AI and Advanced Analytics Applications

Bidyut Sarkar and Rudrendu Kumar Paul

Chapter Chapter 3 in AI for Advanced Manufacturing and Industrial Applications, 2025, pp 61-89 from Springer

Abstract: Abstract This chapter examines how manufacturers can leverage AI and advanced analytics to drive systemic improvements across operations. Building on foundational AI applications, it explores the transformative potential of integrating Generative AI and other advanced technologies into manufacturing processes. Key sections focus on implementing AI-enabled assistance, recommendation, and autonomous systems. Assistance systems, powered by large language models, enhance manual tasks such as programming and maintenance, while recommendation systems analyze complex datasets to optimize decision-making and operations. Autonomous systems use self-adaptive technologies for real-time process optimization, improving efficiency and reducing human error. The chapter also discusses how manufacturers can utilize big data analytics and machine learning to detect patterns, develop predictive models, and uncover cost-saving opportunities. AI-driven simulations are presented as tools to identify and resolve bottlenecks, ensuring smoother production workflows. Detailed case studies illustrate successful AI implementations, including predictive maintenance, quality control, and human resource optimization. The chapter provides step-by-step checklists for integrating AI technologies into manufacturing environments, addressing challenges such as data security, workforce readiness, and ethical considerations. By adopting these advanced technologies, manufacturers can achieve enhanced efficiency, innovation, and competitiveness, paving the way for a new era of data-driven industrial excellence.

Keywords: Generative AI; Manufacturing optimization; AI Assistance systems; Recommendation systems; Autonomous systems; Process optimization (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-86091-1_3

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DOI: 10.1007/978-3-031-86091-1_3

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