INDUSTRY & PRODUCTION
Production yield optimization: adjust what actually moves the yield
Production yield optimization with TIMi identifies the parameters that influence the rate of conforming parts, to adjust the process and reduce scrap and rework, and shows the relative influence of each parameter.
Influential parameters
See which settings move the yield, and which do not.
Combinations
The AutoML looks for combinations of factors, not only single effects.
Explained recommendations
Each adjustment comes with the factors that justify it.
HOW IT WORKS
How production yield optimization works.
Three steps, in one engine, on a standard server with no cluster.
1. Prepare the data
Anatella gathers the production parameters (machine settings, raw materials, environmental conditions) and the quality results of each batch or part, then structures them into variables.
2. Build the model
Modeler links the production parameters to the conformity rate obtained, to identify which ones have the most influence on the yield.
3. Recommend and explain
Each recommended adjustment comes with the factors that justify it, and the relative influence of each parameter identified.
Anatella
ModelerGETTING IT RIGHT
Choices that shape a yield model.
The goal is to adjust what matters, not everything.
Start from existing variability
The model can first rely on the variability already present in your production data, before considering targeted trials to confirm an identified lever.
Look at combinations
Modeler automatically keeps the few variables that matter among many, so a large number of production parameters can be analyzed at once. The AutoML is designed to identify combinations of factors, not only effects taken one by one.
Re-check when inputs change
A change of raw material or machine configuration can change the relationships identified. A new modeling cycle checks whether the levers are still valid.
EXPLAINABILITY
A recommendation says what to change. The factors say by how much.
An adjustment recommendation alone says what to change, not why or by how much. A white-box model shows the relative influence of each parameter on the yield, so production teams can choose between possible levers and prioritize the ones with a real effect, instead of adjusting by guesswork.
For production teams
Setting adjustments can be justified and documented, which helps field teams adopt them.
For continuous improvement
Identifying the parameters that really matter avoids spreading the optimization effort over secondary factors.
For model owners
Modeler’s built-in Drift Report shows when a model loses accuracy, so you update it when needed instead of on a fixed calendar.
DATA
What data a yield model uses.
Anatella connects to more than 80 sources, so this data can be gathered without prior custom development.
Process settings
Machine settings and process control parameters.
Raw materials
Characteristics of the materials used.
Environmental conditions
Temperature, humidity, depending on the context.
Quality results
Yield or quality for each batch produced.
PROOF
Closest references.
We do not yet have a published customer story dedicated to yield optimization. These two deployments are related: defect classification that reduces waste, and root-cause work on a production sequence.
Sheets routed to the right customer, with less waste
Classifying defects with more than 97% accuracy decides which customer each sheet goes to, which drastically reduces waste.
A 20-step sequence reconstructed to find a vibration source
The team reconstructed the whole manufacturing sequence to pinpoint the mechanical command causing vibrations across the factory.
FAQ
Common questions.
Do we need dedicated production trials?
Not necessarily at first. The model can rely on the variability already present in existing production data, before considering targeted trials to confirm an identified lever.
How many parameters can be analyzed at once?
Modeler automatically keeps the few variables that matter among many, so a large number of parameters can be crossed at once. The practical limit is the capacity of the hardware used.
Can it detect an interaction between several parameters?
Yes. The AutoML of Modeler is designed to identify combinations of factors, not only effects taken one by one.
Do we need to redo the model if a new raw material is introduced?
Yes. A change of raw material or machine configuration can change the relationships identified. A new modeling cycle checks whether the levers are still valid.
SEE FOR YOURSELF
Test it on your own data.
Run TIMi on your own data with a free trial, or talk to us about your case.
