Data Analytics in Industry 5.0: Transforming Processes with Insights

Shreeraj Mummidivarapu (), Abhishek Hazra () and Bheemappa Halavar ()
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Shreeraj Mummidivarapu: Indian Institute of Information Technology Sri City
Abhishek Hazra: Indian Institute of Information Technology Sricity
Bheemappa Halavar: Indian Institute of Information Technology Sri City

A chapter in Industry 5.0, 2025, pp 93-118 from Springer

Abstract: Abstract With the arrival of Industry 5.0, it leapt into the industrial paradigms that were constructed from the groundwork laid down by Industry 4.0. New concepts have been introduced that redefine human-machine-data relations. This chapter elaborates on advanced data analytics, one of the enablers and drivers for Industry 5.0, and further discusses how intelligent, data-driven insight changes industrial processes. The chapter also addresses reinforcement learning applied to industrial optimization, emphasizing robotic process automation and the optimization of complex industrial processes. The chapter begins by defining Industry 5.0 and its unmistakable features, pointing out that it creates a symbiotic relationship whereby human creativity is combined with machine efficiency. It goes on to trace how the industry handled data from a mere collection in the early days to sophisticated analytics in Industry 4.0 and, finally, the level of integration possible in Industry 5.0. This is now the era of Information Technology (IT) and Operational Technology (OT) integration, which enables degrees of integration and analysis that have been previously unattainable. The chapter provides detailed coverage of different types of data analytics central to Industry 5.0. These range from the descriptive ones that give insight into past events to more advanced forms of predictive, prescriptive, and cognitive analytics. Real-time analytics has been emphasized, which is central within the very fast-moving, adaptive environments characterizing Industry 5.0. The second subchapter explains the most advanced techniques of predictive analytics applied to industrial cases using the machine learning algorithm of supervised, unsupervised, and semi-supervised learning. It goes on to explain Deep Learning (DL) in manufacturing and process industries, which also involves technologies like Convolutional Neural Networks (CNNs) in quality control with image analysis, Recurrent Neural Networks (RNNs) in forecasting, and Generative Adversarial Networks (GANs) in synthetic data generation. The next sections cover technology support for advanced analytics enablers, such as big data platforms, distributed computing frameworks, stream processing systems, and strategies for data integration and preparation within the context of industry with its variety of data sources. It elaborates length on the challenges and strategies around data integration and preparation in the presence of structured, semi-structured, and unstructured data. Several use cases and applications demonstrate how data analytics is applied to predictive maintenance, assurance of quality, optimization of supply chains, energy management, and product lifecycle management. This will present an integration of data analytics with other key technologies of Industry 5.0, paying attention to the Industrial Internet of Things (IIoT), 5G networks, blockchain, and digital twins. The following section will further explore how the convergence of those technologies has opened new avenues for optimization and innovation in industries. The chapter also covers challenges and considerations in the application of advanced analytics in Industry 5.0: privacy and security issues within big data, ethical concerns related to AI-driven decisions, preexisting skill gaps, and the complexity involved in integrating new technologies with legacy systems. Finally, the chapter looks into the future and draws some emerging trends and research lines in the analytics of industrial data: from the potential of quantum computing for industrial data to federated learning for distributed industrial systems, explainable AI (XAI) for more transparent decision-making, and edge AI with decentralized analytics. It further underlines human-AI collaboration in industrial settings in a way that fully respects the core principles of Industry 5.0. The chapter concludes with a summary of all the important points raised and discussed about how these data analytics enablers would themselves be the drivers of transformation within Industry 5.0. Thus, it is a call to industries to strive for these advanced analytics enablers and reap competitive advantages through operational efficiencies. This chapter on the current state of data analytics in Industry 5.0 provides a comprehensive review that gives readers a broad view of existing capabilities, trends that will shape the future, and emerging possibilities. This would be helpful not only to practitioners who bring to life such technology but also to researchers pushing the envelope on industrial data analytics.

Keywords: Industry 5.0; Data analytics; Real-time analytics; IIoT; Human-AI collaboration (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/978-3-031-87837-4_5

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