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

This study explores the evolution and impact of research on the challenges and opportunities in the implementation of artificial intelligence (AI) in manufacturing between 2019 and August 2024. By addressing the growing integration of AI technologies in the manufacturing sector, the research seeks to provide a comprehensive view of how AI applications are transforming production processes, improving efficiency, and opening new business opportunities. A bibliometric analysis was conducted, examining global scientific production, influential authors, key sources, and thematic trends. Data were collected from Scopus, and a detailed review of key publications was carried out to identify knowledge gaps and unresolved research questions. The results reveal a steady increase in research related to AI in manufacturing, with a strong focus on automation, predictive maintenance, and supply chain optimization. The study also highlights the dominance of certain institutions and key authors driving this field of research. Despite the progress, significant challenges remain, particularly regarding the scalability of AI solutions and ethical considerations. The findings suggest that while AI holds considerable potential for the manufacturing industry, more interdisciplinary research is needed to address existing gaps and maximize its benefits.

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Streamlining Distribution Routes Using the Language Model of Artificial Intelligence

Kleinová,

Straka

2024

Sustainability

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This article addresses the use of artificial intelligence for the needs of effective, sustainable development in logistics and its components. The subject of this article is to highlight the possibility of processing optimization methods using an artificial intelligence module. The goal is to determine whether the AI module can replicate the same, or at least have a similar result, as the traditional optimization methods used in practice. The challenge involves constantly identifying reserves in already highly sophisticated micro-logistics systems using modern commercial means of artificial intelligence. Applying artificial intelligence to elements of a company’s micro-logistics model is a new approach. This article aims to determine whether artificial intelligence can reduce costs through calculations in a specific area defined for it. By optimizing distribution routes using ChatGPT-3.5, we significantly reduced the total distance traveled, leading to substantial savings in transportation costs. This optimization led to a significant improvement in the efficiency of logistic processes and considerable cost savings. This result demonstrates that artificial intelligence can be an effective tool for solving complex logistic tasks. The possibilities of effectively sustainable logistics development with the help of artificial intelligence lie not only in the quality of the achieved outputs but also in the speed of the calculations and the procedures for solving defined project tasks. It follows from this definition that artificial intelligence will continue to play an essential role in the defined field of logistics in the future.

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

Cited by 3 publications

References 85 publications

Leveraging Artificial Intelligence for Innovative Technopreneurial Business Models

Leveraging Artificial Intelligence for Innovative Technopreneurial Business Models

Challenges and Opportunities in the Implementation of AI in Manufacturing: A Bibliometric Analysis

Streamlining Distribution Routes Using the Language Model of Artificial Intelligence

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