Predict
What will likely happen?
INVO Intelligence & AI
Predict production, demand and problems,before they happen.
INVO uses data from orders, bills of materials, purchasing, inventory and actual production execution to forecast future demand, detect variances and show where you need to act.
Data from the whole operation
- Orders
- Bills of materials
- Purchasing
- Inventory
- Production
- Flight test
- Deliveries
INVO Intelligence
Detect
What deviates from the expected course?
Recommend
Where should we act?
From reporting to forecasting
ERP shows what happened.Intelligence helps predict what happens next.
Every day INVO records thousands of events: new orders, configuration changes, stock issues, production starts and finishes, actual consumption, waste and deliveries.
That data builds a history of how the plant runs. INVO Intelligence uses it to compare the plan with the most likely course of operations and with actual execution.
Plan
01What did we plan?
5 000 units · finish 14:00
Expected
02What should we expect?
Predicted completion: 14:52
Actual
03What is really happening?
Line 3: −17% vs expected throughput
Act
04What can we do?
Move Batch #4821 to Line 2
INVO Intelligence
Four ways to useyour operational data.
From demand forecasting, through production planning and purchasing, to detecting problems during execution.
AI Production Planning
Is today's production plan actually feasible?
INVO compares planned orders with the history of actual execution and predicts completion time, line load and the places where a delay may appear.
- Planned completion
- 14:00
- Forecast
- 15:05
- Delay risk
- 78%
- Bottleneck
- Assembly Line 3
Demand & Purchasing
How much do we really need to buy for the next production run?
Forecast demand is converted through configurations and bills of materials into specific component quantities, then set against inventory levels, reservations and deliveries in transit.
- Demand forecast
- 24 850 units
- Carbon fiber required
- 1 482 kg
- Available
- 610 kg
- Recommended purchase
- 1 080 kg
Real-time Anomaly Detection
Is the current consumption normal for this production run?
INVO compares current execution with the expected course for the specific bill of materials, volume and process stage.
- Plan
- 213 kg
- Expected range
- 208–218 kg
- Actual
- 247 kg
Anomaly detected · +15.9%
Digital Twin
What happens if tomorrow's order count rises by 20%?
Simulate the impact of changes in demand, line availability, suppliers or staffing on inventory, production, flight test and logistics.
- Demand +20%
- Carbon fiber shortage: 420 kg
- Line 3: 112% load
- +7 people on shift
- 3 delivery lots at risk
Capability map
The full scope ofINVO Intelligence & AI.
Eleven areas across three layers: forecasting and planning, detection and simulation, copilots and AI agents.
Plan & Predict
AI Production Planning
Predict production times, line utilization, bottlenecks and delay risks.
Demand & Purchasing Forecasting
Forecast demand, material requirements, stockout risks and recommended purchase orders.
BOM & Configuration Intelligence
Optimize configurations and bills of materials against cost, component availability, conformity requirements and production capacity.
Cost & Margin Forecast
Predict unit cost, operational costs and margins before production begins.
Detect & Simulate
Anomaly Detection
Automatically detect unusual variances across production, inventory and material consumption.
Predictive Quality
Predict quality risks and potential variances before production is completed.
Digital Twin
Simulate operational scenarios and predict their impact across production, inventory, purchasing and logistics.
Supplier Intelligence
Analyze supplier reliability, quality, pricing, lead times and delivery risks.
Copilots & AI Agents
Intelligence in practice
See how Intelligence worksacross one production day.
A single day generates thousands of data points. INVO can combine them into one picture of the operation and use it for further forecasts and alerts.
- Demand
- Materials
- Purchasing
- Production
- Variance
- Recommendation
05:30
Demand forecast
- Predicted units
- 24 850
- vs last week's plan
- +7.4%
05:31
Material requirements from configurations and bills of materials
- Carbon fiber
- 1 482 kg
- Epoxy resin
- 830 kg
- Aluminum stock
- 1 240 kg
05:32
Component shortage risk
- Shortage
- 420 kg of carbon fiber
Recommended order before 10:00
07:20
Predicted production course
- Plan
- 14:00
- Forecast
- 15:05
- Delay probability
- 78%
- Expected bottleneck
- Assembly Line 3
10:17
Component consumption variance
- Expected range
- 208–218 kg
- Actual
- 247 kg
- Variance
- +15.9%
Check the material issue · Batch #4821
10:20
Recommended action
- Action
- Move Batch #4821 to Assembly Line 2
- Predicted completion after the change
- 14:16
How it works
From operational datato decisions.
INVO doesn't require moving data by hand between analytics tools. The data is created during the system's daily work and can feed the predictive models.
- 01
Collect
We collect data during daily work.
Orders, bills of materials, stock issues, production, waste, purchasing and deliveries form the source data.
- ERP
- MES
- Inventory
- Purchasing
- CRM
- IoT
- Bills of materials
- Orders
- 02
Unify
We combine it in the data warehouse.
Data from different processes is joined so the whole path from demand to actual execution can be traced.
- INVO Data Warehouse
- Bronze → Silver → Gold
- 03
Learn
Models analyze historical relationships.
The system can analyze thousands of earlier runs and look for relationships between the plan, production conditions and the actual result.
- Statistical Models
- Machine Learning
- Time-Series Models
- Neural Networks
- 04
Predict
A forecast is produced.
The forecast covers specific parameters of the operation, not general indicators.
- Expected demand
- Expected material consumption
- Expected production duration
- Expected supplier lead time
- 05
Compare
We compare expectation with reality.
This lets the system detect a variance before it becomes visible in the post-shift report.
- Plan → Expected → Actual
- 06
Act
We turn forecasts into action.
The goal is not the forecast itself, but an earlier and better operational decision.
- Alert
- Recommendation
- Simulation
- Optimization
Data learning
- Plan
- Execution
- Result
- New data
- Retraining
Every further operation grows the dataset that can be used to train and validate the models.
Business impact
A forecast only creates valueonce it changes the operational outcome.
The ranges below apply to a plant with $14–55M annual revenue. The values are estimates based on typical implementation effects — they are not a guarantee of results.
Material cost ↓
Less waste and overconsumption
Detect unusual consumption during the process, before the loss repeats on the next batches.
- Effect
- 1–3% of material cost
- Value
- $80K–850K / year
Inventory ↓
Less capital frozen in stock
Match inventory levels to future demand instead of historical consumption alone.
- Effect
- 10–30% of stock
- Value
- $55K–500K of freed capital
Stockouts ↓
Fewer emergency purchases
Detect future shortages before they affect production execution.
- Effect
- 2–5% of purchasing costs
- Value
- $70K–700K / year
Overtime ↓
Fewer unplanned overtime hours
Predict production overload and bottlenecks before the shift even starts.
- Effect
- 10–25% of overtime
- Value
- $40K–330K / year
Throughput ↑
More output from the resources you have
Make better use of existing lines, shifts and available capacity.
- Effect
- 5–15% throughput
- Value
- $105K–800K effect / year
Service level ↑
More predictable execution
Identify delay risks affecting flight test and delivery lots earlier.
- Effect
- 2–6 pp service level
- Value
- $50K–420K revenue retained
Estimated combined potential
$0.4–3.6M a year
depending on scale, cost structure and process maturity.
ROI calculator
What does even a small variance cost at your production scale?
Value of current losses: 280,000 USD
Potential annual effect
56,000 USD
≈ 4,667 USD a month
The simulation shows the scale of a potential effect. It is not a guarantee of results. The actual effect depends on data quality, cost structure, processes and the scope of the implementation.
INVO Intelligence
See what you can learn from the datayour plant already generates.
In the demo we'll show how data from production, inventory, purchasing and orders can be used to build forecasts matched to your operation.