Prediction and Optimization with Data Science (AI)

Data analysis technologies, especially those leveraging machine learning and computer vision, have advanced significantly, driving widespread adoption across various sectors.
At ENEOS, we are actively engaged in developing and adapting cutting-edge technologies such as machine learning—including deep learning, unsupervised learning, and reinforcement learning—mathematical optimization, large language models (LLMs), and AI agents.
We are also developing AI for automatic plant operation and anomaly detection to increase plant productivity, enhance logistics efficiency, and optimize allocation of vessels and land transportation.
To meet the rapidly increasing demand for digital technologies, we are creating proprietary data-analysis and machine learning support tools, accelerating company-wide digital transformation (DX).
In addition, our CFD analysis technologies support large-scale equipment development and scale-up projects, while helping prevent operational issues and improving productivity in existing manufacturing facilities.
Looking ahead, we remain committed to acquiring and developing advanced technologies to strengthen the competitiveness of the ENEOS supply chain while contributing to a carbon-neutral society.

Business application

・Ship allocation optimization

ENEOS has a large-scale and complex supply chain that begins with the procurement of crude oil from oil-producing countries and ends with the delivery of petroleum products to consumers.
Here, the planning of sea routes for very large crude carriers (VLCCs) that transport crude oil from the Middle East to refineries throughout Japan represent a highly difficult large-scale combinatorial optimization problem that must consider many types of ports, ships, and crude oil.
ENEOS is using its mathematical optimization technology to construct an original system that can perform high-speed and high-quality planning to maximize the profits of its oil refinery business through an efficient supply chain.