Big Data Analytics Models for Smart Industrial Applications

Jürgen Ziegler

Big Data Analytics Models for Smart Industrial Applications

Author(s)	Jürgen Ziegler
Country	Germany
Abstract	The emergence of Industry 4.0 has transformed industrial environments by integrating connected machines, industrial Internet of Things devices, cyber-physical systems, cloud platforms, robotics, artificial intelligence, and advanced data analytics. Modern industrial facilities continuously generate large volumes of heterogeneous information through production equipment, sensors, enterprise systems, supply-chain platforms, quality-control systems, and operational databases. Big data analytics models provide mechanisms for converting these complex information resources into actionable industrial intelligence. This study examines major big data analytics models for smart industrial applications and evaluates their contribution to predictive maintenance, production optimization, quality management, resource allocation, process monitoring, supply-chain coordination, and intelligent decision-making. A qualitative descriptive research design based on secondary academic literature was adopted. The analysis considers descriptive, diagnostic, predictive, and prescriptive analytics together with machine learning, deep learning, stream processing, anomaly detection, and industrial Internet of Things technologies. Existing research indicates that big data and stream-processing technologies are particularly relevant to Industry 4.0 applications requiring real-time data collection, processing, storage, and analysis. The findings indicate that predictive analytics can support early identification of equipment degradation, while machine learning can improve classification, forecasting, anomaly detection, and remaining-useful-life estimation. Recent research further emphasizes the growing integration of artificial intelligence, IoT, and big data in predictive maintenance while identifying persistent challenges involving real-time deployment, heterogeneous data, model robustness, and cybersecurity. The study concludes that effective smart industrial analytics requires an integrated architecture connecting industrial data acquisition, preprocessing, distributed storage, real-time analytics, intelligent modeling, decision support, and continuous feedback. Successful implementation also depends on data quality, interoperability, cybersecurity, computational scalability, explainability, workforce capabilities, and responsible industrial governance.
Keywords	Big Data Analytics, Smart Industry, Industry 4.0, Industrial IoT, Predictive Maintenance, Machine Learning, Smart Manufacturing, Industrial Intelligence, Data Analytics, Intelligent Decision-Making.
Field	Engineering
Published In	Volume 2, Issue 3, May-June 2020
Published On	2020-05-19

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American International Journal of Advanced Research and Development

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Big Data Analytics Models for Smart Industrial Applications

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