Production
- operation time
- throughput
- component consumption
- waste
- production pace
- delays
Real-time anomaly detection
Variances caught during the process,not after the period closes.
INVO analyzes the course of production and warehouse operations in real time, identifying behavior that departs from the expected course of the process.
247 kg
- Plan
- 213 kg
- Expected range
- 208–218 kg
- Actual
- 247 kg
Anomaly detected
Component consumption is outside the expected range for this stage of production.
Monitoring scope
Four areas analyzedin real time.
Warehouse
- issues
- returns
- corrections
- unusual movements
- stock discrepancies
Purchasing
- delivery delays
- quantity differences
- price variances
- delivery quality
Operational processes
- variances from standards
- unusual process flow
- recurring sources of loss
Thresholds vs learning models
Instead of defining every threshold,the system derives the norm from data.
Traditional threshold alert
Variance > 10% → alert.
INVO Intelligence
Is the course of this operation unusual for this product, bill of materials, batch size, process stage and current context?
In a classic ERP the user defines the rule: exceeding the norm by 10% raises an alert. The question is on what basis that particular threshold was chosen.
For one product +8% sits inside normal process variability; for another, +3% is already a significant disturbance. That is why the range of the norm is derived from historical data.
How it works
On what basis does the systemrecognize a variance?
01
A reference set of correct operations
For each specific product build, INVO analyzes among others:
Production
- product
- bill of materials
- units
- batch
Material
- issued material
- consumed material
- waste
Context
- time
- line
- process stage
Historical examples of a correct process course, held in the operational data warehouse.
02
Deriving the expected range
For 1 000 units of product X the model derives the expected consumption for the current process context:
Expected range208–218 kg
The range doesn't come from a manually entered limit — it follows from the real behavior of the process reflected in the data.
03
Significance depends on process context
The same percentage can mean different things depending on how variable the process is.
Process variability ±1%
+4% = significant variance
Higher-variability process
+8% = within the norm
Live monitoring
The forecast is compared with the real courseof the process, as it runs.
247 kg
- Plan
- 213 kg
- Expected range
- 208–218 kg
- Actual
- 247 kg
Anomaly detected
+15.9% above the expected level
Unusual warehouse issue for Batch #4821.
Areas to verify
- weighing
- material issue
- waste
- bill of materials compliance
The technology behind it
This class of application uses, among others, Isolation Forest, statistical models, autoencoders and — for richer sequential data — time-series models.
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
Estimated financial effect
Catching variances early limits material loss,complaint costs and production downtime.
A plant with $22M annual material consumption
- Unexplained variances and waste
- 2% = $440K
- Assumed reduction through early detection
- 20%
- Annual effect
- $88,000
Achieving the effect does not require eliminating most of the losses. At production scale, detecting even a small share of variances covers the cost of implementing the system.
ROI calculator
Estimate the potential on your own parameters.
Unexplained variances, overconsumption and waste, expressed as a share of material value.
Value of losses: 440,000 USD
Savings potential
88,000 USD
/ year
≈ 7,333 USD a month
An indicative simulation of potential savings — the values are not a guaranteed result.
Real-time anomaly detection
React during the process,not after the month closes.
See how INVO identifies variances in production and warehouse operations.