Knowledge Management in Organizations : 14th International Conference, KMO 2019, Zamora, Spain, July 15–18, 2019, Proceedings

We’re in the midst of a significant transformation regarding the way we produce products and deliver services thanks to the digitization of manufacturing and new connected supply-chains and co-creation systems. This article elaborates Digital Twins Approach to the current challenges of knowledge management when Industry 4.0 is emerging in industries and manufacturing. Industry 4.0 approach underlines the importance of Internet of Things and interactions between social and physical systems. Internet of Things (and also Internet of Services and Internet of Data) are new Internet infrastructure that marries advanced manufacturing techniques and service architectures with the I-o-T, I-o-S and I-o-D to create manufacturing systems that are not only interconnected, but communicate, analyze, and use information to drive further intelligent action back in the physical world. This paper identifies four critical domains of synergy challenge: (1) Man-to-Man interaction, (2) Man-to-Machine interaction, (3) Machine-to-Man interaction and finally (4) Machine-to-Machine interaction. Key conclusion is that new knowledge management challenges are closely linked to the challenges of synergic interactions between these four key interactions and accurate measurements of synergic interaction.The final authenticated version is available online at https://doi.org/10.1007/978-3-030-21451-7_23</p

Knowledge Management in Organizations : 14th International Conference, KMO 2019, Zamora, Spain, July 15–18, 2019, Proceedings

Knowledge management in organizations brings many benefits for R&D operations of companies and corporations. This empirical study demonstrates the power of large database analyses for industrial strategies and policy. The study is based on the Web of Science database (Core Collection, ISI) and provides an overview of the core enabling technologies of Industry 4.0, as well as the countries and regions at the forefront of the academic landscape within these technologies. The core technologies and technologies of Industry 4.0 and Manufacturing 4.0 are: (1) Internet of Things and related technologies (2) Radio Frequency Identification (RFID), (3) Wireless Sensor Network (WSN), and (4) ubiquitous computing. It also covers (5) Cloud computing technologies, including (6) Virtualization and (7) Manufacturing as a Service (MaaS), and new (8) Cyber-physical systems, such as (9) Digital Twin-technology and (10) Smart & Connected Communities. Finally, important for the manufacturing integration Industry 4.0 enabling technologies are (11) Service Oriented Architecture (SOA), (12) Business Process Management (BPM), and (13) Information Integration and Interoperability. All these key technologies and technology drivers were analysed in this empirical demonstration of knowledge management.</p

Managing smart cities with deepint.net

In this keynote, the evolution of intelligent computer systems will be examined. The need for human capital will be emphasised, as well as the need to follow one’s “gut instinct” in problem-solving. We will look at the benefits of combining information and knowledge to solve complex problems and will examine how knowledge engineering facilitates the integration of different algorithms. Furthermore, we will analyse the importance of complementary technologies such as IoT and Blockchain in the development of intelligent systems. It will be shown how tools like "Deep Intelligence" make it possible to create computer systems efficiently and effectively. "Smart" infrastructures need to incorporate all added-value resources so they can offer useful services to the society, while reducing costs, ensuring reliability and improving the quality of life of the citizens. The combination of AI with IoT and with blockchain offers a world of possibilities and opportunities

Methodologies for innovation and best practices in Industry 4.0 for SMEs

Today, cyber physical systems are transforming the way in which industries operate, we call this Industry 4.0 or the fourth industrial revolution. Industry 4.0 involves the use of technologies such as Cloud Computing, Edge Computing, Internet of Things, Robotics and most of all Big Data. Big Data are the very basis of the Industry 4.0 paradigm, because they can provide crucial information on all the processes that take place within manufacturing (which helps optimize processes and prevent downtime), as well as provide information about the employees (performance, individual needs, safety in the workplace) as well as clients/customers (their needs and wants, trends, opinions) which helps businesses become competitive and expand on the international market. Current processing capabilities thanks to technologies such as Internet of Things, Cloud Computing and Edge Computing, mean that data can be processed much faster and with greater security. The implementation of Artificial Intelligence techniques, such as Machine Learning, can enable technologies, can help machines take certain decisions autonomously, or help humans make decisions much faster. Furthermore, data can be used to feed predictive models which can help businesses and manufacturers anticipate future changes and needs, address problems before they cause tangible harm

Intelligent Models in Complex Problem Solving

Artificial Intelligence revived in the last decade. The need for progress, the growing processing capacity and the low cost of the Cloud have facilitated the development of new, powerful algorithms. The efficiency of these algorithms in Big Data processing, Deep Learning and Convolutional Networks is transforming the way we work and is opening new horizons

Learning AI with deepint.net

This keynote will examine the evolution of intelligent computer systems over the last years, underscoring the need for human capital in this field, so that further progress can be made. In this regard, learning about AI through experience is a big challenge, but it is possible thanks to tools such as deepint.net, which enable anyone to develop AI systems; knowledge of programming is no longer necessary

Building Efficient Smart Cities

Current technological developments offer promising solutions to the challenges faced by cities such as crowding, pollution, housing, the search for greater comfort, better healthcare, optimized mobility and other urban services that must be adapted to the fast-paced life of the citizens. Cities that deploy technology to optimize their processes and infrastructure fit under the concept of a smart city. An increasing number of cities strive towards becoming smart and some are even already being recognized as such, including Singapore, London and Barcelona. Our society has an ever-greater reliance on technology for its sustenance. This will continue into the future, as technology is rapidly penetrating all facets of human life, from daily activities to the workplace and industries. A myriad of data is generated from all these digitized processes, which can be used to further enhance all smart services, increasing their adaptability, precision and efficiency. However, dealing with large amounts of data coming from different types of sources is a complex process; this impedes many cities from taking full advantage of data, or even worse, a lack of control over the data sources may lead to serious security issues, leaving cities vulnerable to cybercrime. Given that smart city infrastructure is largely digitized, a cyberattack would have fatal consequences on the city’s operation, leading to economic loss, citizen distrust and shut down of essential city services and networks. This is a threat to the efficiency smart cities strive for

AIoT for Smart territories

Artificial Intelligence revived in the last decade. The need for progress, the growing processing capacity and the low cost of the Cloud have facilitated the development of new, powerful algorithms. The efficiency of these algorithms in Big Data processing, Deep Learning and Convolutional Networks is transforming the way we work and is opening new horizons. Thanks to them, we can now analyse data and obtain unimaginable solutions to today’s problems. Nevertheless, our success is not entirely based on algorithms, it also comes from our ability to follow our “gut” when choosing the best combination of algorithms for an intelligent artefact. It's about approaching engineering with a lot of knowledge and tact. This involves the use of both connectionist and symbolic systems, and of having a full understanding of the algorithms used. Moreover, to address today’s problems we must work with both historical and real-time data

DeepTech – AI-IoT in smart cities

In this keynote, the evolution of intelligent computer systems will be examined. The need for human capital will be emphasised, as well as the need to follow one’s “gut instinct” in problem-solving. We will look at the benefits of combining information and knowledge to solve complex problems and will examine how knowledge engineering facilitates the integration of different algorithms. Furthermore, we will analyse the importance of complementary technologies such as IoT and Blockchain in the development of intelligent systems. It will be shown how tools like "Deep Intelligence" make it possible to create computer systems efficiently and effectively. "Smart" infrastructures need to incorporate all added-value resources so they can offer useful services to the society, while reducing costs, ensuring reliability and improving the quality of life of the citizens. The combination of AI with IoT and with blockchain offers a world of possibilities and opportunities