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.

As AI and digitalization transform industrial operations, disconnected systems and siloed data can limit visibility, slow decisions and hinder innovation. Unlocking their potential requires secure, accessible data with operational context.

Engineering, procurement and construction (EPC) firms are under increasing pressure to deliver projects faster, at lower cost and with greater confidence amid volatile market conditions and growing project complexity.

Geomodels are foundational for understanding subsurface assets and informing field development strategies and drilling plans.

Process engineers are under pressure to optimize production while managing variable feedstock and shifting process conditions. Aspen Hybrid Models™ simplifies the use of machine learning in process simulation, delivering faster, more accurate models that provide timely insights to guide operations decisions.

Designing a distillation column requires balancing thermodynamics, hydraulics, internals, energy use and cost—often with limited information early in a project.

Refinery scheduling is more complex than ever. Volatile crude markets, shifting demand and constant operational changes can make it difficult to respond quickly while protecting margins.

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

In chemical manufacturing, reliability teams must manage critical machinery and instrumentation in a fast-paced, complex environment.

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