SEP 22, 2026
Water Distribution Operational Digital Twins at Scale
UTILIZED SOFTWARE
iTwin, OpenFlows, ProjectWise, OpenFlows WaterGEMS, OpenFlows HAMMER, OpenFlows WaterSight
LOCATION
Netherlands
ORGANIZATION/COMPANY
Evides N. V.
In Rotterdam, Evides N.V. supplies drinking water to 2.5 million customers through a 14,000-kilometer network — an operation where every hour of delay carries a real environmental cost. For years, hydraulic models could not be updated quickly enough to keep pace with the network. Calibration lag meant that when a pipe burst, crews had no reliable real-time picture to work from, and data scattered across disconnected systems slowed every decision. The result was longer emergency response, higher non-revenue water losses, and energy spent producing water that never reached a customer.
Evides changed that by deploying two real-time operational digital twins, built on AI within OpenFlows WaterSight and connected to WaterGEMS, HAMMER, and ProjectWise. Readings from 609 AMI meters and 1,400 network sensors now flow into a single source of truth that unites operational, asset management, and customer data. Demand forecasting and leak geolocation are automated, and transient analyses run directly against live conditions instead of waiting on manual data conversion.
The environmental payoff is measurable. Burst-induced water loss has been cut in half, preserving 42,000 cubic meters of freshwater every year. Leak detection and repair times dropped by more than 50 percent, and crews now respond to emergencies 60 percent faster, halving customer service disruptions. Because a leaner network requires less pumping, less treatment, and fewer chemicals, the utility's energy use and carbon emissions fell alongside its water losses — while annual production and distribution costs came down by 100,000 euros. The full deployment cost 300,000 euros and roughly 400 work hours.
The story here is not only about efficiency. It is about safeguarding a scarce resource and delivering reliable, clean water to a city — proof that digital twins can turn operational insight directly into sustainability gains.
