As AI's integration in the process of designing and improving industrial infrastructure progresses, governance needs to ...

Inside large engineering organizations, the lifeblood is rarely customer records; it is the designs, issues, and experiments ...

It has likely never been a more exciting or uncertain time to be a data professional. The field is being reshaped by long-building trends that have reached critical mass alongside rapid advances in ...

AI initiatives rarely fail because of model quality. They fail because the underlying data systems were never designed for reliability, context retrieval, or operational consistency.

Heavy machinery is entering a new phase where hydraulics, electronics and embedded software are engineered as one integrated system. Using model-based systems engineering (MBSE) as a framework to ...

Though the AI era conjures a futuristic, tech-advanced image of the present, AI fundamentally depends on the same data standards that have been around forever. These data standards—such as being clean ...

Value stream management involves people in the organization to examine workflows and other processes to ensure they are deriving the maximum value from their efforts while eliminating waste — of ...

Modern control system design is increasingly embracing data-driven methodologies, which bypass the traditional necessity for precise process models by utilising experimental input–output data. This ...