An Industry 4.0-Based Data Visualization Framework for Improved Manufacturing Data Analysis—A Case Study

Abstract

The proliferation of Industry 4.0 technologies in manufacturing has created an unprecedented opportunity to leverage Big Data for process optimization and efficiency improvements. However, the sheer volume of data can also lead to critical information being overlooked, potentially hindering productivity and competitiveness. This paper presents a straightforward Industry 4.0-based data visualization framework designed to transform raw manufacturing data into actionable insights. Specifically, this work focuses on the analysis of Overall Equipment Effectiveness (OEE) data. The framework utilizes a practical dashboard tool to enable manufacturers to perform in-depth data analysis and identify areas for improvement in real-time. Such a framework enables prompt intervention when corrective actions are needed, ultimately increasing efficiency and reducing production downtime. The framework was successfully implemented at a tire manufacturing company on a single machine within a short period of time. The results highlighted the effectiveness of data visualization in identifying specific operational losses and informing strategic decision-making. This work emphasizes the critical role of technology and proper policies in leveraging data to optimize production processes and drive continuous improvement in Industry 4.0 environments.

Keywords

Big data / Data visualization / Industry 4.0 / Manufacturing systems / Overall Equipment Effectiveness (OEE)

Statement of the Use of Generative AI and AI-Assisted Technologies in the Writing Process

During the preparation of this manuscript, the authors used Copilot in order to enhance the readability of the manuscript’s “Abstract”. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.

Acknowledgments

The authors would like to acknowledge the contribution of all the members of the 2020-2021 senior design project team for their role in the initial efforts of this work: Abdelrahman Fadel, Abdelrahman Fathy, Ahmed M. Abdelhamed, Alaa Fayed, Sarah Elsayed, and Somaya Yasser.

Author Contributions

A.E.: Conceptualization, Methodology, Supervision, Visualization, Writing—original draft, review, and editing; S.E.: Conceptualization, Investigation, Methodology, Writing—original draft; A.A.A.: Conceptualization, Investigation, Methodology, Software, Visualization, Writing—original draft; H.F.: Conceptualization, Methodology, Writing—original draft, review, and editing.

Ethics Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Due to the nature of the research and the legal non-disclosure agreement with the company, the complete data used in this work is not available.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this work.

Declaration of Competing Interest

This paper is an improved version of previous work by the authors. However, at this level of detail and length, this manuscript is a natural extension to the previous effort, integrating it into a more comprehensive framework that aids manufacturers with decision-making.

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