An end-to-end big data analytics platform for IoT-enabled smart factories: A case study of battery module assembly system for electric vehicles. (April 2022)

• Record Type: Journal Article
• Authors: Kahveci, Sinan; Alkan, Bugra; Ahmad, Mus'ab H.; Ahmad, Bilal; Harrison, Robert
• Is Part Of: Journal of manufacturing systems. Volume 63(2022)
• Page Start: 214
• Page End: 223
• Publication Date: 2022-04
• Subjects: Big data analytics -- Data visualisation -- IoT -- Industry 4.0 -- Smart manufacturing; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2022.03.010
• Languages: English
• ISSNs: 0278-6125

Abstract

Abstract: Within the concept of factories of the future, big data analytics systems play a critical role in supporting decision-making at various stages across enterprise processes. However, the design and deployment of industry-ready, lightweight, modular, flexible, and cost efficient big data analytics solutions remains one of the main challenges towards the Industry 4.0 enabled digital transformation. This paper presents an end-to-end IoT-based big data analytics platform that consists of five interconnected layers and several components for data acquisition, integration, storage, analytics and visualisation purposes. The platform architecture benefits from state-of-the-art technologies and integrates them in a systematic and interoperable way with clear information flows. The developed platform has been deployed in an electric vehicle battery module assembly automation system designed by the Automation Systems Group at the University of Warwick, the UK. The developed proof-of-concept solution demonstrates how a wide variety of tools and methods can be orchestrated to work together aiming to support decision-making and to improve both process and product qualities in smart manufacturing environments. Graphical Abstract: ga1 Highlights: Secure, interoperable, resilient and scalable reference architecture for IoT-based end-to-end big data analytics platform. Cost efficient, enterprise grade implementation of the proposed architecture for Industry 4.0 as evidenced in the case study. Multi-tier processing and analytics of streaming machine data to generate actionable insights.