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Inspired by the natural intelligence of humans and bio-evolution, Artificial Intelligence (AI) has seen accelerated growth since the beginning of the 21st century. Successful AI applications have been broadly reported, with Industry 4.0 providing a thematic platform for AI-related research and development in manufacturing. This paper highlights applications of AI in manufacturing, ranging from production system design and planning to process modeling, optimization, quality assurance, maintenance, automated assembly and disassembly. In addition, the paper presents an overview of representative manufacturing problems and matching AI solutions, and a perspective of future research to leverage AI towards the realization of smart manufacturing. [ABSTRACT FROM AUTHOR]

• Introduced SWM 4.0 by considering Industry 4.0 technologies in waste management. • Reviewed I4.0 role in waste types, processes, and strategies of waste management. • Developed SWM 4.0 framework through smart factories, cities, businesses, and people. Smart Waste Management (SWM) discusses the waste management process for different types of waste while introducing an intelligent approach to controlling the amount of waste. This paper introduces SWM4.0, which applies Industry4.0 (I4.0) technologies in various related events. First, the paper presents a systematic literature review on the role of I4.0 technologies in SWM activities regarding waste types, waste management processes, and 5R strategies. Then, existing solutions supporting SWM4.0 are extracted to develop a framework for exploring the use of I4.0 technologies. This framework includes sharing the four main pillars that contribute to the success of SWM4.0, namely smart people, smart cities, smart enterprises, and smart factories. Furthermore, this review suggests the possibility of unifying and extending existing solutions and identifying the necessary links and interfaces for researchers. For managerial implications, the framework identifies future strategies to fulfill specific SWM tasks and to foster new technological solutions for future research. [ABSTRACT FROM AUTHOR]

In the current era of Industry 4.0, many new technologies offer manufacturing industries to achieve high productivity. Augmented Reality (AR) is one of the emerging technologies that has been adopted in industries to aid users in acquiring complex skills and carrying out many complicated tasks such product assembly and maintenance. Nevertheless, most AR applications have been developed without clear understanding of how such technology can facilitate improved learnability in terms of knowledge reusability. This paper proposed an enhanced AR-based training system that provides multimodal information with a contextualized information to improve task comprehension and knowledge reusability compared with traditional AR that presents unimodal and decontextualized information. An empirical test was carried out to assess the task performance and the task learnability aspects of this enhanced AR compared to the traditional AR and the paper-based document. The experiment consisted of a training phase where participants carried out an electrical connection task of a sensor followed by a knowledge reuse phase where participants had to wire a second sensor using their previous training. A pre-test quiz was given before the experiment followed by the post-tests phase after the training. Post-tests consist of one post-test given directly after the experiment (short-term retention test) and a second post-test quiz given one week later (long-term retention test) to measure information retention. The results indicated that AR-based approaches could enhance knowledge acquisition by around 18 % for traditional AR and almost 25 % for enhanced AR as compared to paper-based approach. While all training systems achieved relatively equivalent well for short-term retention test, trainees who used the enhanced AR training systems statistically outperformed those in the paper-based group for long term retention test. Furthermore, there was a positive correlation between the score of short-term retention test and the score in the knowledge reusability which was also shown by the higher scores in knowledge reusability for the enhanced AR training system compared to the other two approaches. These findings are discussed in relation to the Industry 5.0′s human centric core value. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic and the Russian-Ukrainian war revealed vulnerabilities in supply chains, emphasizing the need for resilience in multiple industries. While Industry 4.0 is valuable, it cannot fully address complex supply chain challenges. Therefore, exploring additional drivers like Operations Management has become imperative. More clearly, the adoption of Operations Management tools and practices helps implement and use enabling technologies more effectively. In this context, the aim of the paper is to conduct a literature review to study supply chain resilience considering these two concepts. In other words, the objective of the work consists in examining the effectiveness of the mentioned combination. The paper presents a conceptual framework for enhancing supply chain resilience by exploring the potential of Industry 4.0 and Operations Management. The paper also underlines the positive impact issued from hybridizing both solutions to achieve resilience. As it discusses enhancing features of each concept aside the other. The paper provides a basic model for researchers to further detailed studies, and for managers to bringing the proposed framework from theory to practice. [ABSTRACT FROM AUTHOR]

To satisfy the rising demand for higher product quality and giga-casting requirements, the casting process is undergoing significant changes. However, current control methods rely significantly on human expertise and experience, making process availability and stability difficult to ensure. The semisolid casting process is more complicated than conventional liquid casting due to the additional casting parameters incorporated during the slurry preparation, which can have an effect on the quality of the final product. Therefore, an efficient tool is required to simplify the complete process of semisolid casting. The introduction of an AI system to aid in the supervision of the casting manufacturing procedure is one potential solution. This paper introduces a new casting system named "Smart-Cast" developed for this specific purpose. The paper describes the functions of the system and its current development process. Using an AI system as an assistant can help to achieve the goal of enhancing the efficacy of casting process control, and it can also help foundries step into the Industry 4.0 era. [ABSTRACT FROM AUTHOR]

In the rapidly evolving field of Artificial Intelligence (AI), its application in industrial settings, particularly for anomaly detection in time series data, poses unique challenges. Current methods often lack a comprehensive understanding of the trade-offs involved in achieving optimal model performance, data preparation effort, and prediction quality. To bridge this gap, this paper presents an adaptive approach to address these challenges, focusing on making conscious decisions about mentioned trade-offs. Inspired by the principles of the Iron Triangle from product engineering, our methodology introduces a novel "AI triangle" with dimensions of Speed, Effort, and Quality. We applied this methodology to a real-world case study involving anomaly detection in a constrained hardware environment in the context of a forming production process. The results highlight the effectiveness of our approach in achieving a practical balance between speed, effort, and quality constraints for implementing AI in an industrial setting. This paper provides valuable insights into the considerations and trade-offs necessary for the successful deployment of AI in manufacturing and other similar industrial applications. [ABSTRACT FROM AUTHOR]

Numerous research has for long studied the challenges and opportunities within digitalization of Small and Medium-sized Enterprises (SMEs). While SMEs are financially limited compared to larger organization, previous research has succeeded in identifying potential focus areas that allow SMEs to participate in the industrial progression. Among these, pragmatic research on exploitation of existing resources have been highlighted. Therefore, this paper intends to report the findings of a single case study in a Danish SME, where the objective is to understand how technological sub-factors influence the prototyping of digital initiatives for advancing maintenance activities. The study begins with mapping the maintenance activities, along with the IT infrastructure. From here, relevant data is analyzed to understand their shortcomings, where the most dominant relates to how technicians report service orders. The paper concludes by identifying two sets of three technological sub-factors that strongly influence the digitalization of the case company's maintenance activities. [ABSTRACT FROM AUTHOR]

In the sphere of Engineer-to-Order (ETO) manufacturing, the demand for highly customized and rapidly adaptable solutions is escalating, particularly in the context of Industry 4.0 and the burgeoning focus on hyper-customization in Industry 5.0. Traditional, rigid simulation structures often fall short in adapting to these dynamic requirements, creating a need for more flexible simulation frameworks. This paper introduces a novel auto-generative Discrete Event Simulation (DES) system specifically designed to address the unique challenges of ETO manufacturing. Unlike traditional rigid simulation architectures, the proposed system offers substantial flexibility and modularity, significantly easing the development effort. Developed in MATLAB and Simulink, this paper not only presents the foundational logic and architecture of the system but also provides a practical implementation guide. By aligning with the imperatives of Industry 4.0 and 5.0, this research fills a significant gap in the literature and offers a scalable, adaptable simulation-based Decision Support System (DSS) for ETO manufacturing. [ABSTRACT FROM AUTHOR]

This paper delves into the transformative realm of Industry 4.0, emphasizing the pivotal role of Human-Robot Collaboration (HRC) in redefining manufacturing landscapes. Central to this revolution is the integration of cyber-physical systems, automation, and the Internet of Things (IoT), culminating in the advent of smart manufacturing. We explore how HRC fosters a synergistic work environment, blurring the traditional boundaries between human workers and robotic counterparts. This research emphasizes the importance of intelligent task allocation in optimizing operational efficiency while safeguarding the ergonomic well-being of human operators. A simulation-based approach is presented, focusing on task allocation within HRC contexts to enhance both human operators' productivity and fatigue levels. The study provides an analysis of fatigue dynamics and its repercussions on industrial settings. The paper outlines various collaborative scenarios, including sole human operation, robot support, a Flexible Workstation (F-WS) support, and combined robot-FWS support, evaluated through a case study of an assembly station. The results underscore the tangible benefits of integrating robotic and F-WS support, demonstrating a balanced improvement in processing speed, operator efficiency, and a significant reduction in fatigue accumulation. In conclusion, this paper advocates for a balanced, human-centric approach to Industry 4.0, where the integration of HRC and ergonomic considerations pave the way for a sustainable, efficient, and health-preserving industrial future. [ABSTRACT FROM AUTHOR]

Industry 4.0 (I4.0) has emerged as a highly innovative response to market needs, particularly in logistics. This paper presents a structured case study on the implementation of I4.0 technologies for material flow and handling management. The study explores the contextual framework, historical development, and key characteristics of I4.0, focuses on the necessary characteristics for I4.0 certification and outlines the specific features exhibited by the company under study. Objectives include enhanced traceability of metallic materials and optimization of crane management. The paper outlines the project phases, highlighting the positive impact on lead time reduction and providing insights into operational procedures. This research contributes to understanding I4.0 practices in logistics and offers valuable insights for future implementations, driving enhanced efficiency and productivity. [ABSTRACT FROM AUTHOR]

he literature on Lean Manufacturing has predominantly emphasized mass production and batch systems, leaving a considerable knowledge gap regarding the challenges and considerations specific to implementing Lean principles in Engineer-to-Order (ETO) manufacturing contexts. This paper addresses this gap by exploring the interaction between Project Cost Deployment (PCD), a tailored method for ETO environments, and the digital technologies of Industry 4.0. The research aims to identify the synergies between Lean Manufacturing and Industry 4.0, investigating how their integration can enhance productivity, quality, and competitiveness in the manufacturing industry. While Lean Manufacturing is adept at addressing loss reduction and elimination across various industrial contexts, Industry 4.0 leverages digital technologies to revolutionize manufacturing operations and enable more efficient and intelligent production systems. The paper demonstrates the effectiveness of the PCD method through an industrial application involving an international company specializing in customized paper converting machinery and lines. By utilizing PCD in conjunction with Industry 4.0 technologies, analysts were able to uncover hidden losses and quantify them from an economic perspective, while also evaluating the potential impacts of improvement activities. The results highlight the significance of synergizing Lean Manufacturing and Industry 4.0 for ETO environments, offering valuable insights into how digital technologies can be effectively integrated into Lean principles. [ABSTRACT FROM AUTHOR]

The evaluation of the volumetric accuracy of a machine tool is an open challenge in the industry, and a wide variety of technical solutions are available in the market and at research level. All solutions have advantages and disadvantages concerning which errors can be measured, the achievable uncertainty, the ease of implementation, possibility of machine integration and automation, the equipment cost and the machine occupation time, and it is not always straightforward which option to choose for each application. The need to ensure accuracy during the whole lifetime of the machine and the availability of monitoring systems developed following the Industry 4.0 trend are pushing the development of measurement systems that can be integrated in the machine to perform semi-automatic verification procedures that can be performed frequently by the machine user to monitor the condition of the machine. Calibrated artefact based calibration and verification solutions have an advantage in this field over laser based solutions in terms of cost and feasibility of machine integration, but they need to be optimized for each machine and customer requirements to achieve the required calibration uncertainty and minimize machine occupation time. This paper introduces a digital twin-based methodology to simulate all relevant effects in an artefact-based machine tool calibration procedure, from the machine itself with its expected error ranges, to the artefact geometry and uncertainty, artefact positions in the workspace, probe uncertainty, compensation model, etc. By parameterizing all relevant variables in the design of the calibration procedure, this simulation methodology can be used to analyse the effect of each design variable on the error mapping uncertainty, which is of great help in adapting the procedure to each specific machine and user requirements. The simulation methodology and the analysis possibilities are illustrated by applying it on a 3-axis milling machine tool. • Artefact-based volumetric error mapping process is modelled for optimization. • Digital twin of the machine simulates machine geometric and elastic errors. • Error mapping uncertainty can be estimated before experimental implementation. • Artefact design and measurement strategy can be optimized based on simulation. [ABSTRACT FROM AUTHOR]

X-ray computed tomography (XCT) has been shown to be a reliable tool for quality inspection, material evaluation, and dimensional measurement tasks across diverse academic and industrial applications. In recent years, the integration of machine learning and deep learning techniques have ushered new advances in the industrial computed tomography domain spanning multiple facets, including image reconstruction, segmentation, and feature characterization. This review paper comprehensively surveys the current state-of-the-art machine learning and deep learning applications throughout the entire XCT workflow. Additionally, we explore relevant developments in the medical imaging domain, evaluating their implications for industrial computed tomography. In conclusion, we identify potential future research, drawing insights from existing research gaps in the domain and recent advancements in artificial intelligence. Notably, we underscore the importance of uncertainty quantification and model explainability for further acceptance of artificial intelligence techniques in the domain. [ABSTRACT FROM AUTHOR]

Safety instrumented systems (SISs) are installed on process plants to protect against undesired events like e.g., gas leakage and overpressure. A SIS has reliability requirements that are determined during design, and conformance to these requirements should be verified during operation. It is therefore important that all SIS failures are recorded and classified according to their impact on the SIS reliability. Failures of SIS equipment classified as dangerous undetected are of particular interest because they are dormant (undetected) and will prevent the execution of the safety function (dangerous). Analysis of the failure mode and detection method is essential when deciding if a failure is dangerous and undetected. Such information is often provided as unstructured text in notifications registered into the maintenance management system. Therefore, the work of classifying failures requires considerable manual effort in reading and analyzing the texts. Approaches within natural language processing, like technical language processing, have the potential to be deployed more actively for this purpose. However, successful adoption relies on groundwork where classification rules are derived from international standards and commonly agreed industry practice. This paper presents a semi-automated process that incorporates classification rules and gives examples that indicate some of the capabilities of technical language processing for failure classification. The paper also elaborates on how the work relates to Industry 4.0 in creating digital representations to monitor the performance of safety instrumented systems. This work has been carried out as part of the APOS project (Automated process for follow-up of safety instrumented systems). The APOS project has developed knowledge and specifications that simplify and automate the design and operation of safety equipment and investigated how the failure classification process can be made more efficient. [ABSTRACT FROM AUTHOR]

In a society based on data-driven, data inclusion and data access play a significant role in societal development. A called democratization of data through open access, Open Data, must be nurtured by countries to empower their citizens, entrepreneurs, companies, industries, academics, and organizations, in general. Open Data Scoring System is an evaluation system that ranks countries in 22 categories of openness in data, divided into the 3 pillars of sustainability. In this paper, we will present the importance of Industry 4.0 and its relation to sustainability and the role of Data Science in Industry 4.0 assuming an Open Design approach. Then, an analysis is made considering the Gross Domestic Product (GDP) of the most relevant countries worldwide, the USA and China, concerning the six (6) higher ranked categories of openness data of these countries, supported by the Open Data Scoring System from 2015 to 2020. Our findings reveal that in the USA and China the main categories are seven (7), five (5), and 2 (two) categories of economic, social, and environmental sustainability, respectively. Through a correlations and co-occurrences analysis of the open data scoring worldwide reveals that the most significant categories are four (4) economic, one (1) social, and two (2) environmental. [ABSTRACT FROM AUTHOR]

Industry 4.0 represents a turning point in the thinking of the production model since it is based on digitalized production systems with the aim of improving productivity, product quality, and delivery time to the customer. The digitalization and evolution of information technology allowed the emulation of production system virtual models, namely in the concept of Digital Twin (DT), with the ability to simulate different scenarios providing support for better decision making. This concept not only represents a virtual copy of the physical world that obtains information about the state of the value chain but also illustrates a system capable of changing the development of the production activity according to the fulfillment of the intended business goals. In literature, the concept of the Digital Twin is exhaustively treated as a stand-alone factory (one digital factory represents one physical factory) and underestimates the possibility of a DT oriented to a customized product (a project) that requires decentralized production systems. This paper brings to discussion the relevance of product customized applying DT to smart customization, and the inclusion of decentralized production systems supported by Cloud Manufacturing. [ABSTRACT FROM AUTHOR]

Industry 4.0 has brought modernization to the production system through the network integration of the constituent entities which, combined with the evolution of information technology, has enabled an increase in productivity, product quality, optimization of production costs, and product customization to customer needs. Despite the complexity of human thought, artificial intelligence tries to replicate it in algorithms, creating models capable of processing databases with a high volume of information, and generating valuable information for decision making. Within this area, there are subfields, such as Machine Learning and Deep Learning, which, through mathematical models, define patterns to predict output data from known input data. In addition to this type of algorithm, there are metaheuristic models capable of optimizing the parameters required in Machine Learning and Deep Learning algorithms. These intelligent systems have applications in various areas such as industry, construction, health, logistics processes, and maintenance management, among others. This paper focuses on Artificial Intelligence models addressing Industry 4.0 approach. [ABSTRACT FROM AUTHOR]

The rise of Industry 4.0 and, therefore, the integration of Internet of Things (IoT), cloud computing, data processing and analytics into the factories infrastructures has enabled the possibility to automatically collect, transform and process data from manufacturing floors. This, complemented with the use of Process Mining algorithms, allows us to use data extracted from resources activities, either performed by humans or machines, in order to discover, compare and enhance the manufacturing processes of any industry. In this paper we present a resource allocation framework that aims to provide production managers with decision support on the best possible resource allocations for a certain business process. For this, it considers various criteria such as performance, cost, frequency and resource centrality metrics. It takes into account historical data from similar business processes of the organization, and analyses this data with Process Mining techniques, such as process discovery, in order to obtain insights about the business processes, such as patterns and bottlenecks. [ABSTRACT FROM AUTHOR]

The study explores the factors affecting the adoption of emerging technologies in warehouse management. There are limited studies in the literature on factors that affect the adoption of emerging technologies in warehouse management. The advent of Industry 4.0 and the demand for next-day delivery services has grown in unison with the rapid growth of multi-channel transactional volume in warehouse management and supply chain processes. Understanding the factors is important for the adoption of emerging technologies to be beneficial. Therefore, this study aims to fill that gap. The study adopted a systematic literature review to explore the factors that affect the adoption of emerging technologies in warehouse management. The Technological, Organizational, and Environmental (TOE) framework was used to explore the factors in the above-mentioned study. The study used quantitative content analysis to analyse data collected from published articles. The study results suggest that the technological factor (relative advantage), the organisational factor (management), and the environmental factor (market structure) were the most dominant factors affecting the adoption of emerging technologies in warehouse management. The paper contributes to the body of knowledge on factors affecting the adoption of emerging technologies in warehouse management. [ABSTRACT FROM AUTHOR]

The phenomenon of Industry 4.0 is requiring organizations to restructure their roles and upskill and reskill the workforce. In addition, Great Resignation is threatening organizational knowledge and it is essential to promote happier and more engaged workers. Learning Organizations promote learning opportunities to their workforce and furthermore foster the concept of Protean Career Attitude. This paper presents the usability evaluation of a competency management and development plan tool to support, through a technological tool, the Protean Career Attitude. Usability evaluation uses tests and makes use of the Technology Acceptance Model (TAM) scale. The results are promising regarding the acceptance and usability of the technology and show no significant discrepancies when comparing age groups or level of education. However, in some responses it was possible to conclude that the older groups (41-50 and 51-60) are more receptive to technology, despite the slight increase in the time to perform the tasks. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is an important enabler of the fourth and fifth industrial revolutions. It has the potential to empower intelligent production systems that are central to industry 4.0 and help place worker wellbeing and the planet at the centre of industry 5.0 processes. However, few studies have considered the different roles of AI in industry 4.0 and 5.0. This paper reports on a bibliometric review of 2887 documents examining the use of AI for industry 4.0 and industry 5.0. We found that scholarly activity increased rapidly from 2018 onwards and can be attributed to a large variety of countries, including India, South Africa and China. Our analysis reveals four clusters in the literature, with two showing particular promise for researchers notably around the themes of industry 5.0 and deep learning. Further insights are presented and discussed, followed by a future research agenda. [ABSTRACT FROM AUTHOR]

Natural Language Processing is the field of Computer Science that focuses on analyzing and processing natural language, mainly human text or speech. Recent trends in Natural Language Processing have led to the development of Large Language Models (LLMs): huge models trained on high amounts of data that achieve unprecedented performances in many tasks, such as answering questions, summarizing texts, or coding. These new tools have a wide range of applications and are being developed by many companies. However, Small and Medium Enterprises (SMEs) struggle to implement these new technologies, mainly because of the lack of resources. This paper aims to show the opportunities and challenges related to NLP-based solutions in SMEs based on a literature review. The main result is that NLP-based solutions have a wide range of applications in various companies, including SMEs, and may lead to many changes. However, there are still many obstacles to developing these tools in SMEs: SMEs lack specialized know-how to develop these solutions and do not often have standardized data. Moreover, there exists nearly no support for SMEs in the scientific literature to develop these tools. [ABSTRACT FROM AUTHOR]

Organizations struggle to generate more value through embracing digital transformation but are often confronted with lack of skills in this growing area. For instance, managing (digital) transformation projects requires appropriate skills to holistically plan and implement transformations. This paper outlines a set of skills that can be acquired through training programs of digital transformation projects managers. These skills are built by a working group in academia based on defined persona and then evaluated by practitioners through interviews. The outcome of the interviews indicates a general acceptance of these skills and uncovered some improvement avenues. The results of this work are expected to pave the ground for structuring training programs and building teaching content meeting actual learners and business needs. [ABSTRACT FROM AUTHOR]

Ensuring precise measurement is critical for manufacturing industries; and a coordinate measuring machine (CMM) is widely used for automated inspection of critical and complex components. But there is a lack of clarity regarding optimal timepoint for CMM probe re-calibration. Too frequent calibrations increase the cost and an uncalibrated CMM probe affects the inspection quality. When to get a CMM probe calibrated is still an open question. It is important to accurately determine the optimal probe re-calibration intervals to mitigate the adverse effects on the quality and the time. This paper presents a novel data-driven approach to predict the optimal timepoint to re-calibrate a CMM probe. The data is collected at various levels to develop, train, and validate the predictive model, which includes numerical and categorical data-frame such as probe usage metrics, probe calibration environmental condition, probe specifications, workpiece data, probe calibrated dimension data and calibration requirement. The research potentially aims to reduce the CMM downtime without compromising the product quality variations that arise from the anomalies/errors related to the probe re-calibration thereby enhancing the inspection efficiency as well as sustainability. [ABSTRACT FROM AUTHOR]

Environmental degradation and stringent business regulations require wider sustainable manufacturing practices by bringing the waste/unused resources back into mainstream production. Implementation of collaborative manufacturing – industrial symbiosis – approach is recognized as an effective strategy in the globalized industry 4.0 environment. Industry 4.0 technologies with intertwined supply chain add to the complexity but offer the possibility of breaking the geographical boundaries as the current industrial symbiosis is heavily location dependent. The paper proposes an industrial symbiosis framework based on Industry 4.0 enabling technologies and scalable to global level. A three-level framework is proposed showing the corresponding industry 4.0 technologies at each level. The proposed symbiosis framework and further development of Industry 4.0 technologies-based architecture shows the capability of using the technologies for wider collaboration and faster achievement of sustainable goals. The horizontal integration capability of industrial symbiosis is inferred from the literature. The current industrial understanding and the level of implementation maturity is comprehended from the analysis of digitalization tools and platforms discussed for industrial symbiosis. The work carries forward the industry 4.0 research and its further extension to fifth generation with human resource knowledge sharing at its core. [ABSTRACT FROM AUTHOR]

Data-driven Digital Twin approaches have gained popularity in research literature, particularly in relation to Industry 4.0 achievements. Data-driven Digital Twins process widely available real-time data in an automated manner to extract models from various systems of interest. However, these model extraction approaches commonly lack systematic integration of expert knowledge from human experts, such as engineers. Combining valuable expert knowledge with data-driven model extraction approaches is a highly complex task that can significantly contribute to more accurate models within shorter time periods. In this paper, we provide a comprehensive overview of the research done on fusion of data and expert knowledge for Digital Twins to identify and describe the current gap. Resulting from our findings, we propose an initial framework for integrating expert knowledge and data for Digital Twin model extraction. Subsequently, we provide an overview of the main challenges and opportunities in fusing data and expert knowledge in the context of Digital Twins. [ABSTRACT FROM AUTHOR]

The rapid expansion of industrialization and the increase in energy demand make energy resource management necessary in various industries. Creating digital twins from available streaming data offers a practical way to enhance the efficiency of manufacturing systems. Digital twins allow for detailed analysis of key metrics, such as the energy consumption of complete manufacturing systems. Utilizing digital twins can greatly assist in improving manufacturing processes for reduced energy consumption. In this paper, we investigate the data requirements for the generation of energy-oriented digital twins of manufacturing systems. For this purpose, we developed a case study to illustrate and outline the necessary data requirements for a manufacturing system to enable the collection of data that can be used to extract energy-oriented simulation models. We, subsequently, analyze and generalize our findings that can be applied to a broader context. [ABSTRACT FROM AUTHOR]

In the rapidly evolving domain of Industry 4.0, effective management of software updates is crucial for maintaining system continuity and security. This paper presents a novel machine learning-based approach for a prompt and effective triage of software updates, leveraging an evaluation of six release note classifiers to categorize updates by component type, release type, and security risk. Our methodology, tested on a dataset of 1,000 release notes commonly encountered in Industry 4.0 ecosystems, demonstrates Logistic Regression as the most accurate classifier. The findings not only highlight the practical applicability of our approach in real-world data but also set the foundation for future enhancements to streamline the machine learning triage process further. [ABSTRACT FROM AUTHOR]

A cyber-physical system (CPS) is an interconnected physical system that can be monitored, controlled, and operated remotely. A systems-based analysis of CPS is critical for enhancing operational safety and security. Such analysis is particularly important in light of the evolving Fourth Industrial Revolution (Industry 4.0) which is largely driven by CPS, Internet of Things (IoT), algorithmic systems, automation, and smart manufacturing. As a result of industries' and societies' growing dependence on CPSs, their safe and secure applications need to be scrutinized to avoid any threats or harm to processes and human lives. In order to achieve this target, the current work analyzes the literature to investigate challenges and opportunities present in the CPSs. The analysis attempts to answer the following Research Questions (RQs): (RQ1) What are the prominent trends in the field of safety and security of CPSs? (RQ2) What are the opportunities in Industry 4.0 technologies related to safety? (RQ3) What characteristics of CPSs are likely to cause safety and security issues? (RQ4) What are the emerging safety and security issues introduced by CPSs? (RQ5) How were these issues addressed in the literature? CPSs can address some traditional safety issues due to characteristics like high efficiency and control. However, a CPS is challenged by heterogeneity, dependencies, complexity, unattended nature, increased machine intelligence, autonomous reconfiguration, and uncertainties. Safety and security challenges include opaque system failure, complex socio-technical system, human-machine interface, cyber-physical attacks, unsecured remote configuration, lack of standards, and resilience. The paper is structured under the following key headings: bibliometric results, opportunities from Industry 4.0 technologies, issues introduced by Industry 4.0 technologies, literature proposed solutions, and research challenges. Based on the assessment of about 900 relevant publications extracted from two databases (Web of Science (WoS) Core Collection and Engineering village (EV)), the documents were categorized into eight major divisions: (i) Safety Opportunities, (ii) Hazardous Characteristics, (iii) Fault Occurrence, (iv) Cyber-physical attacks, (v) Human factors, (vi) occupational Health and safety, (vii) standards, and (viii) Reliability and resiliency. Proactive strategies were classified under four options: identification, evaluation, prevention, and management. The paper also raises some research needs and unanswered questions that require further analysis to address how security and safety mechanisms can introduce more complexity in the system that can lead to new risks. The paper concludes by emphasizing digital process safety as part of the engineering curriculum to address the process industry's need for digital solutions and to make process safety learning a conscious choice. [ABSTRACT FROM AUTHOR]

Meat, as protein-rich food, provides essential nutrition to humans. Meat consumption is expected to increase by 15% over the next decade, requiring the meat industry to boost its capacity. However, the meat cutting industry's harsh working environment and long-term heavy workload result in a labor shortage. Low-margin and low capacity require innovative processing methods. Hence, some novel automation methods are urgently demanded to meet these challenges. Accompanied by the development of Industry 4.0, robotization and intelligent systems are progressively applied in the meat cutting industry, which can provide a flexible, adaptive, and sustainable processing approach. The paper selected 50 papers using the systematic literature review method. This paper provides an overview of the current research and commercial applications of cutting robotization from livestock, poultry, and fish in the meat industry. Additionally, intelligent sensing technologies, advanced cutting techniques, and a novel manufacturing concept called the meat factory cell, are emphasized to promote efficiency, scalability, and modularization. Finally, we discuss the potential applications of digital technologies in the meat cutting industry and the focal points of future research aiming to promote cutting robotization. The meat cutting process can be highly robotized by integrating dexterous cutting robots, advanced sensing techniques, and digital systems, driving the transformation from manual labor to robotic, efficient, and intelligent manufacturing. Consequently, robotization and intelligent digital systems can provide a brand-new manufacturing method to the meat cutting industry and advance the Meat factory 4.0. [Display omitted] • Advanced perception techniques assist robots to perform adaptive cutting. • Intelligent robotic systems in the meat cutting industry are reviewed. • Commercial applications regarding meat cutting robotization are presented. • Advanced cutting techniques are highlighted to promote cutting performance. • Potential applications of advanced digital systems are provided for sustainability. [ABSTRACT FROM AUTHOR]

Industry 4.0 is still the 'next big thing' for many companies, and they are currently trying to adopt Industry 4.0 in their strategy. While Industry 4.0 continues to evolve, understanding of its driving factors remains crucial for industrial development and economic growth for competitive advantage. Those driving factors are revealed not only by exploiting advanced technology such as automation, robotization, Internet of Things, AI, smart system, or big data analytics but also the human factors, including culture or willingness to change. This paper aims to analyze the essential driving factors for Industry 4.0 maturity models. All factors are identified from a structured literature review. Then, the weights expressing the importance of the factors are calculated by obtaining expert opinions. This study utilizes a linguistic fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and using Decision Making Trial and Evaluation Laboratory (DEMATEL) method for weighting and ranking the driving factors and mapping the relationship between them. The analyzed driving factors identified from 52 references containing maturity models for Industry 4.0 are categorized into six domains and 16 subdomains. The presented study findings in this paper can support academicians and practitioners in comparing, developing, and improving Industry 4.0 maturity models as part of their adoption strategy and development. [ABSTRACT FROM AUTHOR]

Manufacturing industry needs major transformation to meet disruptive environmental, social, and economic challenges, thus requiring a highly skilled workforce. This paper presents key functionalities, results, and best-practices for the launch and operation of a national upskilling platform. The Swedish upskilling programme Ingenjör4.0's operations have been constantly user-monitored through participant surveys measuring appreciation for training content from the participants and identifying areas with potential for improvement. Thematic analysis of 137 survey responses identified dimensions relevant for an upskilling programme's success. Results show that success factors and hurdles typically lie within the following dimensions: relevance, organization and structure, working life competencies, support from teachers, and collaboration with other learners. The paper concludes that national programmes like Ingenjör4.0 can, in a short time, have deep impact on skill levels for manufacturing industries in areas such as industrial digitalization. Highlighted success factors are: participant appreciation of highly relevant content, collaboration with other participants, highly competent teachers, and the collaboration between universities. Obstacles for the learners are feelings of mismatch in challenges and prior knowledge, lack of feedback and applicable working life examples in the teaching, and the need for increased collaboration with other participants. [ABSTRACT FROM AUTHOR]

Due to the ever-shortening innovation cycles and the rapid change of customer demands, enterprises introduce new, innovative, and modern products at an increasingly faster frequency and desirably lower costs. The technological change of products and mass customization increase the product complexity. This development requires manufacturing enterprises to perform more frequent production ramp-ups. Many production ramp-up projects miss the time, cost, and quality targets, causing manufacturing enterprises to lose potential profit due to later market introduction. Although digital tools are widely applied during the serial production phase, their potential during production ramp-up is hardly investigated in the scientific literature. Moreover, no guidelines exist on how to use digital tools during the production ramp-up in various industries. To provide data-driven support for the production ramp-up phase, requirements for the functionality of digital tools need to be defined. Henceforth, this paper applies a requirements engineering (RE) approach to elicit the requirements for digital tools to support the production ramp-up phase based on a comprehensive literature review and semi-structured expert interviews. In total, 13 experts from seven different industries participated in the interviews. After introducing the necessary theoretical foundations and the current state of research in ramp-up management literature, the RE process is described in detail. The RE process is based on user stories – an agile development tool – and the ISO 29148, which outlines an international standard for RE processes. The result is a set of requirements for the functionality of digital tools during the production ramp-up phase. Finally, the paper presents an outlook on future research perspectives for applying the elicited requirements in practice. [ABSTRACT FROM AUTHOR]

Small and medium enterprises (SMEs) struggle with the uptake of industrial data in their business. SMEs have expressed interests to integrate physical legacy machines in factories and uptake Industrial Internet of Things (IIoT) for data-driven industrial processes. A widely recognizable aids for companies are testbeds or test-before-invest environments where companies can familiarize themselves with Industry 4.0 (I4.0) technologies. In this paper, an easily scalable approach to collect industrial data is presented. The environment can be used by SMEs to benchmark the developed solutions using affordable licenses and test aspects of the industrial data best suitable for their purposes before making investments. In TAMK FieldLab I4.0 pilot trial, horizontal integration is done in between 14.0 field and control layer to integrate different machines' data to common database and common format to be used for visualization and later for data analysis. The focus of the paper is on data connectivity capabilities. The trial utilize data from different machines. First phase includes exporting data from different machines to same data table with time stamps, to enable comparison of data with same time base. With common time base it's possible to produce synchronized timeseries data visualization from all TAMK FieldLab's machines. Used common database is PostgreSQL, where data from all machines is stored. For horizontal field level integration minimum amount APIs and protocols is used. For common API protocol MQTT was chosen as it's lightweight and supported widely. ABB robot data is taken trough OPC/UA and MQTT. From Omron mobile robot data is transferred trough ARCL. The paper will identify gap areas of the implementation in comparison with I4.0 and when appropriate, RAM 4.0 framework. Finally, the next steps that will be taken in near future regarding artificial intelligence (AI) and upgrading the IIoT system to match with RAMI 4.0 framework will be presented. [ABSTRACT FROM AUTHOR]

The industrial sector absorbs a significant amount of energy and solutions are still being sought, the use of which will allow energy consumption during production processes to be reduced, but which will not influence on the quality of the final product or an order's completion time. One solution is to change the technology in the production process, but such a drastic decision would require a detailed analysis of the profitability of an investment. This article analyses movement in production technology processes towards additive manufacturing (AM) technologies in terms of reducing energy consumption. This paper has used literature studies to describe AM methods, in terms of energy consumption in production companies and a questionnaire survey, which was conducted within 125 automotive manufacturing companies in western Poland and finally for further analysis data from the small and medium sized enterprises (SMEs) were selected in order to determine the impact of the use of AM technology on energy consumption. This sector in particular needs to conduct energy consumption reducing activities at a time when Europe is threatened with massive increases in energy prices. Additionally a statistical analysis was also conducted using correlation coefficients. The results show that the SMEs surveyed, which bore the highest costs of energy consumption in production, declared that they were not interested in implementing AM technology while other companies, incurring high energy consumption costs for illuminating their production halls, saw the benefits of implementing AM and were able to reduce production costs in the assembly stage, use material more effectively and have greater freedom in the design and customization of their products, according to specific customer requirements. It was also determined that managers of manufacturing companies, who are interested in implementing AM would also like to implement water or heat recovery systems. • This paper explores the energy consumption in the context of the implementation of AM. • Employs the empirical analysis Polish SMEs (automotive industry) • Provides the actions taken by manufacturing enterprises to reduce energy consumption • Determines benefits of implementing AM in the automotive industry in the context of energy consumption • The media consumption monitoring system implemented in production processes enhances awareness of energy consumption. [ABSTRACT FROM AUTHOR]

The authors of the paper have recently been involved in various projects related to the Internet of Things (IoT) and its practical application in several fields. These areas include Industry 4.0, smart cities, and healthcare - with a focus on behavior change. The projects used up to date technologies such as IoT, Machine Learning, Big Data, System of Systems (SoS) and Cloud Computing among others. The authors have specifically focused on specific IoT and SoS architectures, aiming to support and illustrate a variety of use cases. In this context, SoS expands the use of localized cloud, improving integration and communication technologies. It is essential that these SoS provide reliable quality of service (QoS), which includes management and monitoring of various QoS attributes. The performance of smart applications is highly dependent on the quality of the data they receive, requiring robust communication and sufficient bandwidth. To address the challenge of promoting interconnectivity and interoperability between different smart devices are protocols such as MQTT, CoAP, HTTP and WebSockets. They facilitate network-based device communication, ensuring data sharing and coordination. The paper explores a variety of use cases and these in relation to specific IoT and SoS architectures. The objective is to highlight the transformative impact these technologies have on global performance. [ABSTRACT FROM AUTHOR]

In the context of ongoing digitalization, manufacturing companies face new challenges and need to expand their portfolios to include new digital services. This paper describes how new digital services are designed for a component manufacturer in the context of a research project. The central enabler for the new digital services is the digital twin based on the asset administration shell (AAS) that makes the necessary data available. Existing approaches start from a standardized modeling form in order to design AAS submodels based on it. This paper is about the specification of functionalities for services as a basis for further modeling and, thus, also for AAS submodels. This paper results in a logically arranged set of methods that displays this approach. The essential steps are definition of the services' basic functionalities, identification of the user groups for specific services, specification of user stories, and subsequent detailing and prioritization with the QFD method. The paper ends with an outlook on the ongoing research activities to define the application-specific AAS submodels. [ABSTRACT FROM AUTHOR]

Digital technologies such as 5G, augmented reality, and artificial intelligence (AI) are currently being used in various ways by manufacturing companies. As the fourth industrial revolution progresses, it has become apparent that reckless use and inadequate regulation of these technologies have a detrimental effect on the environment in which they are utilized. Therefore, regulation of digital technologies is imperative today to ensure more responsible and sustainable use. While governments usually establish regulations, progress is not keeping pace with the demands and hazards of employing digital technologies. The European AI law serves as an example of the considerable distance yet to be covered before binding guidelines are established. Consequently, companies must take proactive measures today to ensure that they use digital technologies responsibly in their environments. In this context, identifying which digital technologies are pertinent to manufacturing companies in terms of regulation is crucial. Furthermore, a comprehensive approach is required to design compliance holistically for digital technologies and to systematically derive the corresponding guidelines. This paper introduces a set of models that not only determine the importance of compliance in the application of different technologies but also present a framework for methodically designing compliance. Furthermore, the paper contributes to the development of an AI platform in the German research project PAIRS by investigating the compliance relevance of applications such as artificial intelligence. [ABSTRACT FROM AUTHOR]

Global competition forces assembly, production, and logistic systems to be highly efficient and agile in delivering their product-systems to customers. To achieve this, Product Lifecycle Management (PLM) and Product Data Management approaches have been developed for managing product-related information across its lifecycle. However, the full potential of PLM has yet to be realized. Industry 4.0 refers to the fourth industrial revolution which promotes the usage of information and digital production with key enabling technologies including digital twins, internet of things, cloud technologies and machine learning. This paper aims to investigate opportunities that industry 4.0 technologies can offer to realize PLM by reviewing existing scholarly papers in this context. The characteristics of industry 4.0 technologies and their usage on PLM is also discussed. As a result of such critical analysis and cross-investigation, this paper discusses the challenges with PLM and proposes future research directions and a roadmap for realization of an integrated PLM by application of key industry 4.0 technologies. [ABSTRACT FROM AUTHOR]

This paper discusses the problem of information sharing and data interoperability in a B2B context. Therefore, this paper presents a case study on the scope of data-sharing in collaborative networks in an industrial cluster. It explores the feasibility of International Data Spaces in the context of the footwear industry cluster. This work also discusses how the adoption of digital processes might contribute to support data-based management to optimize the production planning of a footwear industry. As a result, it is defined and specified the foundations for the development and implementation of an dataspace oriented IIoT architecture, following a fully compliant Industry 4.0 solution for the footwear industry cluster. [ABSTRACT FROM AUTHOR]

The industrial transformation is sociotechnical. Industry 5.0 is one of the recent terms to describe this phenomenon, defined as a humanized vision of technological transformations in industry, balancing the current and future needs of the workers and society with the sustainable optimization of energy consumption, materials processing, and product lifecycles. This paper presents a tertiary study of thirty-two literature reviews on Industry 5.0, supported by a bibliometric analysis in the Scopus database. The results show three stages of Industry 5.0 research since 2018, starting with the Industry 4.0 separation. The latest priority is to deploy circular manufacturing strategies supported by human-friendly digitalization capable of anticipating and acting proactively on impacts. Therefore, Industry 5.0 is future-oriented and cross-sectoral, (interactively) diverging from the original configuration of Industry 4.0. For theory, we highlight the findings of recent literature reviews, clarifying the landscape of Industry 5.0 research and suggesting future developments. For practice, this paper presents examples of societal priorities that industries should consider in their digital transformation investments, as crucial as improving economic competitiveness. [ABSTRACT FROM AUTHOR]

Due to the high cost of their warehousing operations, many manufacturing companies are looking to increase the efficiency of their warehouse. To this end, Industry 4.0 technologies offer several solutions to address this issue. One of them is the digital twin (DT) technology which is relatively new. This paper presents a literature review on DTs in warehousing. The analysis framework used to classify the papers includes the general description of the DT, the possible uses, and the required data. Our results show that several limitations remain to exploit the full potential of a DT. The limitations mainly concern the characterization of the data required. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to critically review the current available (large/SMEs) Maturity Models (MM) for Industry 4.0 (I4.0) adoption in manufacturing and logistics sectors, as well as recognize the specific requirements of SMEs for a MM adoption. In total, 16 MM were studied with various scopes, where 5 studies applied on SMEs. To this end, this paper identified manufacturing SMEs specific requirements, conducted a review of current MM, and identified the main MM scopes for SMEs and large enterprises as well. This study found that Smart Manufacturing, Smart Supply Chain (SC), Product Development, Digitized Data collection & Analytics, Technological Infrastructure, Smart Logistics, Performance Evaluation, and Employee participation are the main scopes used for SMEs. [ABSTRACT FROM AUTHOR]

The digitalization of companies and the implantation of Industry 4.0 concepts are emerging and challenging for micro and Small and Medium Enterprises (SME). The benefits are evident for the companies, as their business processes become simplified, and the internationalization and global market penetration turn out to be improved. However, information security and cybersecurity concerns have been raised on SME, as best practices and regulations compliance should be applied. The wide set of these regulations and their broad scope has put constraints on their overall direct mapping and adoption into SME. This paper describes an original methodology to map the Roadmap for Minimum Cybersecurity Capabilities (RMCSC) delivered by the Portuguese Cybersecurity Centre, into the well-adopted international information security ISO 27001:2013 standard. The proposed mapping is oriented toward the characteristics of SME and allows these companies to assess their cybersecurity risk to further mitigate potential identified flaws. The main deliverable of this paper is the developed methodology, which correlates the actions of the cybersecurity capabilities roadmap and the security controls enclosed in the ISO 27001:2013 standard. A questionnaire was developed to support the cybersecurity risk self-diagnosis, and the actions were justified and detailed in this paper. Further developments include the submission of the questionnaire to a case study of SME in the centre region of Portugal. [ABSTRACT FROM AUTHOR]

Predictive maintenance (PdM) aims the reduction of costs to increase the competitive strength of the enterprises. It uses sensor data together with analytics techniques to optimize the schedule of maintenance interventions. The application of such maintenance strategy requires the cooperation of several agents and involves knowledge and skills in distinct fields, since it encompasses from the averaging of relevant signals in the shop-floor to its processing, transmission, storage, and analysis in order to extract meaningful knowledge. PdM is a broad topic, making it impossible to address all its subtopics in the same paper. Having this into consideration, this paper focuses on the main challenges that hinder the development of a generalized data-driven system for PdM, namely: the existence of noisy or erroneous sensor data in a real industrial environment; the necessity to collect, transmit and process high volumes of data in a timely manner; and the fact that current approaches for PdM are specific for a part or equipment rather than global. This paper connects three different perspectives: anomaly detection, which allows the removal of noisy or erroneous data and the detection of relevant events that can be used to improve the prognostics methods; prognostics methods, which address the models to forecast the condition of industrial equipment; and the architectures, which may allow the deployment of the anomaly detection and prognostics methods in real-time and in different industrial scenarios. Furthermore, the last trends, current challenges and opportunities of each perspective are discussed over the paper. [ABSTRACT FROM AUTHOR]

This paper describes a study on future competence needs in manufacturing and how a learning factory utilising a Connective Model for Didactic Design can be used in teaching and learning of these competencies. The paper briefly reports on a literature study, and a set of interviews in Norwegian manufacturing companies to get a better understanding on the expected future competence needs. This was used to design a learning process with four steps: 1: Exploration, 2: Product and process design, 3: Problem solving and 4: Debriefing. The method was tested in a case study where undergraduate students are learners following the 4-step method. The approach was evaluated through feedback from the learners. The case utilised a Festo CP-Factory learning factory at NTNU. [ABSTRACT FROM AUTHOR]

This paper investigates the transformation of information sharing and multi-tier supply chain management (MSCM) from the perspective of small and medium-sized enterprises (SMEs) in the context of Industry 4.0. SMEs are typically sharing less information digitally, are less active in conducting MSCM, but must be integrated in digital supplier networks to achieve supply chain transparency or traceability. For this purpose, this paper empirically analyzes SMEs from the manufacturing industry in Austria and Germany are investigated regarding information sharing and MSCM. The responses are analyzed to test three hypotheses, using 81 replies from manufacturing SMEs in Austria and Germany. The three hypotheses investigate the upstream and downstream MSCM practices of SMEs as well as which kind of information is shared with and by SMEs. The results help to understand and reveal potential barriers for information sharing besides technical possibilities to SMEs in order to be actively involved in MSCM. [ABSTRACT FROM AUTHOR]

Industry has entered a new age of industrial change which is revolutionising how products are manufactured. Driving this industry change are newly developing digital technologies such as robotics, artificial intelligence, advanced analytics and the industrial internet. This convergence of technology and manufacturing is driving the digital transformation of every industrial segment from operations to logistics, and from aeronautics to retail product manufacture. The technology advances evident from Industry 4.0 will result in industry disruption and build competitive advantages for the organisations who can master the technologies in their manufacturing processes. However, organisations are struggling to develop a tactical methodology to introduce these technologies while simultaneously transforming their organisation cultures and organisations and maximising the benefits. One of the key reasons for the Fourth Industrial Revolution called Industry 4.0 is the need to strengthen the competitiveness of Western European economies, which as a result of the progressing globalization process and rising labor and business costs [1]. A key benefit of the introduction of Industry 4.0 initiatives in a manufacturing plant is to help overcome current operations management limitations, such as, the lack of knowledge on how the process is performing at a specific point in time. This paper will investigate the impact of deploying a range of Industry 4.0 technologies to access machine and operations performance data, to improve equipment reliability and to reduce the costs associated with the maintenance of equipment. With the introduction of the operational data analytics as a result of data extraction from manufacturing equipment, along with integrating other data sources, the business has realised improvements in key metrics like OTIF (On Time in Full), OEE (Overall Equipment Effectiveness), MTBF (Mean Time Between Failure), MTTR (Mean Time to Return), CuC (Consumable Unit Cost), and Lead Time. This paper discusses this project and the results obtained at Zimmer Biomet while also discussing the research carried out for ZOML to begin its journey on manufacturing digitalisation. The paper starts with a background in Industry 4.0. The next section provides a background on the company where the problem statement and project have come from with some detail on their digitalisation journey. The following section details the problem and the project that the research is derived from and the final section is in relation to the results of the research and the project. [ABSTRACT FROM AUTHOR]

IT-systems supporting business operations are a cornerstone of industry 4.0 and smart manufacturing. IT-systems must not be considered as an end in itself. The enterprise IT architecture must be aligned with the business goals and strategy. Current approaches for designing enterprise IT architectures provide little guidance for greenfield environments in which there are little given boundaries, such as operation procedures. In smart manufacturing this is challenging due to the complexity of the shop floor involving OT and IT. This paper addresses this gap by combining the TOGAF architecture development method with the manufacturing operations management standard IEC 62264. The paper explores this combination by the example of designing an enterprise IT architecture for the Fraunhofer battery manufacturing research line involving the TOGAF phases architecture vision, business architecture, information systems architecture and technology architecture for manufacturing. [ABSTRACT FROM AUTHOR]