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.
Asset Performance Management (APM) software applies AI, machine learning, and physics-based models to asset condition data to predict failures, estimate remaining useful life, recommend maintenance strategies, and — in advanced implementations — close the loop autonomously back into operational control. APM sits in the Intelligence (I) and Live Data (L) layers of the FIELD framework. It is distinct from EAM: EAM manages asset data and work execution; APM manages asset intelligence and predictive capability.
A PLM process pattern in which operational field data — sensor readings, failure events, service actions — automatically triggers engineering review or change processes, feeding real-world performance back into the design record.
The digital thread is the connected data backbone that links all lifecycle phases of a product—requirements, design, simulation, manufacturing, quality, and service—so that data flows coherently from engineering intent through to field performance and back. A complete digital thread enables traceability in both directions: forward (from requirements to as-built product) and backward (from field failure to specific design decision and part revision). The digital thread is the primary argument for integrated PLM suites over best-of-breed architectures.
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).
Model-Based Systems Engineering (MBSE) is the formalized application of modeling to support system requirements, design, analysis, verification, and validation throughout the development lifecycle. Where traditional systems engineering relies on documents, MBSE replaces documents with executable, interconnected models — typically authored in SysML or similar languages and managed in tools like Cameo, Rhapsody, or Codebeamer. MBSE is the engineering discipline; PLM provides the governance and configuration management that keeps the models authoritative across their lifecycle.