13Jun Industry 5.0: human-centric, resilient and sustainable strategy by Bojana Bajic R&D Industry 5.0 represents the concept of transition to a human–centric, sustainable, and resilient manufacturing system driven by advanced technologies. However, the greatest potential for innovation in the industry is reflected in the application of advanced digital technologies. The era of advanced digital technologies has been started by the fourth-industrial revolution, better known as Industry 4.0. Many industries expect Industry 4.0 to have a significant impact on their supply chains, manufacturing processes, and business models. Thus, Industry 4.0 is a technological concept offering a promise of enhancement in efficiency through digital connectivity and AI, but reporting a lot of implementation challenges. Read More
2Jun Faculty of Technical Sciences and Mitsubishi Electric Signed the Agreement on Long-Term Business and Technical Cooperation by Prof. Dr. Aleksandar Rikalovic Implementation The Faculty of Technical Sciences from Novi Sad, Serbia, and Mitsubishi Electric Europe B.V., German Branch signed the Agreement on long-term business and technical cooperation. The agreement signed between Faculty and this prominent company defines a joint cooperation in the development of smart factories in the field of Industry 4.0. Mitsubishi Electric has recognized Faculty as a strategic partner in Europe for the development and implementation of the e-F@ctroy concept and Edge Computing solutions based on artificial intelligence. Read More
27May Toward Real-Time Data Analytics Application for Industry 4.0 by Prof. Dr. Aleksandar Rikalovic R&D The Industry 4.0 is moving the production towards smart production systems, based on new technologies (i.e. Internet of Things, Cyber-Physical Systems, Cloud Computing, Big Data and Artificial Intelligence). Companies rightfully have high expectations of Industry 4.0. However, one of the major obstacles is how to transform reactive, via proactive, to predictive production systems via data analytics application. The predictive production systems are new type of intelligent production systems enable the implementation of new technologies. Read More
2May Industry 4.0 Implementation Challenges and Opportunities: A Technological Perspective by Bojana Bajic R&D During the last decade, we have witnessed steady movement of industry and academia toward Industry 4.0. Industry 4.0 is a concept aimed at achieving the integration of physical and cybernetic parts of the manufacturing process via networks and driven by Industry 4.0 technology categories used for the prediction, control, maintenance, and integration of manufacturing processes (i.e., cyberphysical systems, Internet of Things, big data analytics, cloud computing, fog and edge computing, augmented and virtual reality, robotics, cybersecurity, semantic web technologies, and additive manufacturing). However, the research on implementing Industry 4.0 is lagging behind. Read More
1Oct Real-time Data Analytics Edge Computing Application for Industry 4.0: The Mahalanobis-Taguchi Approach by Prof. Dr. Aleksandar Rikalovic R&D Industry 4.0 and its innovative technologies (e.g., Internet of Things, Cyber-Physical Systems, Cloud Computing, Big Data and Artificial Intelligence) represent great promise. Still, companies experience hardship when transforming from reactive to predictive manufacturing systems. The latter, driven by data science development, use predictive models to detect and solve production and maintenance issues before they happen. Read More
28Sep Industry 4.0 Implementation Challenges and Opportunities: A Managerial Perspective by Prof. Dr. Aleksandar Rikalovic R&D Industry 4.0 is a concept aimed at achieving the integration of physical parts of the manufacturing process (i.e., complex machinery, various devices, and sensors) and cyber parts (i.e., advanced software) via networks and driven by Industry 4.0 technology categories used for prediction, control, maintenance, and integration of manufacturing processes. Industry 4.0, which is expected to have a great impact on manufacturing systems in the future, is attracting attention in both industry and academia. Although academic research on Industry 4.0 is growing exponentially, evidence of Industry 4.0 implementation challenges are still main topic. Read More
15Sep Tetra Pak Smart Factory Support by Prof. Dr. Aleksandar Rikalovic Consulting The Tetra Pak hosted partners from Faculty of Technical Sciences where the project-specific topics of the Smart Factory development were discusses with an emphasis on the use of Artificial Intelligence (AI) in manufacturing processes based on Industrial Big Data Analytics. Read More
20May Smart Factory Development – Tarkett project by Prof. Dr. Aleksandar Rikalovic Implementation The Tarkett hosted partners from Faculty of Technical Sciences where the project-specific topics of the Smart Factory concept were discusses as a industry-academy collaboration with an emphasis on the use of Artificial Intelligence (AI) in manufacturing processes. More specifically, the AI models were developed based on Industrial Big Data Analytics using Edge Computing. Read More
30Nov Challenges of Big Data Analytics in Industry 4.0 by Prof. Dr. Aleksandar Rikalovic R&D Today, the rapid development of information and communication technology (ICT) leads to the generation and collection of large amounts of raw data, which represents the undiscovered source of information. The demand of the industry sectors for the constant improvement of production systems leads to the expectation that processing such data, using the advanced analytics method and technique, will have a major impact on the implementation of Industry 4.0 in the future. Read More
23Oct Edge Computing vs. Cloud Computing: Challenges and Opportunities in Industry 4.0 by Prof. Dr. Aleksandar Rikalovic R&D With the technological development of advanced technologies and the use of the Internet of Things (IoT), the number of connected devices is increasing in manufacturing processes. As devices become more and more incorporated using more processing power, the big data is generated. However, increasing the generation of big data leads to problems related to processing and analysis. Read More