In-depth analysis tagged iot — covering PLM history, vendor strategy, and the technical decisions reshaping engineering software.
2 articles
Digital twins have outgrown the engineering lab. Scaling them from prototype to enterprise asset requires a PLM integration architecture that most organizations have not yet built.
Most digital twin programs stall because they build beautiful models disconnected from real sensor data. Connecting IoT telemetry to PLM closes the loop between as-designed and as-operated.
An operational discipline using real-time sensor data, predictive analytics, and digital twin models to monitor the health and performance of physical assets, predict maintenance needs, and optimize availability and efficiency.
The continuous cycle where field data (customer usage, failures, maintenance records, warranty claims) flow back to product design and PLM for incorporation into next generation designs.
The governed lifecycle of information needed to operate, improve, maintain, and transform an industrial system — connecting engineering, ERP, MES, maintenance, and operations.
A digital twin is a virtual representation of a physical product, asset, or system that is synchronized with real-world data to reflect current state, predict future behavior, or support operational decisions. In engineering, digital twins range from design-phase simulation models updated with as-built geometry, to operational twins that receive live sensor data from deployed assets and run predictive models in real time. Digital twins that connect simulation to operations require integration between the simulation platform, the PLM system (which holds the product model), and the MES or IIoT layer (which provides operational data).
A systems engineering methodology that uses formalized models — rather than documents — as the primary means of expressing, communicating, and managing system requirements, design, analysis, and verification across the lifecycle.