Manufacturing processes are highly complex. Production lines have several robots and digital tools, generating massive amounts of data. Unstructured, noisy and incomplete data have to be collected, aggregated, pre-processed and transformed into structured messages of a common, unified format in order to be analysed not only for the monitoring of the processes but also for increasing their robustness and efficiency. This chapter describes the solution, best practices, lessons learned and guidelines for Big Data analytics in two manufacturing scenarios defined by CRF, within the I-BiDaaS project, namely ‘Production process of aluminium die-casting’, and ‘Maintenance and monitoring of production assets’. First, it reports on the retrieval of useful data from real processes taking into consideration the privacy policies of industrial data and on the definition of the corresponding technical and business KPIs. It then describes the solution in terms of architecture, data analytics and visualizations and assesses its impact with respect to the quality of the processes and products.
Keywords: Big Data · Self-service solution · Manufacturing · Die-casting · Maintenance and Monitoring · Advanced analytics and visualizations
Excerpt from: Alexopoulos A, et al. (2021) Big Data Analytics in the Manufacturing Sector: Guidelines and Lessons Learned Through the Centro Ricerche FIAT (CRF) Case. In: Curry E., Auer S., Berre A. J., Metzger A., Perez M. S., Zillner S. (eds) Technologies and Applications for Big Data Value. Springer, Cham.