Case studies: How organizations turn digital twins into real results
Explore how teams across industry and infrastructure use intelligent digital twins to reduce risk, improve performance, and make better decisions—using their real data, systems, and constraints.
Proof, not promises
No hypotheticals. No inflated claims. Just what worked—and why.
Each case study focuses on:
The operational context
Real constraints and business requirements
The specific problem
Clear challenges teams needed to solve
The approach taken
How the platform was implemented and used
The measurable outcomes
Quantified results and business impact
The lessons learned
What worked, what didn't, and key insights
Find stories relevant to your world
By Industry
By Solution
Selected real-world deployments
Reducing Unplanned Downtime in Manufacturing
How a manufacturer used intelligent digital twins to detect early equipment degradation and cut reactive maintenance
Global manufacturer implements predictive maintenance across rotating equipment, achieving 35% reduction in unplanned downtime and 45% decrease in emergency maintenance costs through early degradation detection.
Optimizing Energy Use Across a Building Portfolio
How facilities teams reduced energy consumption while maintaining occupant comfort
Commercial real estate portfolio uses intelligent building controls and optimization to reduce energy costs by 22% while improving occupant satisfaction scores across 45 buildings.
Improving Supply Chain Visibility Under Disruption
How scenario simulation helped planners anticipate and mitigate cascading delays
Global logistics company uses digital twin simulation to model disruption scenarios, reducing delay propagation by 40% and improving customer delivery reliability during peak disruption periods.
Strengthening Emergency Preparedness for Critical Infrastructure
How simulation improved coordination and response confidence without live risk
Municipal emergency management uses digital twin simulation for crisis scenario training, improving response coordination time by 50% and building confidence across multiple response agencies.
More real-world examples
Predictive maintenance for rotating equipment
Early bearing wear detection reduces maintenance costs by 30% across pump and compressor fleet.
Production bottleneck identification and optimization
Line simulation reveals hidden constraints, increasing throughput by 18% without new equipment.
Load forecasting and peak energy reduction
Utility demand response program reduces peak load by 15% through intelligent forecasting.
Portfolio-level building performance improvement
Multi-site optimization improves energy efficiency by 25% while maintaining comfort standards.
City-scale emergency response simulation
Multi-agency coordination training improves emergency response effectiveness by 35%.
Virtual prototyping to reduce late-stage design changes
R&D simulation reduces physical prototyping costs by 60% and accelerates time-to-market.
What teams achieved
While every environment is different, teams commonly achieved:
Reduced unplanned downtime
20-35% reduction in reactive maintenance
Lower energy consumption
15-25% reduction in operating costs
Earlier risk detection
2-4 week advance warning of system degradation
Faster decision-making
50% improvement in response coordination time
Improved team coordination
Better alignment across teams and systems
Greater resilience
Enhanced ability to handle disruption and change
These outcomes come from better understanding how systems behave—not from automation alone.
See what's possible in your environment
The patterns in these case studies are repeatable—adapted to your data, systems, and constraints.