Plan
5 000 units
Start: 10:00
Planned completion: 13:00
AI Production Planning
See if the production plan is feasible,before you launch the order.
INVO analyzes planned orders, bills of materials, available lines, resources and the history of actual execution to predict completion time, production load and delay risk.
Units
24,850
Orders
186
Lines
7
Line 1Line 2Line 3
+45 min
Planned completion 14:20 Predicted completion 15:05
Delay risk78%
Assembly Line 3 · expected bottleneck
Recommendation
Move Batch #4821 to Line 2
Plan vs reality
The production plan doesn't always showhow production really runs.
The plan may assume the order finishes at 14:00. In practice the outcome depends on what happens on the floor: batch sizes, line load, job sequencing, staff availability and delays carried over from earlier stages.
INVO uses the history of actual production to estimate — before the shift even starts — how long production will likely take and where a problem may appear.
Actual conditions
- Line 3 heavily loaded
- earlier order +18 min
- bigger batch than usual
- flight test close to full capacity
INVO forecast
Predicted completion
13:34
PlanPredicted completion
13:00
+34 min
Delay risk: 72%
The plan says what should happen. INVO uses historical data to predict what will most likely happen for real.
Before production starts
What can you knowbefore the shift begins?
When will production actually finish?
Predicted completion time for the order and for the whole plan.
Which lines will be overloaded?
Forecast utilization of the available capacity.
Where will a bottleneck appear?
Processes likely to limit execution of the plan.
How many components will we actually use?
An execution forecast that accounts for historical variances.
Which orders are at risk?
Probability of missing the schedule.
Is the current staffing enough?
Predicted staffing needs for individual stages.
How it works
From production history tothe next shift's forecast.
INVO uses data that already exists from planning and executing production. It compares earlier plans with actual execution and predicts how the next orders will run.
Production history
Plan + actual execution
AI
Finds the relationships
Forecast
What will likely happen tomorrow?
- Plan
- 14:00
- Forecast
- 15:05
- Risk
- 78%
01
We collect real production data
INVO records, among others:
- what we produced
- how much
- on which line
- how long
- at what throughput
- what delays occurred
This builds a history of how production really ran — not just how it was planned.
02
AI finds recurring relationships
The models analyze earlier runs and check what influenced production time and throughput. For example, the system may notice:
At this production volume and flight-test load, similar orders took on average 12% longer than the plan assumed.
You don't have to define each such relationship by hand — the model finds them in the historical data.
03
We predict the next production run
When a new plan is created, INVO uses earlier data to estimate:
- when production will finish
- which lines may be overloaded
- where a bottleneck may appear
- which orders are at risk of delay
Instead of
Planned completion: 14:00
You also get
- Predicted completion: 15:05
- Delay risk: 78%
- Problem: Line 3 overloaded
The more actual runs there are in the history, the more data we have to train and improve the predictive models.
Models & technology
Different questions requiredifferent models.
All the models draw on data collected in the INVO data warehouse. It combines the history of orders, bills of materials, production plans, line operation, actual execution times, component consumption, downtime and other events from ERP and MES.
On that basis we match the model to the specific question — for example how long production will take, where an overload will appear, or what the delay risk is.
Where do the models get data from?
ERPMESWMSProduction data
INVO Data Warehouse
- Orders
- Bills of materials
- Production plans
- Actual times
- Line operation
- Consumption
- Downtime
- Throughput
Plan
How production was planned
Actual
How it really ran
AI models
The data warehouse builds a history of how production was planned and how it really ran. That is the data we train and feed the predictive models on.
Forecasting model
How long will production actually take?
Models analyzing many factors
The model analyzes thousands of earlier runs and checks which conditions influenced their actual outcome.
It may notice, for example, that a certain combination of a large batch, a specific line and high load historically resulted in longer production.
When a similar order appears, it uses those relationships to predict its outcome.
Plan180 min
AI forecast214 min
Use cases
- Production time
- Delay risk
- Predicted throughput
- Component consumption
Benefit: A more realistic forecast based on actual runs.
Gradient Boosting
LightGBM · XGBoost · CatBoost
Time-series models
How will the situation change over time?
Models analyzing the course of production
These models don't only look at the final result. They analyze how load, production pace and other parameters change over the following hours.
That lets them predict at which point an overload or a drop in throughput will occur.
Line 3 · now
- 82%
- 10:3091%
- 11:30103%
- 12:30112%
Expected capacity overload
Use cases
- Line load
- Production pace
- Future bottlenecks
- Capacity utilization
Benefit: You see not only the current situation, but also the direction production is heading.
Time-Series Forecasting
N-HiTS · Temporal Fusion Transformer
Risk model
How high is the risk that the plan won't be met?
Models accounting for uncertainty
Production is not 100% predictable. That is why the model can show not only the most likely outcome, but also the range of possible outcomes and the level of risk.
Instead of one seemingly certain finish time, the planner sees how confident the forecast actually is.
- Most likely finish
- 14:52
- Expected range
- 14:40–15:10
- Delay risk
- 84%
Use cases
- Delay risk
- Overload risk
- Completion time range
- Plan uncertainty assessment
Benefit: The planner decides based on a level of risk, not on a single seemingly certain number.
Probabilistic Forecasting
Quantile Regression · Conformal Prediction
And when the process is more complex?
With large volumes of data, many parallel lines, long operation sequences or data from IoT devices, we can also use neural networks and deep learning models. They allow more complex relationships to be analyzed, but we use them when tests show they improve forecast quality.
Neural Networks · Deep Learning · Sequence Models
A more complicated model does not always mean a better forecast.
We pick the model that best predicts actual execution on the client's own data.
Optimization engine
The model predicts a problem.The system helps find a better plan.
If the models predict that one of the lines will be overloaded, the next question is: what can we change?
The optimization layer can compare different ways of executing the plan, accounting for available lines, job sequencing, staffing, deadlines, changeovers and costs.
Current plan
15:12
Delay risk: 84%
AI forecast · Line 3 overloaded
Alternative scenarios
Scenario A
+4 employees
+$500
Additional cost
- Finish
- 14:31
Scenario B
RecommendedMove 2 orders to Line 2
+$90
Additional cost
- Finish
- 14:16
The forecast does not end with information about a potential problem. It can be used to compare variants and find a better way to execute the plan.
ERP + MES + Production
INVO Data Warehouse
What did earlier runs look like?
AI models
What relationships exist in the data?
Forecast
What will likely happen tomorrow?
Optimization
What alternatives do we have?
Recommendation
Which variant gives the best predicted outcome?
From forecast to action
A forecast alone doesn't change production.A decision does.
Alert
Line 3 will likely exceed capacity by 12%.
Recommendation
Move two orders to Line 2.
Simulation
Check the impact of the change before you update the plan.
Current plan
As scheduled
15:12
Overtime: 72 min
Delay risk: 84%
Scenario A
+4 operators
14:31
Additional cost: $500
Delay risk: 21%
Scenario B · recommended
Move 2 orders
14:16
Additional cost: $90
Delay risk: 9%
Business impact
A better plan before the start meansfewer costly corrections during the shift.
Overtime ↓
Fewer unplanned overtime hours
Waiting ↓
Less downtime and waiting
Throughput ↑
More output from the lines you already have
On-time ↑
Higher on-time delivery rate
Estimate the potential on your own production costs.
Current avoidable cost: 850,000 USD
Savings potential
127,500 USD
/ year
≈ 10,625 USD / month
The simulation shows the potential scale of the effect and is not a guarantee of the result. The actual outcome depends on processes, data quality and the scope of the implementation.
Check whether tomorrow's plan is feasible,before production starts.
See how INVO can use the history of actual execution to predict production time, overloads and delay risk.