Manufacturing — Digital Twin

See Your Entire Production Line in Real Time

Track every station from raw material feed through final packaging. Identify bottlenecks, predict throughput, and simulate line changes before you make them.

MANUFACTURING PRODUCTION LINE — LIVE DIGITAL TWINFEED72%CNCBOTTLENECK88%ASSEMBLY65%INSPECT78%PACK70%OEE DASHBOARD91.0%Availability85.0%Performance97.0%QualityOVERALL OEE75.0%HOURLY THROUGHPUTTARGET8:00359:004210:004511:004212:003413:002714:002515:0029DIGITAL TWIN INSIGHTSBOTTLENECK ANALYSISConstraint: CNC StationUtilization: 88.0% (target <85%)Cycle time deviation: +8.0% above baselineRecommendation: Reduce batch size or add parallel capacityQUALITY CORRELATIONDefect Rate: 3.00%Top correlator: CNC spindle temp (42.0C)Confidence: 89% (R-squared)Alert threshold: spindle temp > 48CSHIFT FORECASTPredicted: 419 units/shiftTarget: 336 units/shiftVariance: 24.9% vs targetOn track for shift targetLINE STATUSRUNNINGOEE75.0%BOTTLENECKCNCTHROUGHPUT29 units/hrNEXT CHANGEOVER14:30 (2.5h)SIMULATION INSIGHTAdding a parallel CNC station would increase line throughput by ~18% and reduce bottleneck utilization to 72%.Estimated ROI: 8.2x in first year based on current product mix and demand forecast.

How It Works

From raw PLC signals to production intelligence in three steps.

01

Connect

Tap into existing PLCs, CNCs, and I/O modules via OPC UA, Modbus, or EtherNet/IP. No production line changes required.

02

Model

Station cycle times, buffer levels, OEE components, and quality metrics are calculated from raw signals — not manual data entry.

03

Simulate

Test line balancing changes, changeover sequences, and capacity additions on the digital twin before disrupting production.

What You Can Simulate

Every production scenario — tested safely on the digital twin before committing.

Line balancing

Simulate redistributing work content across stations to eliminate the bottleneck and increase throughput.

Changeover scheduling

Model different product sequencing to minimize total changeover time across a shift.

Quality root cause

Correlate a spike in defects with equipment parameters (tool wear, temperature drift) to find the root cause.

Capacity what-if

Add a parallel station in the model and see the throughput impact before approving the capital expenditure.

Predictive maintenance

Schedule station maintenance during planned changeovers to eliminate unplanned production stops.

Shift handover briefing

Auto-generate a shift summary with OEE, bottleneck events, quality alerts, and predicted next-shift output.

Traditional MES vs. Digital Twin

MES tells you what happened last shift. A digital twin tells you what will happen next.

FeatureTraditional MESTwinEdge Digital Twin
Production visibilityEnd-of-shift reportsReal-time per-station metrics
Bottleneck detectionWalk the lineAutomatic constraint identification
OEE decompositionManual data entryAuto-calculated from sensors
What-if analysisSpreadsheet modelsLive simulation with real data
Quality root causeAfter-the-fact investigationReal-time correlation engine
Changeover optimizationTribal knowledgeData-driven sequencing

How does the simulation engine work? →

12%
OEE Improvement
Targeted bottleneck elimination and reduced micro-stops drive measurable OEE gains
34%
Fewer Unplanned Stops
Condition monitoring on critical stations catches issues before they halt the line
8.2x
First-Year ROI
Throughput gains and reduced scrap pay back the investment within months

See Your Production Line as a Digital Twin

Share your line layout and station configuration. We will build a live demo showing your actual bottlenecks, OEE, and throughput predictions.