Manufacturers routinely lose margin due to the gap between what is planned, what is scheduled, and what actually happens on the plant floor.

Developing projects in emerging industries often requires making critical decisions with limited information. Engineering teams must evaluate multiple design alternatives early in the project lifecycle to identify the optimal configuration that maximizes return on investment while minimizing risk.

Electric utilities are under increasing pressure from regulators, customers and stakeholders to integrate renewable energy resources while maintaining reliability, power quality and operational flexibility.

The rapid growth of AI and Machine Learning is creating new possibilities for seismic processing and interpretation.

Refineries are facing increasing crude market volatility, tighter margins and greater operational complexity.

Hunt LNG, a Peruvian company and operator of the PERU LNG Project, is transforming reliability by using AI and machine learning to turn operational data into actionable decisions.

Industrial organizations are increasingly focused on making operational data accessible across plants, business units and cloud initiatives—yet many data platforms struggle to scale without introducing complexity, cybersecurity risk and high operational overhead.

Bharat Petroleum Corporation Limited (BPCL), one of India’s leading energy and refining companies, has relied on Aspen Petroleum Supply Chain solutions for more than a decade.

Upstream teams are under pressure to move faster, reduce costs and make better decisions with increasingly complex data.

Contextualizing equipment data to extend asset life and optimize performance is a major challenge of Asset Performance Management (APM), but the real hurdle is scaling.