Digital Twin Model Configuration

There is no digital twin. When the plant manager wants to know 'what happens if we add a second shift on Line 3?' someone builds a one-off spreadsheet with guesses for throughput and bottleneck. The 'model' lives in the head of the industrial engineer who's been here 20 years.

AI cannot simulate production scenarios because no model of the plant exists. Every what-if question requires manual analysis from scratch.

A Visio process flow diagram shows the major production steps with approximate cycle times. An Excel model calculates theoretical throughput for the main lines. The engineer who built it knows which numbers are real and which are estimates. When someone else tries to use it, they get wrong answers because they don't know the assumptions.

AI could read the spreadsheet model, but undocumented assumptions make simulation results unreliable. The model describes an idealized plant, not the real one.

A digital twin model exists in a simulation tool (Arena, FlexSim, or Plant Simulation) with documented parameters: cycle times per station, buffer capacities, shift patterns, and product-specific routings. The model was built during a layout project and validated against a week of production data. Engineers run occasional what-if scenarios for capacity planning.

AI can execute predefined simulation scenarios and report results. The model produces useful directional answers but may not match current plant reality because parameters haven't been updated since the initial build.

The digital twin model configuration is stored as a structured, versioned parameter set in a model repository. Each parameter — cycle time, failure distribution, changeover matrix, buffer size — is a discrete, documented field with source attribution (time study, MES data, engineering estimate). The model can be queried: 'what is the current bottleneck station for Product X at 85% OEE?' and return a simulation-backed answer.

AI can run simulations on demand, compare scenarios, and recommend capacity changes. Model results are trustworthy because parameters are documented and traceable to real measurements.

The digital twin model configuration is a schema-driven entity with explicit relationships to MES production data, equipment asset records, maintenance histories, and product specifications. Each model parameter has a machine-readable lineage: source system, last calibration date, confidence interval, and drift tolerance. An AI agent can ask 'which model parameters have drifted more than 10% from measured reality?' and get a prioritized recalibration list.

AI can autonomously run simulations, detect model-reality drift, and recommend model updates. Full what-if analysis with confidence intervals is possible because parameter quality is tracked.

The digital twin model configuration synchronizes continuously with the physical plant. Cycle times update from MES in real-time. Failure distributions recalibrate as maintenance events occur. Changeover matrices adjust as new timing data flows in. The model IS the plant — a living, continuously calibrated virtual replica that reflects current reality, not a static snapshot from last quarter's project.

Fully autonomous digital twin management. AI maintains, calibrates, and operates the twin as a real-time decision support tool without human model administration.

Ceiling of the CMC framework for this dimension.