Cosmo Energy drives high operational efficiency with Cognite Data Fusion®

Cosmo Energy Holdings leverages Cognite Data Fusion®️ as the foundational data platform to secure profitability and enhance competitiveness in Cosmo Energy's downstream and petrochemical business

Remote monitoring through digital twins

Collaborative maintenance among refineries and plants

Maximized productivity across their engineering teams

For users

Enabling collaborative maintenance

Cosmo Oil used Cognite Data Fusion®️ to built a "digital twin of the refinery”, consolidating three facilities into one virtual space, in order to enhance efficiency in maintenance operations and increase the productivity of their domain experts across each site:

  • Access to contextualized data in an open industrial digital twin
  • Reduce time spent finding and analyzing data
  • Increased productivity of their domain experts across each site

At the forefront of digital transformation

See Cognite Data Fusion® in action

Get in touch with our product experts to learn more and identify quick wins

This inspiring partnership propels us towards our DX vision of cosmological evolution, which the Cosmo Energy Group is actively envisioning.

Dr. Noriko Rzonca

Dr. Noriko Rzonca

Chief Digital Officer, Cosmo Oil

Cognite Data Fusion will accelerate achievements to advance the digitalization of refineries and serves as the ideal platform foundation to support Cosmo Energy's DX Initiatives.

Hirokatsu Harui

Director, Executive Managing Officer, Cosmo Oil

Generative AI-driven contextualized data with Cognite empowers our operations to be safer and more optimized.

Dr. Noriko Rzonca

Dr. Noriko Rzonca

Chief Digital Officer, Cosmo Oil

The Total Economic Impact of Cognite Data Fusion®

Customer interviews and financial analysis reveal an ROI of 400% and total benefits of $21.56M over three years for the Cognite Data Fusion® platform.

Summary of benefits

(three-year risk-adjusted)

Improved SME efficiency

$1.5M

Revenue gains arising from shorter shutdown period

$4.8M

Real-time data efficiencies

$2.3M

Optimized planned maintenance programme

$4.3M

Energy efficiency savings

$5.1M

Optimization of heavy machinery and industrial work-flow

$9M