video

Best open-source low code platform for the energy industry

The energy and utilities (E&U) industry faces many challenges, including decarbonization, electrification and market volatility. To thrive in this environment, E&U businesses must embrace innovation and digitalization. Watch our video to learn how the best open-source low code platform enables anyone in your business to quickly build applications that connect isolated data sources and unlock their insights.


Implementing new technology in an organization

With low code development, E&U businesses can introduce innovative systems and services faster while reducing costs. Because the simplified visual tools are available to anyone, IT resources can become more responsive towards overall businesses strategies. Workers and customers alike will benefit from implementing new technology in an organization.

Overcome data isolation using the cloud

Within E&U businesses, many software tools fail to exchange information across the enterprise. This creates information silos that limit awareness of events throughout the lifecycle of assets and operations. With low code development, businesses can overcome data isolation using the cloud. Non-IT-workers from any department can build powerful applications to connect the data sources, empowering workers from across the company to make data-driven decisions.

Outpace global market competition

Low code development provides 10 times greater capacity to build custom applications without hiring new software developers. Along with making workers more successful, it gives E&U businesses greater flexibility to incorporate advanced technologies such as artificial intelligence, blockchain and machine learning. Take advantage of these capabilities to outpace global market competition while navigating energy industry transformation.

Share

Related resources

Machine learning-powered etch bias prediction for etch retargeting flow enhancement
Technical Paper

Machine learning-powered etch bias prediction for etch retargeting flow enhancement

The paper proposes a machine learning (ML) based approach to predict etch bias, which can replace the traditional rule-based etch bias tables

Guided random synthetic layout generation and machine-learning based defect prediction for leading edge technology node development
Technical Paper

Guided random synthetic layout generation and machine-learning based defect prediction for leading edge technology node development

Combining synthetic layout generation and machine learning accelerates advanced semiconductor development. It enables early identification of process hotspots and defect prediction, improving efficiency and reducing costs.

A study on the improvement of machine learning (ML)-based defect prediction model reflecting process variations
Technical Paper

A study on the improvement of machine learning (ML)-based defect prediction model reflecting process variations

This paper presents an ML-based risk pattern predictor that combines pattern segmentation, Greedy sampling, and unbiased statistics to find defects and improve yield.