01
Mirroring the real course of the operation
The data warehouse collects the history of transitions across the whole process chain.
- Orders
- Bills of materials
- Purchasing
- Inventory
- Production
- Flight test
- Logistics
INVO Digital Twin
Test a decision on a digital model of the plantbefore you roll it out.
Digital Twin combines operational data, predictive models and production constraints to determine how a decision under consideration affects the course of the whole operation.
Scenario analysis
- Demand up 20%
- One production line down
- Delivery delayed by 24 h
- Demand+20%
- Components+14.2 t
- InventoryCarbon fiber shortage: 420 kg
- ProductionLine 3 capacity exceeded
- Staffing+7 operators
- Flight test+1 h 15 min
- Delivery3 lots at risk
Scope of use
Decision questionsDigital Twin can answer.
- 01
Can the plant increase production by 20%?
- 02
What follows from taking one production line out?
- 03
How will a bill of materials change affect production cost?
- 04
Does executing the plan require an extra shift?
- 05
What is the impact of a delayed delivery on the production schedule?
- 06
Which schedule variant minimizes overtime?
Simulation → comparison → decision
Three stages from a decision questionto a chosen variant.
- 01
Simulation
Compute many scenarios without touching current production.
- 02
Comparison
Cost, lead time, capacity utilization, waste and risk level.
- 03
Decision
Pick the variant with the best predicted outcome.
From recording to an autonomous operation
Five levels ofoperational maturity.
- 11
Recording
INVO
Recording data and managing processes.
- 22
Understanding
Operational Intelligence
A coherent picture of how the whole operation runs.
- 33
Forecasting
INVO Intelligence
Forecasts for demand, production, purchasing and risks.
- 4
You are here
4Simulation
You are hereINVO Digital Twin
Assessing the consequences of a decision before it is made.
- 55
Optimization
AI Planner
Recommending the variant with the best predicted outcome.
How it works
How the digital modelof the plant is built.
Digital Twin is not a single model — it is a combination of several layers.
The first step is mirroring the real course of the operation; the second is training models for its individual segments.
- Data
- The model learns the relationships
- Forecast
- Decision
- P&L
02
Dedicated models for individual areas
Instead of one universal model, a set of specialized models is used:
- Demand Model → forecast order volume
- Production Duration Model → forecast completion time
- Consumption Model → forecast actual consumption
- Waste Model → forecast loss level
- Supplier Model → forecast actual delivery date
- Capacity Model → bottleneck location
Each model is trained and validated for its specific forecasting task.
03
Predictive models joined with optimization
Digital Twin is built from four cooperating layers:
Machine Learning
forecast of the likely course
Optimization
choosing the optimal variant within the constraints
Simulation
assessing the consequences of individual scenarios
ERP data
the real state of the operation as the entry point
The technology behind it
The predictive layer (gradient boosting, time-series models) feeds an optimization solver and a scenario simulation engine that runs on the plant's operational constraints.
The model architecture is not a fixed part of the product — the choice depends on the characteristics of the client's dataset.
Pipeline
- collect
- validate
- train
- backtest
- compare models
- deploy
- monitor drift
One changed input
Change a single input parameter.See the consequences across the chain.
Demand up 20% — Digital Twin determines what follows across the whole process chain, and then computes alternative ways of executing the plan.
- Demand24 000 → 28 800
- Materials+4.8 t
- Inventory420 kg of carbon fiber missing
- PurchasingEmergency order required
- ProductionLine 3 → 112% of capacity
- Staffing+7 operators
- Flight test+1 h 15 min
- Delivery3 lots at risk
Alternative variants
Variant A
+7 employees
+$2,300
- On-time rate
- 99%
Variant B
an extra shift
+$4,000
- On-time rate
- 100%
Variant C
Recommendedreschedule + Line 2
+$800
- On-time rate
- 98%
This is a decision-support layer, not another reporting dashboard.
Sources of the financial effect
The effect appears in several areas at once,so it can't be reduced to a single indicator.
Waste ↓
Less overproduction and material loss
Inventory ↓
Lower level of frozen working capital
Overtime ↓
More accurate planning of people
Throughput ↑
Higher volume from existing infrastructure
Emergency buys ↓
Fewer emergency purchases
Downtime ↓
Higher utilization of available capacity
ROI calculator
Estimate the potential on your own parameters.
The Digital Twin effect comes from several sources at once — the calculator covers one of them.
Value of losses: 425,000 USD
Savings potential
85,000 USD
/ year
≈ 7,083 USD a month
An indicative simulation of potential savings — the values are not a guaranteed result.
Assess the consequencesbefore you make the decision.
See how Digital Twin determines the impact of changes in demand, capacity and delivery conditions on the whole operation.