Enabling real time big data solutions

for manufacturing at scale


The study “Enabling real time big data solutions for manufacturing at scale” conducted by Istanbul Technical University Department of Physics Engineering member Prof. Dr. Altan Çakır was published in Journal of Big Data


Prof. Dr. Altan Çakır, Istanbul Technical Universtiy, Physics Eng. Dept.

Özgün Akın

Halil Faruk Deniz

Ali Yılmaz


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 realtime

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.