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Blockchain and the Internet of Things (IoT) are separately regarded as highly capable popular technologies. Blockchain is a database used for decentralized transaction purposes. It provides novel directions to store and manage data, whereas IoT relates to the propagation of linked machines by providing information through the Internet. A mixture of Blockchain and the IoT appears hopeful, even though blockchain requires real-time data application, and IoT describes processes to store and manage information overloads safely and proficiently. The technology is significant to the manufacturing business, experiencing a digital revolution by incorporating equipment, developments, and data, leading to the Industrial IoT (IIoT). Such a combination of IIoT and blockchain is termed Blockchain for Industrial Internet of Things (BIIoT). The paper gives a detailed survey on BIIoT and discusses all relevant aspects of this novel concept. Firstly, IoT as a concept has been explored briefly, and relevant threats to IoT has been investigated. Furthermore, the domain of IIoT and the relevant challenges to IIoT is discussed. Additionally, an overview of blockchain technology is presented. Subsequently, a combination of IIoT and blockchain is explored, and the BIIoT structural design proposal is submitted. Finally, issues related to the use of IIoT with blockchain for industrial applications of BIIoT is explored. Thus, all relevant open research directions in Blockchain and IIoT are summarized in this paper. This survey shows that the proposed BIIoT is used to develop a redundant, traceable, and secure complex interconnected IIoT environment. Furthermore, the BIIoT system enables us to communicate with each other without the need for a trusted intermediary in a decentralized, unreliable, peer-to-peer network.

Composites have become the go-to material of the aerospace industry during the past decades and a significant uptake in composite materials for aerospace applications was evident in recent years. Both expert academics and industry practitioners believe, to meet the future demand, the level of automation in the aerospace composite manufacturing process chains must be improved. The main focus of automation in composites so far has been given to automate siloed operations but limited attention has been paid to end-to-end integration of the process chains leading to inefficiencies, rising operational costs, and low productivity. This paper intends to compare and contrast the level of automation (LOA) in different aerospace composite manufacturing process chains to identify where the LOA triumphs and lacks. For this purpose, core-process and sub-process tasks involved in commonly used manufacturing process chains (i.e. Filament Winding, Automated Tape Layup, Automated Fiber Placement, Resin Transfer Molding, and Pultrusion) are identified by conducting a detailed literature review and verified by the experts. Then, the process chains are mapped and visualized to understand the workflow. Later, these tasks are evaluated based on an established LOA taxonomy developed for manufacturing processes. The study reveals that even the popular ‘automated’ processes are developed in silos and do not show consistent higher LOA throughout their process chain. While core-process tasks show intermediate LOA (Level 5–6), most non-value-added activities show poor LOA (Level 1–4). Most importantly, none of the tasks involved in the existing composite manufacturing process chains have reached a higher LOA (Level 7). The paper reveals that focusing on sub-process tasks, and tasks that lack automation should be the next step towards achieving fully automated composite manufacturing and presents a two-pronged approach to realize Industry 4.0.

3D printing, unlike other manufacturing processes, being an additive process has emerged as a viable technology for the production of engineering components. The aspects associated with 3D printing such as less material wastage, ease of manufacturing, less human involvement, very less post processing and energy efficiency makes the process sustainable for industrial use. The paper discusses numerous 3D printing processes, their advantages and disadvantages. A comprehensive description of different materials compatible for each type of 3D printing process is presented. The paper also presents the various application areas of each type of process. A dedicated section on industry 4.0 has also been included. The literature studied revealed that although the field of 3D printing has evolved to a great extent, there are still issues that need to be addressed such as material incompatibility and the cost of the materials. Future research could be undertaken to develop and modify the processes to suit a broad range of materials. To broaden the range of applications for 3D printed parts, more focus needs to be laid on developing cost effective printer technologies and materials compatible for these printers.

Purpose The involvement of wear, friction and lubrication in engineering systems and industrial applications makes it imperative to study the various aspects of tribology in relation with advanced technologies and concepts. The concept of Industry 4.0 and its implementation further faces a lot of barriers, particularly in developing economies. Real-time and reliable data is an important enabler for the implementation of the concept of Industry 4.0. For availability of reliable and real-time data about various tribological systems is crucial in applying the various concepts of Industry 4.0. This paper aims to attempt to highlight the role of sensors related to friction, wear and lubrication in implementing Industry 4.0 in various tribology-related industries and equipment. Design/methodology/approach A through literature review has been done to study the interrelationships between the availability of tribology-related data and implementation of Industry 4.0 are also discussed. Relevant and recent research papers from prominent databases have been included. A detailed overview about the various types of sensors used in generating tribological data is also presented. Some studies related to the application of machine learning and artificial intelligence (AI) are also included in the paper. A discussion on fault diagnosis and cyber physical systems in connection with tribology has also been included. Findings Industry 4.0 and tribology are interconnected through various means and the various pillars of Industry 4.0 such as big data, AI can effectively be implemented in various tribological systems. Data is an important parameter in the effective application of concepts of Industry 4.0 in the tribological environment. Sensors have a vital role to play in the implementation of Industry 4.0 in tribological systems. Determining the machine health, carrying out maintenance in off-shore and remote mechanical systems is possible by applying online-real-time data acquisition. Originality/value The paper tries to relate the pillars of Industry 4.0 with various aspects of tribology. The paper is a first of its kind wherein the interdisciplinary field of tribology has been linked with Industry 4.0. The paper also highlights the role of sensors in generating tribological data related to the critical parameters, such as wear rate, coefficient of friction, surface roughness which is critical in implementing the various pillars of Industry 4.0.

Purpose Unmanned aerial vehicles are commonly known as UAVs and drones. Nowadays, industries have begun to realise the operational and economic benefits of drone-enabled tasks. The Internet of Things (IoT), Big Data, drones, etc., represent implementable advanced technologies intended to accomplish Industry 4.0. The purpose of this study is to discuss the significant contributions of drones for Industry 4.0. Design/methodology/approach Nowadays, drones are used for inspections, mapping and surveying in difficult or hazardous locations. For writing this paper, relevant research papers on drone for Industry 4.0 are identified from various research platforms such as Scopus, Google Scholar, ResearchGate and ScienceDirect. Given the enormous extent of the topic, this work analyses many papers, reports and news stories in an attempt to comprehend and clarify Industry 4.0. Findings Drones are being implemented in manufacturing, entertainment industries (cinematography, etc.) and machinery across the world. Thermal-imaging devices attached to drones can detect variable heat levels emanating from a facility, trigger the sprinkler system and inform emergency authorities. Due partly to their utility and adaptability in industrial areas such as energy, transportation, engineering and more, autonomous drones significantly impact Industry 4.0. This paper discusses drones and their types. Several technological advances and primary extents of drones for Industry 4.0 are diagrammatically elaborated. Further, the authors identified and discussed 19 major applications of drones for Industry 4.0. Originality/value This paper’s originality lies in its discussion and exploration of the capabilities of drones for Industry 4.0, especially in manufacturing organisations. In addition to improving efficiency and site productivity, drones can easily undertake routine inspections and check streamlines operations and maintenance procedures. This work contributes to creating a common foundation for comprehending Industry 4.0 outcomes from many disciplinary viewpoints, allowing for more research and development for industrial innovation and technological progress.

Purpose Augmented reality (AR) integrates the digital world with the real world and thus, provides a real-time experience to the users. With AR, the immediate surroundings become a learning platform for the users. The perception of the products has been enhanced many times with AR; thus, enriching user experience and responsiveness. The purpose of this paper is to bring forth the basics of AR and provide an overview of the research work carried out by researchers in the implementation of AR in different sectors. Design/methodology/approach This paper summarizes the usefulness of AR in different industries. The authors have identified the peer-reviewed research publications from Web of Science, Scopus, Google Scholar, etc. The selection of literature has been made based upon the significance of AR in recent times. The industries/sectors where AR has been implemented successfully have been considered for this paper. The paper has been divided into various sections and subsections to bring more clarity to the readers. Findings This paper presents a brief and a precise information on Industry 4.0 and AR. The basic working of AR system and its implications have also been discussed. The preference of AR over virtual reality (VR) has also been deliberated in this paper. The authors have presented the usefulness of AR in different sectors such as smart factories, ship yard building, online shopping, surgery and education. This paper discusses the AR-ready procedures being followed in these sectors. Originality/value AR has been an add-on to VR systems. The processes in industries have become very handy and informative with AR. Because the application of AR in different sectors has not been discussed in a single paper; thus, this work presents a systematic literature review on the applications of AR in different sectors/industries.

With the advent of Industry 4.0, economic development has become a rapid information age. The content of macroeconomic forecast is very extensive, and the existence of big data technology can provide the government with multilevel, diversified, and complete information and comprehensively process, integrate, summarize, and classify these pieces of information. This paper forecasts the CPI value in the next 12 months according to the CPI in China in the recent 20 years. Compared with the traditional forecasting methods, the forecasting results have higher accuracy and timeliness. At the same time, the trend of growth rate of industrial value-added is analyzed, and the experiments on MAE and RMSE show that the method proposed in this paper has obvious advantages. It also analyzes the disadvantages of traditional psychological decision-making behavior analysis, introduces the development status and advantages of big data-driven psychological decision-making behavior analysis, and opens up new research ideas for psychological decision-making analysis.

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 f...

Nowadays, Industry 4.0 (I4.0) has become a trendy topic in manufacturing industries worldwide. The definition is far from being comprehensible for small players, and the practical uptake is ambiguous. Transnational companies are often at the top in deploying I4.0 features, learning primarily from their experimentation. Alternatively, small- to medium-sized enterprises (SMEs), given their less stable value chains and unsteady processes, tend to target most of their efforts on controlling disturbances and adopting solutions for deviation control. Such solutions can be features that set the path for SMEs to transition to I4.0. This paper aims to examine the reported degree of digitalization in implemented solutions in SMEs when handling deviations and analyze the integration of such solutions in their digital transformation process. Systematic literature review (SLR) is used to examine literature published up to and including January 2019. The results show a higher concentration on practical applications rather than on frameworks. Existing frameworks that focus on SMEs address particular elements of I4.0 rather than a gradual shift with a holistic view, increasing the deployment difficulty for SMEs. This paper identifies potential constraints in deployment if such a trend maintains for consecutive years.

Purpose This paper aims to put forward a labelling system capable of reflecting the level of different Industry 4.0 (I4.0)features present in a manufacturing system and further propose a comparative index to collectively estimate and compare the system automation level. Design/methodology/approach Data for the empirical study were collected from interactions with the practising managers and experts. A relationship among the six I4.0 features is developed with fuzzy cognitive maps. Findings The paper proposed a simple and easy-to-understand labelling system for I4.0 systems, which indicates the automation level in each of six dimensions of any manufacturing system. The system is further strengthened by a proposed automation comparative index (ACI), which collectively reflects the automation level on a scale of “0” to “1”. Thus, the labelling system and parameter could help in comparing the level of automation in the manufacturing system and further decision-making. Research limitations/implications Only seven industrial sectors are illustrated in the paper, but the proposed concept of the classification scheme and ACI find their applicability on a large spectrum of industries; thus, the concept can be extended to other industrial sectors. Furthermore, a threshold value of ACI is a differentiator between a I4.0 and other automated systems. Both aspects have the scope of further work. Practical implications The way and pace by which the industrial world takes forward the concept of I4.0, soon it will need a labelling system and a parameter to assess the automation level of any automated system. The scheme assesses the automation level present in a manufacturing system. It will also estimate the level of the presence of each of all six attributes of an I4.0 system. Both labelling system and ACI will be the practical tools in the hands of the practising managers to help compare, identify the thrust areas and make decisions accordingly. Originality/value To the best of the authors’ knowledge, this is the first study of its kind that proposed the labelling system and automation comparison index for I4.0 systems.

Fillet welding is one of the most widespread types of welding in the industry, which is still carried out manually or automated by contact. This paper aims to describe an online programming system for noncontact fillet welding robots with U and L shaped structures, which responds to the needs of the Fourth Industrial Revolution. In this paper, the authors propose an online robot programming methodology that eliminates unnecessary steps traditionally performed in robotic welding, so that the operator only performs three steps to complete the welding task. First, choose the piece to weld. Then, enter the welding parameters. Finally, it sends the automatically generated program to the robot. The system finally managed to perform the fillet welding task with the proposed method in a more efficient preparation time than the compared methods. For this, a reduced number of components was used compared to other systems, such as, a structured light 3D camera, two computers and a concentrator, in addition to the six axis industrial robotic arm. The operating complexity of the system has been reduced as much as possible. To the best of the authors knowledge, there is no scientific or commercial evidence of an online robot programming system capable of performing a fillet welding process, simplifying the process so that it is completely transparent for the operator and framed in the Industry 4.0 paradigm. Its commercial potential lies mainly in its simple and low cost implementation in a flexible system capable of adapting to any industrial fillet welding job and to any support that can accommodate it., 11 pages, 8 figures

PurposeThe objective of the work is to analyze the impact of the Internet of Things (IoT) concepts and associated technologies in the framework of organizations and the management of their processes and how event orientation, as well as the structure of said business processes, can play an important role in this new organizational model. The main contribution of this work is to present a conceptualization of the research, identify approaches and challenges that require further study, and as a result, a proposal for future research.Design/methodology/approachThe methodology comprises a qualitative analysis using secondary data. The approach relies on searches of scientific papers conducted in well-known databases, identifying research work around the IoT and Industry 4.0 applied to business process management. Based on the identified papers, the authors selected the most relevant and the latest publications, and categorized their contributions and findings based on open and selective coding. In total, the analysis is based on 95 papers that were selected and analyzed in depth.FindingsThe results of this research allow analyzing and ordering the existing contributions around Industry 4.0 and its impact on current organizations. The proposed conceptualization was derived from the analysis of the state of the research and identifies four categories: (1) improvements caused by Industry 4.0 and its impact on inter-organizational relations, (2) new architectural models and infrastructure of remote resources, their movement from the cloud to the edge and its effect on business processes, (3) context-aware concepts brought to business process management (BPM) linked to unstructured business processes and (4) complex event processing as a possible means for business processes sensitive to IoT signals.Practical implicationsThe construction of current software ecosystems is strongly affected by the variety of information sources that feed them, as well as their volume. In addition, business processes represent organizations internally and are challenged to transcend the limits of companies due to the mentioned changes in software ecosystems. Industry 4.0 in conjunction with BPM re-defines the business process management paradigm and leads them to acquire the dynamism and sensitivity to the context that they usually did not have, as well as force them to move toward distributed platforms.Originality/valueThis paper assesses the state of the art in Industry 4.0 and business process management. The area can be defined as the intersection of two bigger areas highly relevant for organizations; on the one hand, the management and execution of business processes; and on the other hand, new conceptual, technological and methodological challenges to information systems that have to become more sensitive to event processing and also have to consume a large volume of data permanently and ubiquitously.

In this paper, we propose to design an indoor sign detection system for industry 4.0. In order to implement the proposed system, we proposed a lightweight deep learning-based architecture based on MobileNet which can be run on embedded devices used to detect and recognize indoor landmarks signs in order to assist blind and sighted during indoor navigation. We apply various operations in order to minimize the network size as well as computation complexity. Internet of things (IoT) presents a connection between internet and the surroundings objects. IoT is characterized to connect physical objects with their numerical identities and enables them to connect with each other. This technique creates a kind of bridge between the physical world and the virtual world. The paper provides a comprehensive overview of a new method for a set of landmark indoor sign objects based on deep convolutional neural network (DCNN) for internet of things applications.

In this paper, the cooperation between human and robot companies plays a significant role in factories, contributing to greater productivity and efficiency. However, this development breaches established safety procedures when the workspaces are separated from the robot and the human being. These changes have been reflected in industrial robotic safety standards for the last 20 years. We have directed the expansion of a broad field of examination, which focuses on avoiding robotic humans’ effects and minimizing associated risks and consequences. The paper depicts an analysis of prominent safety systems projected and implemented in engineering robotic surroundings that contribute to safe, collective work between humans and robots. Besides, the current regulation has introduced a review and new concepts. The discussion includes multidisciplinary approaches such as estimating and evaluating human-robot collision injuries, mechanical equipment and software to minimize human-robot impacts, impact detection systems, and collision prevention strategies and minimizing their impact to proposed approach for Human Security with Mobile Robots in Industry 4.0 using SDN and CPS with GMM-GM machine learning model.

PurposeThis paper aims to develop a conceptual framework of the implementation of the contact points (CPs) between Lean Six Sigma practices and Industry 4.0 technologies.Design/methodology/approachA systematic literature review was carried out based on two samples. A first sample containing 78 articles was analyzed through bibliometric indicators. After that, a second sample of 33 articles was analyzed in-depth according to research questions.FindingsThe conceptual framework involves 13 CPs between Lean Six Sigma (LSS) practices and I4.0 technologies (what), going through the technical requirements needed (how), categorized as information technology (IT), automation and competence requirements, to finally present the main results reported in the literature (why).Research limitations/implicationsThis paper presents an innovative perspective of interactions between digital technologies and LSS practices, expanding knowledge about Digital LSS. Such perspective gives emphasis to the importance of technical requirements, such as communication and connectivity protocols, network topology, machine-to-machine communication (M2M), human–machine interfaces (HMI), as well as analytical and digital skills.Practical implicationsThe managerial implications regarding the digitalization of LSS practices address the investments required for the acquisition and maintenance of cyber-physical systems (CPS). Moreover, there is a need for the development of skills so that operators can successfully use the new technologies in a context of continuous improvement.Originality/valueThis paper presents a conceptual framework covering 13 CPs between LSS practices and Industry 4.0 technologies, the technical requirements and the expected results. It is hoped that this framework can assist future research and operational excellence projects towards digitalization.

“Industrie 4.0” or the Industrial Internet of Things (IIoT) are two terms for the current (r)evolution seen in industrial automation and control. Everything is getting smarter and data generated at all levels of the production process are used to improve product quality, flexibility, and productivity. This would not be possible without smart sensors, which generate the data and allow further functionality from self-monitoring and self-configuration to condition monitoring of complex processes. In analogy to Industry 4.0, the development of sensors has undergone distinctive stages culminating in today's smart sensors or “Sensor 4.0”. This paper briefly reviews the development of sensor technology over the last 2 centuries, highlights some of the potential that can be achieved with smart sensors and data evaluation, and discusses success requirements for future developments. In addition to magnetic sensor technologies which allow self-test and self-calibration and can contribute to many applications due to their wide spectrum of measured quantities, the paper discusses condition monitoring as a primary paradigm for introducing smart sensors and data analysis in manufacturing processes based on two projects performed in our group.

Industry 4.0 systems in general and advanced manufacturing systems such as collaborative robots, in particular, are characterized by a high level of complexity leading to new safety concerns. Safety, specifically for collaborative robots, has been mainly addressed from a technical perspective, to safeguard the physical safety of the operator. Concerns have been raised regarding less focus in Industry 4.0 literature on how other factors, such as psychosocial can produce safety-related risks for the operator in human-robot collaboration. This paper identifies and classifies the risk factors in a human-robot collaboration that have been described in research papers in the last decade. The resulting five classes constitute dimensions that will be used as preliminary building blocks for a safety evaluation framework to be developed in the next step. By evaluating the resulting classes with the underlying dimensions of contemporary socio-technical thinking, this paper demonstrates that these five classes offer a comprehensive, system-wide perspective including risk factors beyond technological considerations. Topics emerging from new risks related to the impact of working with collaborative robots, such as psychosocial, ethical, and cyber risk factors will need to be taken into account in the risk factors that are important to identify, assess and mitigate before working with collaborative robots. Operator involvement and participation, especially throughout the risk assessment and mitigation cycle are recommended as new areas of attention in human-robot collaboration. Going forward, one challenge will be the agility and adaptability of legislation to at least keep track of risk factors emerging from continuously changing technologies and to translate them into practically applicable tools for enterprises and design engineers implementing collaborative applications. Another key challenge will be the measurement of the new emerging and sometimes less technological risks.

A technologically based society geared towards every human being represents a Society 5.0 environment whose goal is to upgrade society to improve daily human life. It can be achieved through the significant application of information and communication networks, technologies, services, and user equipment. Industry 4.0 is the basis for developing the Society 5.0 environment in which the harmonious coexistence of people and machines comes to the fore. Artificial intelligence is one of the critical technologies for the further growth and development of the Society 5.0 environment, whose ability is to convert collected networked stakeholder's data into values. The authors in this paper systematically review the literature to investigate the application of artificial intelligence technology within the Society 5.0 environment. This paper aims to present the possibilities and scenarios of using artificial intelligence in different areas of the Society 5.0 environment, such as the digital transport system, healthcare, and manufacturing.

Digitalisation of manufacturing is a crucial component of the Industry 4.0 transformation. The digital twin is an important tool for enabling real-time digital access to precise information about physical systems and for supporting process optimisation via the translation of the associated big data into actionable insights. Although a variety of frameworks and conceptual models addressing the requirements and advantages of digital twins has been suggested in the academic literature, their implementation has received less attention. The work presented in this paper aims to make a proposition that considers the novel challenges introduced for data analysis in the presence of heterogeneous and dynamic cyber-physical systems in Industry 4.0. The proposed approach defines a digital twin simulation tool that captures the dynamics of a machining vibration signal from a source model and adapts them to a given target environment. This constitutes a flexible approach to knowledge extraction from the existing manufacturing simulation models, as information from both physics-based and data-driven solutions can be elicited this way. Therefore, an opportunity to reuse the costly established systems is made available to the manufacturing businesses, and the paper presents a process optimisation framework for such use case. The proposed approach is implemented as a domain adaptation algorithm based on the generative adversarial network model. The novel CycleStyleGAN architecture extends the CycleGAN model with a style-based signal encoding. The implemented model is validated in an experimental scenario that aims to replicate a real-world manufacturing knowledge transfer problem. The experiment shows that the transferred information enables the reduction of the required target domain data by one order of magnitude.

The growing diffusion of digital technologies, especially in production systems, is leading to a new industrial paradigm, named Industry 4.0 (I4.0), which involves disruptive changes in the way companies organize production and create value. Organizations willing to seize the opportunities of I4.0 must thus innovate their processes and business models. The challenges that companies must face for the transition towards I4.0 paradigm are not trivial. Several digital transformation models and roadmaps have been lately proposed in the literature to support companies in such a transition. The literature on change management stresses that about 70% of change initiatives—independently of the aim—fail to achieve their goals due to the implementation of transformation programs that are affected by well-known mistakes or neglect some relevant aspects, such as lack of management support, lack of clearly defined and achievable objectives and poor communication. This paper investigates whether and to what extent the existing digital transformation models (DTMs) and roadmaps for I4.0 transition consider the lessons learnt in the field of change management. To this aim, a Systematic Literature Review to identify existing models and roadmaps is carried out. The results obtained by the review are discussed under the lens of the change-management literature. Based on that, the shortcomings and weaknesses of existing DTMs are pinpointed. Extant DTMs mainly focus on digital transformation initiatives carried out in manufacturing companies; they do not cover all the phases of the digital transformation process but rather focus on the definition of the I4.0 vision, strategy and roadmap. Little attention is devoted to the implementation and consolidation of digital change. Change management lessons are considered to a limited extent, based on which, some suggestions for better dealing with digital transformation initiatives are discussed. The paper contributes to advancing knowledge on models and approaches to support organizations in managing digital transformation. The identification of change management activities that a digital transformation initiative should involve as well as the suggestions on how to effectively deal with it can be used by managers to successfully lead the I4.0 transition journey in their organizations.

The article takes the case of the adoption of machine learning in a steel manufacturing process through a platform provided by a novel Canadian startup, Canvass Analytics. This way the steel company could optimize the process in a blast furnace. The content of the paper includes a conceptual framework on key factors around steel manufacturing and machine learning. Method: The article takes the case of the adoption of machine learning in a steel manufacturing process through a platform provided by a novel Canadian startup, Canvass Analytics. This way the steel company could optimize the process in a blast furnace. The content of the paper includes a conceptual framework on key factors around steel manufacturing and machine learning. Results: This case is relevant for the authors by the way the business model proposed by the startup attempts to democratize Artificial Intelligence and Machine Learning in industrial environments. This way the startup delivers value to facilitate traditional industries to obtain better operational results, and contribute to a better use of resources. Conclusion: This work is focused on opportunities that arise around Artificial Intelligence as a driver for new business and operating models. Besides the paper looks into the framework of the adoption of Artificial Intelligence and Machine Learning in a traditional industrial environment towards a smart manufacturing approach.

The primary purpose of this paper is to present a system intended for student education based on a microcontroller platform and a cloud system where data will be stored. Obtained research results will be based on the application of The NodeMcu platform for data collection with sensor systems. MatLab, ThingSpeak cloud system, and the Virtuino platform will be used for data presentation. Quantitative data are applied to determine conditions for agriculture system performance management. By analyzing the literature and the current situation in monitoring and managing in the agriculture and ecology field, it can be concluded that there are no special education courses regarding these issues. This knowledge was a good starting point for the research presented in this paper. Accordingly, the findings include developing a monitoring and management system intended for student education in mentioned fields. In addition, the practical implications of this research includes the acquisition of information related to student satisfaction with this method of education in the courses of engineering, agriculture, and ecology. The presented system may enable benchmarking, simulation, and verification of different scenarios to improve students’ knowledge regarding sensors’ application in natural conditions. The originality of the research lies in the presented software solution that can be made available to educational institutions and other scientific institutions to serve as a basis in the overall monitoring and management of agricultural and ecology parameters.

Industry 4.0 challenges facilities entrepreneurs to be competitive in the market in terms of energy by rational decision making. The goal of the paper is aimed at introducing Prospect Theory (PT) in Industry 4.0 for making decisions in order to select an optimal energy technology. To reach this goal, an approach for decision making on energy investment has been developed. In this paper, the authors have also provided a new opportunity to apply the new decision making method for strengthening Industry 4.0 by addressing energy concerns based on which rational decisions have been made. The study uses a fuzzy analytical hierarchy process for weighting the evaluation sub-criteria of energy technologies and a modified PT for making decisions related to the selection of one of the investigated technologies. The results show that it is possible to implement PT in Industry 4.0 via a decision making model for energy sustainability. Decision probability was achieved using a behavioral approach akin to Cumulative Prospect Theory (CPT) for the considered technology options. More specifically, the probability has created the same threshold-based decision possibilities. The authors used the case study method based on a company located in North America which produces hardwood lumber. The company uses a heating system containing natural gas-fired boilers. This study has also contributed to the literature on energy sustainable Industry 4.0 by demonstrating a new phenomenon/paradigm for energy sustainability-based Industry 4.0 through using PT. In this context, the main motivation of writing the article has been to promote energy sustainability via complex mechanisms and systems that involve interrelated functions.

Nowadays, the need for energy conserving in industries is in big rise. To meet the demand, various technologies have been adopted to fit industrial standards. This paper relies on the modern standard 4.o conventions used in critical industrial applications at each point. In Internet of Things (IoT), the use of short packets is expected to meet the stringent latency requirement in ultra-reliable low-latency communications network. In view of the reaction of ping production, idleness and throughput and analyzing their upgraded exhibition in companies such as oil and gas, energy utility, media transmission and some more industries are carried out. A number of application layer protocols have been considered in order to accommodate a wide range of applications. HTTP, a well-known, fundamental client–server protocol and the protocol that is most compatible with current network infrastructure, is one of the leading candidate solutions in this paper. In this paper, the Trial outcomes are acquired through demo industrial setup and recognized through Message Queuing Telemetry Transport (MQTT) and resultant checked through Wire shark and LCD in real-time.

Industry 4.0 has transformed manufacturing systems by introducing new functionalities that improve manufacturing processes' operational efficiency and allow mass customization under intelligent software control. The visible development in the Internet of Things (IoT) and Wireless Sensor Networks (WSN) has made INDUSTRY 4.0 significantly affect the manufacturing industry in all aspects, including logistics to quality control. Manufacturing industries will no longer take quality management tests in a separate metrology segment but instead on the manufacturing line in real-time. Quality control systems have progressed technologically in recent years, especially through laser or optical measurements. However, these systems still suffer from some incompatibilities with production systems 4.0, especially in terms of interoperability and information exchange, which are the guiding design principles for industrialization 4.0 initiatives. Quality control is an important phase of the manufacturing process, and fully integrating it into a production line is a significant challenge for intelligent and autonomous execution. In this paper, a use case regarding quality control and assurance in the production platform 4.0 is described to present how a QC system could be integrated into a manufacturing system and compatible with the industry 4.0 principles. The quality control process is performed by implementing a 3D scanner, the Faro 3D imager Cobalt, and software named PolyWorks-InspectorTM installed on a host computer to which the scanner is also connected. Therefore, this paper presents an application of the concept of quality 4.0 in advanced manufacturing systems.

[EN] Within the scheduling framework, the potential of digital twin (DT) technology, based on virtualisation and intelligent algorithms to simulate and optimise manufacturing, enables an interaction with processes and modifies their course of action in time synchrony in the event of disruptive events. This is a valuable capability for automating scheduling and confers it autonomy. Automatic and autonomous scheduling management can be encouraged by promoting the elimination of disruptions due to the appearance of defects, regardless of their origin. Hence the zero-defect manufacturing (ZDM) management model oriented towards zero-disturbance and zero-disruption objectives has barely been studied. Both strategies combine the optimisation of production processes by implementing DTs and promoting ZDM objectives to facilitate the modelling of automatic and autonomous scheduling systems. In this context, this particular vision of the scheduling process is called smart manufacturing scheduling (SMS). The aim of this paper is to review the existing scientific literature on the scheduling problem that considers the DT technology approach and the ZDM model to achieve self-management and reduce or eliminate the need for human intervention. Specifically, 68 research articles were identified and analysed. The main results of this paper are to: (i) find methodological trends to approach SMS models, where three trends were identified; i.e. using DT technology and the ZDM model, utilising other enabling digital technologies and incorporating inherent SMS capabilities into scheduling; (ii) present the main SMS alignment axes of each methodological trend; (iii) provide a map to classify the literature that comes the closest to the SMS concept; (iv) discuss the main findings and research gaps identified by this study. Finally, managerial implications and opportunities for further research are identified., This work was supported by the Spanish Ministry of Science, Innovation and Universities project entitled 'Optimisation of zero-defects production technologies enabling supply chains 4.0 (CADS4.0) ' (RTI2018-101344-B-I00) , the European Union H2020 research and innovation programme with grant agreement No. 825631 "Zero Defect Manufacturing Platform (ZDMP) " and the European Union H2020 research and innovation programme with agreement No. 958205 "In-dustrial Data Services for Quality Control in Smart Manufacturing (i4Q) ".

In the Industry 4.0 era, manufacturers strive to remain competitive by using advanced technologies such as collaborative robots, automated guided vehicles, augmented reality support and smart devices. However, only if these technological advancements are integrated into their system context in a seamless way, they can deliver their full potential to a manufacturing organization. This integration requires a system architecture as a blueprint for positioning and interconnection of the technologies. For this purpose, the HORSE framework, resulting from the HORSE EU H2020 project, has been developed to act as a reference architecture of a cyber-physical system to integrate various Industry 4.0 technologies and support hybrid manufacturing processes, i.e., processes in which human and robotic workers collaborate. The architecture has been created using design science research, based on well-known software engineering frameworks, established manufacturing domain standards and practical industry requirements. The value of a reference architecture is mainly established by application in practice. For this purpose, this paper presents the application and evaluation of the HORSE framework in 10 manufacturing plants across Europe, each with its own characteristics. Through the physical deployment and demonstration, the framework proved its goal to be basis for the well-structured design of an operational smart manufacturing cyber-physical system that provides horizontal, cross-functional management of manufacturing processes and vertical control of heterogeneous technologies in work cells. We report on valuable insights on the difficulties to realize such systems in specific situations. The experiences form the basis for improved adoption, further improvement and extension of the framework. In sum, this paper shows how a reference architecture framework supports the structured application of Industry 4.0 technologies in manufacturing environments that so far have relied on more traditional digital technology.

This research work is focused towards identification of application of components of industry 4.0 such as Internet of Things (IoT), machine learning, big data, and blockchain to supply chain management (SCM) to address the current research gaps as well as to identify the potential of industry 4.0 in developing real time decision making smart production process. In this paper a discussion is presented about problems that are being faced in existing systems such as system complexity, scalability issues, operational cost and difficulty in decision making in dynamic condition of demand. This paper also presented the impacts of components of industry 4.0 in SCM. Along with that, a six-layer architecture integrating these components are also presented for smart production planning and decision making.

Following the Industry 4.0 paradigm, many industrial companies capture high volume of time series data from their industrial processes. A common task to generate value from this data is to visualize and analyze it. However, regular visualization approaches of this data require specialized hardware. Thus, downsampling algorithms as M4 are required to create a simplified view of the original data, which requires less computation power to be visualized while keeping as much information as possible from the original data. Although industrial processes involve synchronized variables that should be visualized together for their analysis, existing downsampling algorithms tackle visualization of univariate data series. Moreover, most of these algorithms require regularly sampled intervals, while data from many industrial processes does not fulfil this condition. This paper addresses these issues. The paper proposes an adaptive extension of the M4 algorithm suitable for multivariate datasets. The algorithm has been successfully tested with synthetic multivariate time series and commodity hardware, validating its suitability for the visualization and analysis of time series from industrial processes.

This study investigates the application of a smart manufacturing execution system (SMES) based on the current controlling structure in a medium-sized company in the Czech Republic. Based on existing approaches on the architecture of SMESs, this paper develops a sample architecture grounded in the current controlling structure of small and medium-sized enterprises (SMEs). While only a few papers on approaches to the given topic exist, this approach makes use of operative production controlling data and uses a standardisation module to provide standardised data. The sample architecture was validated with a case study on a Czech SME. This case study was conducted on two different entities of one production company suggesting differences in the entities due to the nature of production. The research showed that simple tasks with intelligent welding equipment allow for a working SMES architecture, while complex assembly works with a high extent of human labour, and a high number of components still remain an obstacle. This research contributes to gathering more understanding of SMES architectures in SMEs by making use of a standardisation module.

The Fourth Industrial Revolution drives industries from traditional manufacturing to the smart manufacturing approach. In this transformation, existing equipment, processes, or devices are retrofitted with some sensors and other cyber-physical systems (CPS), and adapted towards digital production, which is a blend of critical enabling technologies. In the current scenario of Industry 4.0, industries are shaping themselves towards the development of customized and cost-effective processes to satisfy customer needs with the aid of a digital twin framework, which enables the user to monitor, simulate, control, optimize, and identify defects and trends within, ongoing process, and reduces the chances of human prone errors. This paper intends to make an appraisal of the literature on the digital twin (DT) framework in the domain of smart manufacturing with the aid of critical enabling technologies such as data-driven systems, machine learning and artificial intelligence, and deep learning. This paper also focuses on the concept, evolution, and background of digital twin and the benefits and challenges involved in its implementation. The Scopus and Web of Science databases from 2016 to 2021 were considered for the bibliometric analysis and used to study and analyze the articles that fall within the research theme. For the systematic bibliometric analysis, a novel approach known as Proknow-C was employed, including a series of procedures for article selection and filtration from the existing databases to get the most appropriate articles aligned with the research theme. Additionally, the authors performed statistical and network analyses on the articles within the research theme to identify the most prominent research areas, journal/conference, and authors in the field of a digital twin. This study identifies the current scenarios, possible research gaps, challenges in implementing DT, case studies and future research goals within the research theme.

Wireless technologies are being introduced in industrial applications since they provide certain benefits, such as the flexibility to modify the layout of the nodes, improving connectivity with monitoring and decision nodes, adapting to mobile devices and reducing or eliminating cabling. However, companies are still reluctant to use them in time-critical applications, and consequently, more research is needed in order to be massively deployed in industrial environments. This paper goes in this direction by presenting a novel wireless acquisition system aimed at industrial applications. This system embeds a low-cost technology, such as XBee, not frequently considered for deterministic applications, for deploying industrial applications that must fulfill certain QoS requirements. The use of XBee 900 MHz modules allows for the use of the 2.4 GHz band for other purposes, such as connecting to cloud services, without causing interferences with critical applications. The system implements a time-slotted media access (TDMA) approach with a timely transmission scheduling of the messages on top of the XBee 900 MHz technology. The paper discusses the details of the acquisition system, including the topology, the nodes involved, the so-called coordinator node and smart measuring nodes, and the design of the frames. Smart measuring nodes are implemented by an original PCB which were specifically designed and manufactured. This board eases the connection of the sensors to the acquisition system. Experimental tests were carried out to validate the presented wireless acquisition system. Its applicability is shown in an industrial scenario for monitoring the positioning of an aeronautical reconfigurable tooling prototype. Both wired and wireless technologies were used to compare the variables monitored. The results proved that the followed approach may be an alternative for monitoring big machinery in indoor industrial environments, becoming especially suitable for acquiring values from sensors located in mobile parts or difficult-to-reach places. This research was funded by the Basque Government, through the project EKOHEGAZ (ELKARTEK KK-2021/00092), Diputación Foral de Álava (DFA) through the project CONAVANTER, and to the UPV/EHU through the project GIU20/063.

In order to attain high manufacturing productivity, industry 4.0 merges all the available system and environment data that can empower the enabled-intelligent techniques. The use of data provokes the manufacturing self-awareness, reconfiguring the traditional manufacturing challenges. The current piece of research renders attention to new consideration in the Job Shop Scheduling (JSSP) based problems as a case study. In that field, a great number of previous research papers provided optimization solutions for JSSP, relying on heuristics based algorithms. The current study investigates the main elements of such algorithms to provide a concise anatomy and a review on the previous research papers. Going through the study, a new optimization scope is introduced relying on additional available data of a machine, by which the Flexible Job-Shop Scheduling Problem (FJSP) is converted to a dynamic machine state assignation problem. Deploying two-stages, the study utilizes a combination of discrete Particle Swarm Optimization (PSO) and a selection based algorithm followed by a modified local search algorithm to attain an optimized case solution. The selection based algorithm is imported to beat the ever-growing randomness combined with the increasing number of data-types.

The technological advances recently brought to the manufacturing arena (collectively known as Industry 4.0) offer huge possibilities to improve decision-making processes in the shop floor by enabling the integration of information in real-time. Among these processes, scheduling is often cited as one of the main beneficiaries, given its data-intensive and dynamic nature. However, in view of the extremely high implementation costs of Industry 4.0, these potential benefits should be properly assessed, also taking into account that there are different approaches and solution procedures that can be employed in the scheduling decision-making process, as well as several information sources (i.e. not only shop floor status data, but also data from upstream/downstream processes). In this paper, we model various decision-making scenarios in a shop floor with different degrees of uncertainty and diverse efficiency measures, and carry out a computational experience to assess how real-time and advance information can be advantageously integrated in the Industry 4.0 context. The extensive computational experiments (equivalent to 6.3 years of CPU time) show that the benefits of using real-time, integrated shop floor data and advance information heavily depend on the proper choice of both the scheduling approach and the solution procedures, and that there are scenarios where this usage is even counterproductive. The results of the paper provide some starting points for future research regarding the design of approaches and solution procedures that allow fully exploiting the technological advances of Industry 4.0 for decision-making in scheduling.

The push towards Industry 4.0 is constraining the industries to work in integrated supply chains. This implies to be open to the integration their production plants with other plants, and to provide access to their data and processes. In most cases, this also means to give access at data and flows to their customers to perform some synchronizations, permit supervision, and to create integrated control rooms, with synoptics and dashboards. This activity is also facilitated by the introduction of IoT solutions with IoT Devices, IoT Brokers, etc., which have a completely different approach with respect to DCS and SCADA solutions usually adopted in the industry for controlling their local productions. In this paper, Snap4Industry with its IoT development environment and framework for implementing the Control and Supervision of complex plant with multiple DCS and/or chains in the view of Industry 4.0 is presented. In particular, the paper describes the motivations/requirements and the actions performed to extend IoT Snap4City 100% open-source platform to comply with Industry 4.0 requirements. The main additions for creating Snap4Industry solution have been on: (i) industry protocols, (ii) custom widgets and synoptics Dashboards, (iii) new MicroServices for Node-RED for enabling the usage of synoptics as event driven devices, (iv) usage of WebSocket secure for the communication with custom Synoptics and Widgets for dashboards, automatized process for producing synoptics templates according to GDPR, and (v) the possibility of creating integrated workflows from data ingestion to synoptics. The paper also presents the assessment of the solution in terms of volume data and messages exchanged. The research has been founded into SODA R&D project founded by Regional Tuscany Gov.

Prior studies have investigated the relationship between Industry 4.0 technologies and work. This paper acknowledges the contributions of such studies and builds on their perspective to broaden the understanding of the contribution of Industry 4.0 technologies to specific worker capabilities and manufacturing activities. The aim is to build a conceptual framework to consolidate a common view on this growing yet fragmented issue by integrating a wide range of findings from the literature. The study adopts a systematic literature review approach to systematize such knowledge in a singular and consolidated perspective on Industry 4.0 technologies and work. The study analyzes 80 papers in this field and investigates how different Industry 4.0 technologies are related to workers’ manufacturing activities. Eight main manufacturing activities were considered to frame the analysis: assembly, maintenance, training, quality control, movement, machine operation, product and process design, and production planning and control. Eight worker capabilities that Industry 4.0 technologies can enhance were also considered: super-strength capability, augmented capability, virtual capability, healthy capability, smart capability, collaborative, social capability, analytical capability. Based on these 80 papers, this paper conceptualizes Smart Working-related technologies for Operators 4.0 and shows their benefits and limitations as described in the literature. The study shows how these manufacturing activities and worker capabilities can be supported by Industry 4.0 technologies, which is useful for future research and the design of operational processes in the Industry 4.0 context.

The article describes an innovative metrizable idea for systemic assessments of product quality within the baking industry. Complex product quality analysis requires the employment of metrizability criteria for factors that impact the quality of the product, and these are called determinants. Therefore, such analysis is only possible with the use of systems engineering. A system represents the potential of a manufacturing process, of major impact on quality. Composites of the manufacturing process make up the determinants of bread quality, grouped into three sets: raw materials, manufacturing technology, and manufacturing organization and technique. This paper also contains methodological implications for the construction of algorithms for manufacturing process potential determinants. Metrizable product quality assessment is a very important issue in the context of its implementation in manufacturing companies. Its use allows for obtaining comprehensive data on the quality status of a product. It is an important tool for analyzing and forecasting modern quality trends. The method presented in the article is new, innovative, and practical; and its vector representation may prove useful in Quality 4.0. The method could be an important point of reference for managers, directors, and decision makers who must determine the best metrizability criteria for systemic product quality assessments, and could prove useful in Industry 4.0 in the bakery industry. The main value of the paper is the presentation of a new, extensive method for systemic assessments of product quality based on vector analysis in industrial organization. We trialed the method in the baking industry. We concluded that the method is a contribution to management science, especially in the field of quality management, because this approach is not used in business and is not described in relevant international literature.

Assembly line balancing problems (ALBP) have plagued scholars and practitioners for decades. This paper investigates a new assembly system called flexible assembly line (FAL) derived from empirical observations in an air-conditioner assembly workshop. FAL can avoid the ALBP itself thanks to its structural flexibility and reconfigurability. However, field investigation highlights new challenges in the FAL - the mismatch between production (assembly) and intralogistics (material supply) leads to long waiting/idle time and workflow chaos, consequently lowers productivity and increases backorders. The production-intralogistics (PiL) processes are spatiotemporally coupled and interactional. Its complexity is much higher than considering the production or intralogistics optimization solely. And the PiL processes are further complicated by uncertain events such as new job arrivals, stochastic operational time, and equipment failures. The advent of Industry 4.0 technologies shows the tremendous potentials to revolutionize the contemporary notions of production management. Massive production data can be collected and analyzed in real-time. Nevertheless, there is little methodological research regarding utilizing real-time data to support production decisions under uncertainties. Thus, how to leverage real-time data collected in Industry 4.0 environments to support the decision-making of PiL processes for achieving a matched, coordinated, and synchronous operations management under various uncertainties, is a novel research problem. This paper develops a five-phase Graduation intelligent Manufacturing System (GiMS) to achieve PiL synchronization with flexibility and resilience. The underlying principles and rationale of GiMS are formulated as a synchronization mechanism, which includes a graph-theory based clustering for planning/scheduling and real-time decentralized ticketing for execution/control. Comprehensive numerical results validate the superiority of GiMS and the benefits of visibility and traceability in various scenarios. Moreover, the effects of uncertainties and trolley capacity are investigated in the sensitivity analysis.

Current tendencies of product, project and services development focus on a higher consideration of the User Experience (UX). Therefore, traditional training and teaching methodologies need to adapt to prepare the students to develop strategies for problem solving for their professional education. Such needs have risen and interest in tendencies such as education and Industry 4.0 has grown. This paper presents and analyzes the process and results of a teaching implementation methodology based on Challenge-Based Learning (CBL). The paper describes the process followed, explaining the methodology precedents that led to the final implementation case. It also mentions previous experiments on product analysis and home automation developments that are linked to implementation of the technology. This case’s implementation, analysis and experimentation integrated the use of Emotional Domotics (ED) Tools for the UX analysis, to grant feedback and compare the students’ results with the bio-metrical and emotional computational analysis. The methodology, described through this document, allowed the students to better understand and experience some of the implications of an interconnected system with instant information feedback. This allowed them to better grasp part of the impact that the tendency towards the Internet of Things (IoT) is currently having, and the impact of the improvement proposals from the students.

The increasing economic level impacts the urban structure. Therefore, based on the continuous improvement of the level of science and technology, it is necessary to conduct green city innovation and development planning in combination with economic development. This article combines IoT technology and 5 G technology to build an intelligent planning model under the current economic level background. Moreover, based on network dual connectivity research, our work discusses and analyzes the application of dual connectivity technology and specific architecture selection in the early stage of 5 G deployment and 4 G LTE network collaborative networking. Simultaneously, this paper studies and analyzes related protocols and proposals that were proposed and frozen, researches and improves the hybrid networking technology in 5 G heterogeneous networks, and constructs the functional structure model. In addition, this paper designs a systematic planning method based on the demands of innovative green city planning. Finally, this paper analyzes the performance of this method through simulation experiments. The research results show that the model constructed in this paper has certain effects.

In industry 4.0 and digital transformation scenarios, manufacturing companies face the challenges of the exponential growth in the volume of data. A considerable part of the data that enterprises generate in large quantities, at a high speed, and in different forms can be defined as dark data. Companies are not able to extract their potential value for data management, storage, and maintenance issues, and, due to their unstructured form, they become unknown together with their informative value. Some researchers and professionals have addressed the issue of dark data but none of them have focused on the specificity of the manufacturing industry or on the data generated in it. Based on a lack in the literature, in this article, we explore the dark data through a systematic literature review and three focus groups with manufacturing companies. This article fills the gap and provides a valuable support for manufacturing companies to be aware of the presence of dark data in their scenarios, to identify them, and to stimulate initiatives for dark data exploitation. Furthermore, the study provides for the academic audience a reference paper for addressing future exploration in the field of data management in the companies for improving their innovative and operative capabilities.

The assets in Industry 4.0 are categorised into physical, virtual and human. The innovation and popularisation of ubiquitous computing enhance the usage of smart devices: RFID tags, QR codes, LoRa tags, etc. for assets identification and tracking. The generated data from Industrial Internet of Things (IIoT) eases information visibility and process automation in Industry 4.0. Virtual assets include the data produced from IIoT. One of the applications of the industrial big data is to predict the failure of manufacturing equipment. Predictive maintenance enables the business owner to decide such as repairing or replacing the component before an actual failure which affects the whole production line. Therefore, Industry 4.0 requires an effective asset management to optimise the tasks distributions and predictive maintenance model. This paper presents the Genetic Algorithm (GA) based resource management integrating with machine learning for predictive maintenance in fog computing. The time, cost and energy performance of GA along with MinMin, MaxMin, FCFS, RoundRobin are simulated in the FogWorkflowsim. The predictive maintenance model is built in two-class logistic regression using real-time datasets. The results demonstrate that the proposed technique outperforms MinMin, MaxMin, FCFS, RoundRobin in execution time, cost and energy usage. The execution time is 0.48% faster, 5.43% lower cost and energy usage is 28.10% lower in comparison with second-best results. The training and testing accuracy of the prediction model is 95.1% and 94.5%, respectively.