Recently, there have been great advances in internet of things (IoT) and wireless sensor networks (WSNs) technologies for promoting electricity industrial upgrades and even allow the introduction of ...the fourth industrial revolution, namely, Smart Grid Industry (SGI) 4.0. The main functions of SGI4.0 are:
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•We propose a novel BMO-based dynamic clustering algorithm to balance the data traffic and energy consumption load evenly among clusters in the smart grid.•We propose an innovative BMO-based routing algorithm to solve energy consumption and QoS-aware reliable data transmission in the smart grid.•The performance evaluations show that EQRP has successfully minimized the end-to-end delay and has improved the other routing QoS performance metrics, such as packet delivery ratio, efficient memory utilization, residual energy, and throughput.
Recently, there have been great advances in internet of things (IoT) and wireless sensor networks (WSNs) leading to the fourth industrial revolution in power grid, namely, Smart Grid Industry 4.0 (SGI 4.0). In the Smart Grid Industry 4.0 framework, the WSNs have the potential to improve power grid efficiency by cable replacement, deployment flexibility, and cost reduction. However, the smart grid (SG) environment that the WSNs operate in is very challenging because of equipment noise, dust, heat, electromagnetic interference, multipath effects and fading, which make it difficult for current WSNs to provide reliable communication. For SGI 4.0 to come true, a WSN-based highly reliable communication infrastructure is essential for successful operation of the next-generation electricity power grids. To address this need, in this paper a novel dynamic clustering based energy efficient and quality-of-service (QoS)-aware routing protocol (called EQRP), which is inspired by the real behavior of the bird mating optimization (BMO), has been proposed. The proposed distributed scheme improves network reliability significantly and reduces excessive packets retransmissions for WSN-based SG applications. Performance results show that the proposed protocol has successfully reduced the end-to-end delay and has improved packet delivery ratio, memory utilization, residual energy, and throughput.
•Theory and application for Industry 4.0 supply chains is investigated.•Application of multi-agent technology in Industry 4.0 supply chain is studied.•A multi-agent approach for sustainable supplier ...evaluation is proposed.•The MAS approach is tested through a real-world case study implementation.•Future research opportunities in Industry 4.0 supply chains are articulated.
Advancements in information and communication systems offer immense opportunities for supply chain intelligence and autonomy establishing stepping stones for Industry 4.0 supply chains (SCs). As a crucial SC decision, sustainable supplier evaluation and selection process have been addressed abundantly in the previous literature. However, this process has not yet been realized within Industry 4.0 SCs where interconnection, real-time information transparency, technical assistance and decentralization of members of a physical system (i.e., supply chain members) are regarded as the main design principles. To narrow the identified gap, a Multi-Agent Systems (MASs) approach is proposed for addressing sustainable supplier evaluation and selection process to provide a proper communication channel, structured information exchange and visibility among suppliers and manufacturers. Furthermore, the application of MASs in this process and their natural applicability as one of the enabling technologies in moving towards Industry 4.0 SCs are investigated in detail. It is found that the proposed approach can help decision-makers inside manufacturing firms to make prompt decisions with less human interactions. The merit of the developed MAS is demonstrated through a real-world implementation on a medical device manufacturer. Finally, the limitations and advantages of the proposed approach are presented together with some remarks for future work.
Industry 4.0, known as the fourth technological transformation towards digital-physical systems in manufacturing, creates a disruptive impact on industries. Manufacturing companies, especially small ...and medium-sized ones, are facing various challenges and must constantly innovate to remain competitive. One way to innovate is by implementing new technologies into company processes. In this study, we investigate how technology, company and industry related factors are associated with the implementation of Industry 4.0 in SMEs. We collect data via a survey with a focus on Industry 4.0 in SMEs. The results indicate that knowledge and expected benefits of technology are the drivers for the implementation of Industry 4.0 technologies. They also show that companies with high levels of process automation and high product variety are more likely to implement Industry 4.0 technologies. Our study creates a better understanding of the status, challenges and plans within Industry 4.0 implementation in SMEs, which will support the development of SME-friendly manufacturing tools and systems and craft managers’ and policymakers’ understanding of Industry 4.0 technologies.
Industry 4.0 is collaborating directly for the technological revolution. Both machines and managers are daily confronted with decision making involving a massive input of data and customization in ...the manufacturing process. The ability to predict the need for maintenance of assets at a specific future moment is one of the main challenges in this scope. The possibility of performing predictive maintenance contributes to enhancing machine downtime, costs, control, and quality of production. We observed that surveys and tutorials about Industry 4.0 focus mainly on addressing data analytics and machine learning methods to change production procedures, so not comprising predictive maintenance methods and their organization. In this context, this article presents a systematic literature review of initiatives of predictive maintenance in Industry 4.0, identifying and cataloging methods, standards, and applications. As the main contributions, this survey discusses the current challenges and limitations in predictive maintenance, in addition to proposing a novel taxonomy to classify this research area considering the needs of the Industry 4.0. We concluded that computer science, including artificial intelligence and distributed computing fields, is more and more present in an area where engineering was the dominant expertise, so detaching the importance of a multidisciplinary approach to address Industry 4.0 effectively.
•We review the state-of-the-art of predictive maintenance in the Industry 4.0.•The methodology consisted of a systematic literature review of the area.•We consider time-based approaches as the main challenge for predictive maintenance.•We present a taxonomy for monitoring in the context of the Industry 4.0.•We highlight the multidisciplinarity involved and the need for integration.
Contemporary manufacturing must prioritise the sustainability of its manufacturing processes and systems. Zero Defect Manufacturing (ZDM) focusses on minimising waste of any kind using data-driven ...technology, hence enhancing the quality of all manufacturing aspects (product, process, service, etc.). Making things right on the first try is the central tenet of ZDM. In recent years, the application of automation for in-line quality inspection systems has begun to attract the interest of both practitioners and academics because of its capability to detect defects in real-time, and thus adapt the system to disturbances. In this work, we provide a systematic review of the literature on current trends in the application of automation for in-line quality inspection with the ultimate objective of achieving ZDM. Additionally, bibliometric and performance analyses have been performed to gain a complete picture of the field. In this work, we have collected bibliometric data from the most widely referred search engines for academic engineering papers, i.e. Scopus, Web of Science, and IEEE Explorer, involving a total of 145 academic publications from 2011 to 2021. Uniquely for this study, we used three research attributes for the analysis of the selected articles, that is, the level of automation, the condition for quality inspection, and the contribution to ZDM dimensions. The literature suggests that there is a lack of research on the use of in-line detection data for the prediction of defects or repair. Based on the results and our interpretation of the literature, an adapted framework of ZDM (Psarommatis et al., 2020a) and multi-layer quality inspection (Azamfirei et al., 2021a) is presented.
•Over the past ten years, interest in automated in-line quality inspection has risen.•Universities from Asia and Europe were found to have the most knowledge on this topic.•99% of the papers clearly showed the level of integration of their inspection system.•Only 1% of the papers focused on theoretical advancement or simulation for the field.•Analysed papers used the principles of ZDM without explicitly mentioning it.•Several departments, with their vast experience in the sector, did not work together.
Despite the recent interest in the Industry 4.0 applications for sustainability, little is known on the processes through which digital transformation and Industry 4.0 technologies enable sustainable ...innovation in manufacturing. The present study addresses this knowledge gap by developing a strategic roadmap that explains how businesses can leverage Industry 4.0 technologies to introduce sustainability into innovative practices. For this purpose, the study conducts a systematic review of extant literature to identify Industry 4.0 functions for sustainable innovation and applies interpretive structural modeling to devise the promised roadmap. The results offer interesting insights into Industry 4.0 applications for sustainable innovation. The strategic roadmap developed reveals that Industry 4.0 enables sustainable innovation through 11 functions. Industry 4.0 and the underlying digital technologies and principles allow businesses to improve interfunctional collaboration and better integrate with internal and external stakeholders. Industry 4.0 further improves the knowledge base and advanced manufacturing competency and promotes organizational capabilities valuable to sustainable innovation such as green absorptive capacity, sustainable partnership, and sustainable innovation orientation. Through these functions, Industry 4.0 subsequently enhances green process innovation capacity and the ability to develop or reintroduce eco‐friendly products economically and competitively. Overall, the roadmap explains the complex precedence relationships among the 11 sustainable innovation functions of Industry 4.0, offering important implications for businesses that seek to leverage Industry 4.0 sustainability implications and manage sustainable development.
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Over the last two centuries, medicines have evolved from crude herbal and botanical preparations into more complex manufacturing of sophisticated drug products and dosage forms. Along ...with the evolution of medicines, the manufacturing practices for their production have advanced from small-scale manual processing with simple tools to large-scale production as part of a trillion-dollar pharmaceutical industry. Today’s pharmaceutical manufacturing technologies continue to evolve as the internet of things, artificial intelligence, robotics, and advanced computing begin to challenge the traditional approaches, practices, and business models for the manufacture of pharmaceuticals. The application of these technologies has the potential to dramatically increase the agility, efficiency, flexibility, and quality of the industrial production of medicines. How these technologies are deployed on the journey from data collection to the hallmark digital maturity of Industry 4.0 will define the next generation of pharmaceutical manufacturing. Acheiving the benefits of this future requires a vision for it and an understanding of the extant regulatory, technical, and logistical barriers to realizing it.
•A systematic literature review is conducted to explore the main features, research and technical challenges in conceiving and building Digital Twins.•Topic Modelling Analysis has been implemented to ...provide an up-to-date picture of the digital twin.•Formal Concept Analysis (FCA) has been applied to understand the digital twin trends and strategies.
Manufacturing enterprises are facing the need to align themselves to the new information technologies (IT) and respond to the new challenges of variable market demand. One of the key enablers of this IT revolution toward Smart Manufacturing is the digital twin (DT). It embeds a “virtual” image of the reality constantly synchronized with the real operating scenario to provide sound information (knowledge model) to reality interpretation model to draw sound decisions. The paper aims at providing an up-to date picture of the main DT components, their features and interaction problems. The paper aims at clearly tracing the ongoing research and technical challenges in conceiving and building DTs as well, according to different application domains and related technologies. To this purpose, the main questions answered here are: ‘What is a Digital Twin?’; ‘Where is appropriate to use a Digital Twin?’; ‘When has a Digital Twin to be developed?’; ‘Why should a Digital Twin be used?’; ‘How to design and implement a Digital Twin?’; ‘What are the main challenges of implementing a Digital Twin?’. This study tries to answer to the previous questions funding on a wide systematic literature review of scientific research, tools, and technicalities in different application domains.
The recent White House report on Artificial Intelligence (AI) (Lee, 2016) highlights the significance of AI and the necessity of a clear roadmap and strategic investment in this area. As AI emerges ...from science fiction to become the frontier of world-changing technologies, there is an urgent need for systematic development and implementation of AI to see its real impact in the next generation of industrial systems, namely Industry 4.0. Within the 5C architecture previously proposed in Lee et al. (2015), this paper provides an insight into the current state of AI technologies and the eco-system required to harness the power of AI in industrial applications.
•An overview of Blockchain and Industry 4.0 is provided.•The abilities of Industry 4.0 for advancing sustainable supply chains are discussed.•Four capabilities of blockchain towards sustainable ...supply chain management are described.•The design of incentive mechanisms to promote consumer green behavior is elaborated.•Reduction of operational costs and improvement of sustainability monitoring are discussed.
The objective of this study is to provide an overview of Blockchain technology and Industry 4.0 for advancing supply chains towards sustainability. First, extracted from the existing literature, we evaluate the capabilities of Industry 4.0 for sustainability under three main topics of (1) Internet of things (IoT)-enabled energy management in smart factories; (2) smart logistics and transportation; and (3) smart business models. We expand beyond Industry 4.0 with unfolding the capabilities that Blockchain offers for increasing sustainability, under four main areas: (1) design of incentive mechanisms and tokenization to promote consumer green behavior; (2) enhance visibility across the entire product lifecycle; (3) increase systems efficiency while decreasing development and operational costs; and (4) foster sustainability monitoring and reporting performance across supply chain networks. Furthermore, Blockchain technology capabilities for contributing to social and environmental sustainability, research gaps, adversary effects of Blockchain, and future research directions are discussed.
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