JOURNAL ARTICLE

Advanced Analytics and Machine Learning Transforming Industry 5.0

Bakator, MihaljĐorđević, LukaNovaković, BorivojUgrinov, StefanĐurđev, MićaPremčevski, Velibor

Year: 2025 Journal:   Zenodo (CERN European Organization for Nuclear Research)   Publisher: European Organization for Nuclear Research

Abstract

This paper analyzes the transformative impact of advanced analytics and machine learning on Industry 5.0, highlighting the integration of these technologies to improve productivity, efficiency, and innovation. The integration of real-time data processing and predictive analytics enables informed decision-making and proactive issue management, creating agile and responsive manufacturing environments. Human-machine collaboration, augmented by AI, uses the strengths of both human creativity and machine precision, improving operational efficiency and workplace safety. A theoretical model is proposed, detailing the relationships among data collection, advanced analytics, human-machine interfaces, and decision support systems. The model aims to present how machine learning and advanced analytics can contribute to transformation of enterprises in the context of Industry 5.0.

Keywords:
Analytics Predictive analytics Transformative learning Context (archaeology) Agile software development Big data Industry 4.0

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