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AI-Driven Intelligent Data Analytics and Predictive Analysis in Industry 4.0: Transforming Knowledge, Innovation, and Efficiency

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  • Zhijuan Zong

    (Fuyang Normal University)

  • Yu Guan

    (Fuyang Normal University)

Abstract

In the era of Industry 4.0, integrating digital technologies into industrial processes has become imperative for sustaining growth and fostering innovation. This research paper explores the profound impact of AI-driven intelligent data analytics and predictive analysis on economic efficiency and managerial practices within Industry 4.0. With a focus on knowledge, innovation, technology, and society, this study delves into the transformative potential of these advanced technologies. Intelligent data analytics, powered by artificial intelligence (AI), has revolutionized the way industries harness vast datasets. Uncovering real-time patterns, correlations, and opportunities empowers decision-makers with accurate and timely insights. Predictive analysis, rooted in statistics and machine learning, aids in forecasting trends and managing risks, offering economic stability across sectors. Using a mixed-methods approach, the study combines qualitative interviews with 19 Chinese operations managers and quantitative data from an online survey of 286 managers. The study ranks various Industry 4.0 technologies through ordinal regression based on their impact on environmental sustainability and economic management. Results show that smart sensors, radio-frequency identification, AI, and analytics are the most influential technologies for enhancing economic and environmental outcomes. Conversely, technologies like additive manufacturing and automated robots yield less favorable results. The study also identifies a noticeable gap in professionals’ understanding and adoption of AI and augmented reality. Environmental concerns around the disposal of electronic waste generated by these technologies are also highlighted. The research thus offers significant insights for companies seeking to adopt intelligent data analytics to enhance economic performance and environmental sustainability. On the managerial front, the fusion of these technologies enables agile and responsive frameworks, promoting dynamic strategies in response to changing market dynamics. This culture of continual improvement fosters excellence and foresight in managerial processes. However, challenges exist, including the underutilization of data, data complexity, historical biases, and the need for tailored AI solutions across industries. Ethical considerations, data privacy, and security also pose concerns. Collaborative innovation among stakeholders is crucial to addressing these challenges and seizing opportunities. Governments, academia, and industry players must collaborate to develop technologically advanced, economically viable, and socially responsible solutions. As industries transition to Industry 4.0, this paper advocates a critical approach that embraces technology’s potential while mitigating risks. The future lies in a technologically advanced, economically resilient, and socially inclusive industrial landscape driven by AI-powered knowledge and innovation.

Suggested Citation

  • Zhijuan Zong & Yu Guan, 2025. "AI-Driven Intelligent Data Analytics and Predictive Analysis in Industry 4.0: Transforming Knowledge, Innovation, and Efficiency," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(1), pages 864-903, March.
    • Handle: RePEc:spr:jknowl:v:16:y:2025:i:1:d:10.1007_s13132-024-02001-z
    DOI: 10.1007/s13132-024-02001-z
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