The Digital Twin Pipeline: Using Edge Platforms to Model, Simulate, and Optimize Factory Operations

Executive Summary

Digital twins are virtual representations of physical systems that enable real-time simulation, predictive analytics, and informed decision-making. However, their effectiveness hinges on the fidelity and timeliness of operational data. This white paper outlines how Artisan Edge, an edge-native SaaS platform, provides the critical infrastructure needed to build and maintain accurate, actionable digital twins in industrial environments.

The Promise of Digital Twins

Digital twins offer a range of transformative capabilities:

Simulating system behavior before implementing changes
Predicting equipment failures based on sensor trends
Optimizing production parameters via virtual testing
Training AI models using live operational data

Yet many manufacturers fail to realize this promise due to limited access to high-resolution, real-time machine data.

The Data Bottleneck

To function properly, digital twins require continuous streams of structured, contextualized data. Legacy systems often block this flow:

Machines use proprietary or outdated protocols
Control systems lack historical data logging
Cloud-based analytics suffer from latency or connectivity issues
Manual data entry introduces inconsistency and delay

Without a real-time data pipeline, the twin becomes a static replica rather than a living model.

Artisan Edge: Enabling the Digital Twin Pipeline

Artisan Edge addresses the data ingestion gap by serving as the unified interface between physical assets and digital models.

Key functionalities include:

Real-Time Data Acquisition: High-frequency capture from PLCs, sensors, and HMIs
Edge-Level Preprocessing: Noise filtering, timestamp alignment, and formatting
Protocol Translation: i.e. Modbus, OPC-UA, proprietary device communication
Streamlined Cloud Sync: Efficient data handoff to Azure Digital Twins, AWS IoT TwinMaker, and private ML models
Historical Logging: Enables trend analysis and time-series simulations

Use Case: Predictive Twin for CNC Operations

A precision machining firm implemented Artisan Edge to create digital twins of its CNC assets. Key outcomes:

Tool wear forecasting improved lead time for maintenance scheduling
Cycle time variance simulations enabled optimization of feed rates
Energy usage modeling led to off-peak production scheduling and savings

The twin now serves as both a live monitor and a planning tool.

Strategic Benefits

Reduced Downtime: Predict failure modes before they interrupt production
Faster Iteration: Virtually test scenarios without halting machines
Improved Yield: Adjust setpoints based on model feedback to reduce scrap
Empowered Workforce: Operators gain visual insight into system behavior and risk thresholds

Conclusion

Digital twins hold immense value—but only when powered by timely, accurate, and complete machine data. Artisan Edge delivers the edge-native infrastructure to create, sustain, and scale digital twins across industrial operations. It bridges the physical-digital divide and positions manufacturers to lead in an increasingly data-driven future.

To explore integration options or request a demo, contact sales@artisantec.io or visit www.artisantec.io