital finance reduces environmental pollution through production technology and industrial structure, while pollution control technology does not play an intermediary role. The pollution control effect of digital finance is more prominent in cities with a high level of Internet development and human capital. Furthermore, the inhibitory effect is more significant in western region and medium-small sized cities. [ABSTRACT FROM AUTHOR]

In Warehouse Logistics (WL), the growing adoption of Industry 4.0 automations coupled with the demand for efficient and almost instantly responsive decision-making has already created a paradigm shift from the traditional Discrete Event Simulation (DES) models into innovative new ones utilizing the inherent functionalities of 'Digital Twins' (DTs), one of today's most disruptive Industry 4.0 technologies. DTs are 'real-world aware' through the creation of exact software replicas of actual systems, allowing the convergence of physical and virtual entities and facilitating real-time responsive decision-making. In this paper, a bibliometric and content analysis-based systematic literature review is performed to explore the adoption, implementation and evaluation of DES and DTs in WL. The authors focus on how and to what extent these two methods are integrated into the various WL theoretical and real-life applications to enhance a plethora of performance factors as well as look into their exploitation, as standalone solutions or in tandem with one another and other modelling and simulation approaches, technologies, algorithms, methods, methodologies and strategies in different contexts and environments. Based on the review results, a conceptual framework is proposed combining the multiple perspectives examined, contributing to the systematization and standardization of the field and pinpointing future research directions. [ABSTRACT FROM AUTHOR]

Cleaner Production has emerged as a preventive environmental strategy integrated into production systems as part of the necessary changes. Much research on Cleaner Production has been carried out worldwide. However, research that addresses the use of Industry 4.0 technologies in Cleaner Production practices is still insufficient and unclear. Based on this scenario, a bibliometric analysis was conducted to explore research trends regarding Cleaner Production using Industry 4.0 technologies. Publication data were collected from the Scopus and Web of Science databases, and the time frame was 2011 to 2023. The results presented the journals, countries, themes, and studies with the most significant impact. In this study, 112 papers on the context of Cleaner Production in Industry 4.0 and its technologies were identified. The results obtained can help scholars better understand the research development trends in Cleaner Production using tools from Industry 4.0, detect unexplored areas, and provide direction for future research. [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]

Nowadays, academics and industrial practitioners widely consider additive manufacturing systems to enhance overall production and operational effectiveness and efficiency, owing to the concepts of Industry 4.0 and 5.0. This paper proposes a structural equation model to evaluate the impact of customisation experience and perceived value within a data-driven additive manufacturing system. To deepen the understanding of customer-perceived value and its connection to additive manufacturing, this study defines four new sources of perceived value: reliability, performance, aesthetics, and features. All of these new values are based on utilitarian value, and four of them exhibit statistical significance in relation to utilitarian value. In order to apply these new values to additive manufacturing, it is necessary to conduct mechanical testing and studies to identify the ideal parameter set for 3D printing that aligns with customer-perceived value regarding these new values. [ABSTRACT FROM AUTHOR]

This study aims to validate adequate content for Industry 4.0 training recommended to managers unfamiliar with this theme and intends to get a holistic view. A Delphi Method was conducted with experts in Industry 4.0 and training. These experts evaluated an initial training content structure segmented into seven modules based on academic literature. The consensus was reached in two rounds with the participation of twenty-seven (27) experts in the first round and twenty (20) experts in the second round. The results were discussed considering literature statements. The initial training content structure was reordered and an additional module was appended. The validated training content structure considers a total of eight modules ordered as follows: Business Management Models Impact; Product Personalisation and Smart Products; Smart Factory and Integration; Modularity and Service Orientation; Decentralisation and Interoperability; Virtualisation and Real-Time Capability; Corporate Social Responsibility (CSR) and Sustainability Impact; and Industry 4.0 Implementation. The results presented here are helpful for academics, consultants, and professionals who need to design courses and training about Industry 4.0 theme. It is essential to mention that no similar papers were found in scientific databases, reinforcing the originality and the contribution of this research. [ABSTRACT FROM AUTHOR]

As Industry 4.0 develops, enterprises are investing in the industrial intelligent transformation. Using unique data from Chinese industrial enterprises, this paper investigates whether air quality affects the upgrading of industrial intelligence in enterprises. We find that decreased air quality improves industrial intelligence for enterprises, as enterprises increase their use of intelligent industrial robots when air pollution causes shortages of labour quantity and efficiency. Further analysis shows that large-scale enterprises and enterprises in areas with high economic development are more inclined to develop industrial intelligence in response to decreased air quality than their counterparts. In addition, the results indicate that the intelligent transformation of enterprises can improve their performance. This study provides a new perspective on the impact of environmental issues on corporate behaviour. [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]

This paper outlines a conceptual framework and a repository of skills for Industry 4.0 that manufacturing managers and other stakeholders can use for training, hiring and developing human resources. The framework and the repository of Industry 4.0 skills were developed by involving industrial practitioners, technology providers, recruitment agencies, research and education organizations in scenario-based focus groups and semi-structured interviews. The results of this study contribute to improving our current understanding of the skills required for Industry 4.0, and they can be used for the identification and assessment of workers' skills as well as the design of skill development programs that match the needs of the industry and for the definition of policies that support the development of human capital to improve the employability of individuals and the performance of manufacturing companies. [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]

This study aims to investigate the effect of different types of performance measurement systems (PMSs) use (diagnostic use and interactive use) on Industry 4.0 maturity, examining whether there is a need for digital governance to facilitate the relationship between different types of PMS use (diagnostic use and interactive use) and Industry 4.0 maturity. Although the use of PMSs has been identified as beneficial in the Industry 4.0 context, relatively little research exists on the digital governance that enables firms to lead and control digital processes. The paper posits that digital governance plays an important role in mediating the relationship between PMS use and Industry 4.0 maturity. The data were gathered from 280 small- and medium-sized enterprises (SMEs), which operate in the service and manufacturing industry in Finland. The results demonstrate that different types of PMSs use cannot provide Industry 4.0 maturity alone, so there is a need for digital governance to fuel different types of PMS use, hence leading to Industry 4.0 maturity. However, diagnostic use of PMSs significantly hinders digital governance, while the interactive use of PMSs significantly drives digital governance. Finally, digital governance facilitates Industry 4.0 maturity. [ABSTRACT FROM AUTHOR]

Currently, organizations in the manufacturing sector are exposed to high levels of competition and constant changes in consumer requirements. To survive in an increasingly competitive environment, it is essential to adopt new technologies to ensure success in a sector that is currently going through the fourth industrial revolution, associated with Industry 4.0 (I4.0). Additive manufacturing (AM) is one of I4.0's technologies, which can produce products layer by layer, playing a key role in the innovation of business models. In this manner, organizations must integrate AM starting by understanding their maturity level, which allows them to reflect on weaknesses and strengths as well as opportunities for improvement. However, literature is currently lacking maturity models for AM. This paper proposes a maturity model for AM, which aims to help organizations in the manufacturing sector in determining their maturity level regarding the implementation of the AM. The model developed was validated theoretically. Also, a case study was conducted on an automaker, where it was possible to conclude that, despite the analyzed company has a high level of maturity regarding the technological deployment of AM, an AM's strategy definition is presently missing. [ABSTRACT FROM AUTHOR]

Within Industry 4.0, the integration of Industrial IoT (IIoT) marks a transformative phase for modern industries, fostering the development of smart factories and intelligent manufacturing systems. Despite the substantial growth of IIoT, a critical research gap persists in web security within IIoT environments. This paper addresses this gap by proposing an explainable and robust machine learning-based web attack detection system to ensure IIoT web application security using ToN-IoT and NF-ToN-IoTv2 datasets that accurately reflects IIoT traffic. Given eXplainable Artificial Intelligence’s (XAI) capability to instill trustworthiness and transparency in learning models, the authors opted for the SHAP technique for feature selection, leveraging its global insights to explain feature contributions to the system’s decision-making. Compared to the existing works, the system demonstrated strong performance across various metrics, including accuracy, recall, precision, specificity, F-value, FPR, FNR, AUC-ROC curve, MCE, training, and prediction times, in binary and multi-classification scenarios. [ABSTRACT FROM AUTHOR]

In light of Industry 4.0, rapid analysis and optimization of manufacturing processes are emerging as a vital demand of smart manufacturing factories. Ergonomics is an essential aspect of the ongoing screening of working conditions and a fundamental variable in Industry 4.0, as it calls for a flexible manufacturing system to strengthen the competitiveness of factories in the global market. This paper proposes a new augmented reality-based ergonomic evaluation platform: Augmented Reality-based Ergonomic Platform (ARE Platform). Introducing the Augmented Reality technology by superimposing virtual planning objects into the physically existing production environment. Utilizing the motion capture system collects data for a set of ergonomic indexes (RULA, OWAS, and NIOSH), accessibility and visibility verification. ARE platform could be used in the verification phase of smart manufacturing systems to evaluate the level of risk to workers' bodies during operations in real-time. The platform reduces the time and economic cost of verification and satisfies the rapid response of ergonomic evaluation and feedback in the context of smart manufacturing. Finally, the developed ARE platform is validated in two rigorous automotive assembly cases in the laboratory. Meanwhile, the ergonomic assessment results are analysed and reported for a people-oriented manufacturing system. [ABSTRACT FROM AUTHOR]

Timely and accurate misalignment fault detection in rotor bearing systems is crucial for reliable and efficient operation, especially in Industry 4.0 based condition monitoring. The method described in this paper uses artificial neural networks (ANN) and empirical modelling to identify misalignment defects in rotor bearing systems. To capture the dynamic behaviour of the rotor bearing system under different operating conditions and fault scenarios, an empirical model based on Dimensional Analysis (DA) is developed. This model is trained using vibration data obtained from a well-aligned system. By comparing the predicted response with the actual vibration response, an accuracy of 8% to 10% is achieved. However, accurately quantifying the severity of misalignment poses challenges due to system nonlinearities. To address this issue, an ANN is employed to learn the mapping between vibration features and misalignment levels. To train the ANN, a comprehensive dataset is created through experiments conducted on a test rig. Artificial misalignment is introduced to the rotor, and vibration signals are analysed under various conditions of misalignment, rotor speed, and static load. The trained ANN is validated using experimental data, demonstrating an impressive accuracy of 99.46%. This integrated approach holds significant potential for predictive maintenance, leading to improved operational efficiency. [ABSTRACT FROM AUTHOR]

Recently, Metaverse is gaining prominence within the field of radiology due to its potential to revolutionize image visualization. Radiologists can harness its capabilities to access dynamic, highly detailed results, thereby enhancing diagnostic precision. Digital twins, at the core of the Metaverse, are digital replicas of real-world objects and entities. They serve as the foundational building blocks, enabling the creation of virtual counterparts for everything within the Metaverse. To ensure the reliability of these digital twins, blockchain technology offers a multi-dimensional data storage solution, reinforcing data integrity and trustworthiness. It is used to ensure a transparent and 3D visualization of each communication and interaction for further looking up any criticality if present in the network. With the rapid increase in value and volume of data, the evolution of the metaverse faces a number of privacy and security concerns. Furthermore, the metaverse in Industry 4.0 is a trending topic that further needs to focus on its security challenges at its initial stage. Fortunately, blockchain is considered as one of the significant solutions. The aim of this paper is to propose a secure and efficient fuzzy evaluation matrix by computing the trust values along with integrating with blockchain mechanism in industry 4.0 enabling metaverse environment. The proposed mechanism is validated against various security concerns such as broken authentication, eavesdropping, personal information leakage, data injection, and unauthorized access. The proposed mechanism showed the validation rate against existing schemes with an improvement of 94% in comparison of several security metrics. [ABSTRACT FROM AUTHOR]

This study aims to model temperature distribution in friction stir welding (FSW) using various backing plates and polygonal pin profiles since temperature significantly modifies the microstructure and texture of the weld zone affecting the weld quality such as weld strength, hardness, etc. The experimental results depict the importance of temperature on the grain size and tensile strength of the materials. However, determining the temperature at each point of the weld is difficult and expensive in the case of experiments. Therefore, in order to accomplish the objective, it is necessary to perform simulations. This paper presents a 3-D transient multiphysics model developed for FSW combining multiple physical phenomena such as heat transfer and structural mechanics in a unified framework, COMSOL. A viscoplasticity model is chosen as behavior for the AA1100 aluminum material using Anand viscoplasticity. It is computationally efficient and accurate. The model fidelity to the twin FSW process is achieved by considering temperature-dependent yield strength. Modeling results show the polygonal pin profile edges to be influencing the temperature. Increasing the number of faces on the pin sides leads to a higher temperature. Specifically, transitioning from an octagonal to a decagonal profile results in a minimal increase in total heat generation. As the pin shape approaches cylindrical, there is a gradual convergence in heat generation with that of a cylindrical pin. Experiments are also carried out that validate simulation results. Overall, the model is sufficient to twin the process for predicting weld quality and is Industry 4.0-compliant. [ABSTRACT FROM AUTHOR]