•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 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.
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.
Impact of Cobots on automation Lefranc, Gastón; Lopez-Juarez, Ismael; Osorio-Comparán, Roman ...
Procedia computer science,
2022, 2022-00-00, Letnik:
214
Journal Article
Recenzirano
Odprti dostop
This paper presents cobots (collaborating robots) and their impact on automation and real life. The impact of using cobots is analyzed from the economic, philosophical, and human point of view. ...Current models of cobot use are presented and illustrated through examples of Cobot use today and what it might look like in the future. Finally, it is believed that cobots could be valuable for developing countries and small manufacturing companies.
Smart manufacturing is being shaped nowadays by two different paradigms: Industry 4.0 proclaims transition to digitalization and automation of processes while emerging Industry 5.0 emphasizes human ...centricity. This turn can be explained by unprecedented challenges being faced recently by societies, such as, global climate change, pandemics, hybrid and conventional warfare, refugee crises. Sustainable and resilient processes require humans to get back into the loop of organizational decision-making. In this paper, we argue that the most reasonable way to marry the two extremes of automation and value-based human-driven processes is to create an Industry 4.0 + Industry 5.0 hybrid, which inherits the most valuable features of both - efficiency of the Industry 4.0 processes and sustainability of the Industry 5.0 decisions. Digital cognitive clones twinning human decision-making behavior are represented as an enabling technology for the future hybrid and as an accelerator (as well as resilience enabler) of the convergence of the digital and human worlds.
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