As industrial organizations seek to scale digital transformation initiatives, the ability to develop custom business applications fed by real-time operational data helps operational technology (OT) and data teams support enterprise and on-site use cases.

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.

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.

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.

Durante las primeras etapas de un proyecto, la precisión en la estimación de costos es fundamental para su éxito. Una estimación confiable permite comparar alternativas de diseño, evitar sobrecostos y optimizar el costo capital (CAPEX) desde las fases iniciales.

Process simulation tools—such as steady-state and dynamic modeling platforms—provide value far beyond traditional design and analysis.