Does your engineering teams already rely on custom tools to analyze process data, calculate key metrics, or classify outcomes? These tools are often validated, embedded in internal workflows, and trusted by users. We don’t replace them. We integrate them. The Yieldmanager lets you embed Python scripts, AI models, Excel macros, or other logic as internal modules. This reduces manual exports, ensures consistent data handling, and makes your existing know-how usable across the full process lifecycle.
Scrtips
Like python or R, often used for CpK calculation or parameter optimization
AI Models
If you are already buildling models for image classification, regression models, time series prediction or other use cases.
Spreadsheet Macros
For formatting reports, batch result processing or customized exports of data into your specified formats.
Your Existing Tools
Any deterministic tool with defined inputs and outputs.
Your tailored software integrations
How it works
You provide the code, macro, or model
Together we define the interfaces and wrap it into a callable module within the Yieldmanager
We map inputs (e.g. measurement values, material types, equipment ID)
Results are stored as structured outputs linked to the trial
We define Role-based access and execution rights
Examples
Post-trial CpK script: runs after trial ends, saves CpK value in trial summary
Force drift monitor: takes raw machine data and plots deviation trends
Report macro: creates a formatted PDF from selected trial parameters
AI model: classifies pass/fail from X-ray images and links label to result
Benefits
No need to rewrite existing code
Avoids unnecessary export/import steps
Supports validation and auditability
Reduces user-side variability and inconsitencies
Enables reuse of logic across teams and sites
Improves speed and consistency of engineering decisions
