Enabling real time big data solutions for manufacturing at scale

Çakır M. A., Akın Ö., Deniz H. F., Yılmaz A.

Journal of Big Data, vol.9, no.1, 2022 (Scopus) identifier identifier

  • Publication Type: Article / Article
  • Volume: 9 Issue: 1
  • Publication Date: 2022
  • Doi Number: 10.1186/s40537-022-00672-6
  • Journal Name: Journal of Big Data
  • Journal Indexes: Scopus
  • Keywords: Big data, Digital transformation, Real-time learning, Machine learning, Manufacturing, Open source, Plastics injection
  • Istanbul Technical University Affiliated: Yes


Today we create and collect more data than we have in the past. All this data comes from different sources, including social media platforms, our phones and computers, healthcare gadgets and wearable technology, scientific instruments, financial institutions, the manufacturing industry, news channels, and more. When these data are analyzed in a real-time nature, it offers businesses the opportunity to take quick action in business-development processes (B2B, B2C), gain a different perspective, and better understand applications, creating new opportunities. While changing their sales and marketing strategies, businesses are now able to manage the data they collect in real-time to transform themselves, to record them in a healthy way, to analyze and evaluate data-based processes, and to determine their digital transformation roadmaps, their interactions with their customers, sectoral diffraction, application, and analysis. They want to accelerate the transformation processes within the technology triangle. Thus, big data, recently called as small and wide data, is at the center of everything and becomes an important application for digital transformation. Digital transformation helps companies embrace change and stay competitive in an increasingly digital world. The value of big data in manufacturing, independent from sectoral variations, comes from its ability to combine both in an organization’s efforts to both digitize and automate its end-to-end business operations. In this study, the current digitalization and automation applications of one of the plastic injection-based manufacturing companies at scale will be discussed. Presented open-source-based big data analytics platform, DataCone, that increases data processing efficiency, storage optimization, encourages innovation for real time monitorization and analytics, and support new business models in different industry segments will be demonstrated and discussed. Thus, development and applied ML solutions will be discussed providing important prospects for the future.