New EU cross-border waste rules are now in force. Try our free platform and get compliant today.
Digital Twin
Visualize, test and compare different operational scenarios before you choose between them. Evreka Digital Twin turns your own operational data into a living virtual model, so every decision is backed by evidence instead of a guess.
Any operation runs on hundreds of interacting variables (assets, schedules, workflows, costs), and a single change in one corner can ripple into service levels, timelines and costs elsewhere in ways that are hard to predict from a spreadsheet alone. Evreka Digital Twin models those interactions directly from your own operational history, so you can build a scenario, run it virtually, and see its cost, efficiency and sustainability impact before you commit real resources to it. One scenario moves forward with confidence; the other is ruled out before it costs you anything.
Managing complexity in daily operations
Most operations run on hundreds of interacting variables, and most organizations still plan around them with spreadsheets, intuition or limited historical data. A single change in one corner of the operation can ripple into service levels, cost and output elsewhere, and without a way to model that ripple effect, the impact is almost impossible to anticipate before it happens.
No way to test before committing
A resourcing or process change is usually decided on paper, then corrected in the field once the real costs and bottlenecks show up.
Digital Twin fix: run the change as a virtual scenario first, and see the cost, time and capacity impact before it touches a single asset.
Spreadsheets can't model ripple effects
A single change in capacity, scheduling or workflow affects cost and output elsewhere in the operation, but static spreadsheets don't show that chain reaction.
Digital Twin fix: simulations model how operational variables interact, so the downstream impact of one change is visible upfront.
Decisions rely on assumptions, not data
Without a way to quantify trade-offs, planning defaults to intuition and past habit rather than evidence from the operation's own performance.
Digital Twin fix: every scenario is built on your own operational data, not generic assumptions.
Field testing is slow, costly and risky
Validating a change by trying it in the real world risks live service disruption if it goes wrong, and weeks of observation before you know either way.
Digital Twin fix: instant virtual what-if analysis instead of real-world field testing, so issues surface before rollout, not after.
These pressures show up differently depending on where you sit in the chain. Waste management operators feel it as unbalanced fleets, unpredictable routes and service levels that are hard to guarantee. Waste producers and manufacturers feel it as on-site collection that's hard to size correctly, and compliance reporting with little visibility into what drives the numbers. Digital Twin is built to answer both. See how under Applications Across The Waste Ecosystem below.
Evreka Digital Twin
A virtual model of your operations, built from the data you already capture. Adjust the variables that matter, run the scenario, and see the outcome before you commit resources to it, turning planning from a reactive process into a predictive, data-driven one.
Predictive Planning
Anticipate bottlenecks, plan resources ahead, and prevent inefficiencies before they happen.
Resource Optimization
Achieve higher utilization of your assets, fleets and manpower.
Operational Efficiency
Reduce unnecessary trips, minimize resource usage, and increase throughput.
Strategic Decision Support
Compare investment or process-change scenarios against real operational data.
Sustainability Alignment
Identify the configurations that reduce emissions while improving performance.
Digital Twin vs. traditional planning
Traditional planning answers "what happened?" A digital twin answers "what will happen if we change this?"
| Dimension | Traditional Planning | Evreka Digital Twin |
|---|---|---|
| Approach | Reactive | Predictive |
| Data source | Historical reports | Live simulation on real operational data |
| Decision method | Assumption-driven | Data-driven |
| Testing speed | Slow, real-world field testing | Instant, virtual what-if analysis |
| Implementation risk | High (issues surface after rollout) | Lower (issues surface before rollout) |
Built for every role in the waste value chain
Municipalities & Public Authorities
Simulate service coverage, fleet distribution and budget planning by modeling collection frequencies, fleet sizes and shifts before implementing real-world changes, for evidence-based, transparent public operations.
Private Waste Management Companies
Test adding or reallocating vehicles, introducing new waste streams, or outsourcing service zones, and see how route structure and shift timing affect collection time, productivity and profitability.
Recycling & Treatment Facilities
Forecast inbound material flows from changes in collection or segregation strategy, and plan sorting, storage and processing capacity with greater accuracy for stable material inflow.
Waste Producers & Manufacturers
Model on-site waste generation, storage and internal collection workflows, simulating container placement, pickup frequency, segregation setup and workforce scheduling.
Common questions
Also worth knowing
There's nothing extra to set up. Digital Twin runs on the operational data your systems already collect, so you can start testing scenarios without a separate data project.