Efficient decision-making in SMEs: leveraging knowledge graphs with Neo4j and AI vision

Mo, F.; Ur Rehman, H. U.; Elshafei, B.; Chaplin, J. C.; Sanderson, D.; Martínez-Arellano, G.; Ratchev, S.

Abstract

In the evolving digital landscape, Small and Medium-sized Enterprises (SMEs) grapple with the intricate task of managing vast manufacturing data while operating within budgetary constraints. Addressing this dichotomy, our research introduces an innovative and cost-conscious solution that marries the capabilities of Neo4j's knowledge graph with an AI-enhanced vision system. This integrated system adeptly captures real-time manufacturing data, including product images, configuration details, and specific parameters relevant to the leak testing process. This data is subsequently structured within a comprehensive knowledge graph, enabling SMEs to derive actionable insights and optimize their manufacturing decisions. By harnessing the affordability and scalability of Neo4j's cloud service, the approach we propose stands as a beacon for SMEs, positioning them for enhanced precision in leak testing, operational efficiency, and sustained growth in the digitized economy.

Citation

Mo, F., Ur Rehman, H. U., Elshafei, B., Chaplin, J. C., Sanderson, D., Martínez-Arellano, G., & Ratchev, S. (2023). Efficient decision-making in SMEs: leveraging knowledge graphs with Neo4j and AI vision. In Low-Cost Digital Solutions for Industrial Automation (LoDiSA 2023). https://doi.org/10.1049/icp.2023.1736

• Presentation Conference Type: Edited Proceedings
• Conference Name: Low-Cost Digital Solutions for Industrial Automation (LoDiSA 2023)
• Start Date: Sep 25, 2023
• End Date: Sep 26, 2023
• Acceptance Date: Sep 25, 2023
• Online Publication Date: Nov 21, 2023
• Publication Date: Sep 25, 2023
• Deposit Date: Mar 19, 2024
• Publicly Available Date: Mar 26, 2024
• Book Title: Low-Cost Digital Solutions for Industrial Automation (LoDiSA 2023)
• DOI: https://doi.org/10.1049/icp.2023.1736
• Public URL: https://nottingham-repository.worktribe.com/output/29000890
• Publisher URL: https://ieeexplore.ieee.org/document/10324451