Real-time Data Analytics Edge Computing Application for Industry 4.0: The Mahalanobis-Taguchi Approach

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. 

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Industry 4.0 Implementation Challenges and Opportunities: A Managerial Perspective

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. 

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Challenges of Big Data Analytics in Industry 4.0

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. 

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Edge Computing vs. Cloud Computing: Challenges and Opportunities in Industry 4.0

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.

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Faculty of Technical Sciences Smart Factory Workshop

The Faculty of Technical Sciences hosted partners from Tarkett and Mitsubishi Electric, where the vision of presenting the Smart Factory concept. The workshop lasted three days. The first day of the Faculty of Technical Sciences held the workshop at the University of Novi Sad, where the basics of Smart Factory were presented, with an emphasis on Industrial Big Data Analytics using Edge Computing.

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Predictive Manufacturing Systems in Industry 4.0: Trends, Benefits and Challenges

The fourth industrial revolution, known as Industry 4.0, has tendency to push the boundaries of science and technology. This is especially true for the manufacturing industry. One of the biggest challenges facing the manufacturing industry today is how to make intelligent systems for production with “self-aware”, “self-predict and “self-maintain” abilities. Predictive manufacturing systems (PMS) are new intelligent systems that provide these abilities in the production, processes and machines. 

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