Before integrating predictive capabilities into a DORG, an organization must know if its data is ready, which ML model is best suited for its use case, and if the investment produces a measurable return. DORG University provides this service directly through the application of its proprietary module — not available on the market nor transferable to Faculty or third parties.
The service is divided into two distinct phases. In the first, DORG University analyzes the client’s dataset, calculates characterization metrics, identifies relevant independent and dependent variables, optimizes hyperparameter selection, and trains the model by identifying predictive patterns. In the second phase, the validation module estimates the algorithm’s performance as the dataset grows — at 2x, 5x, and 10x the available data — and translates technical metrics into concrete economic indicators: minimum performance for production release, payback period, ROI over a selectable horizon, and reallocable man-hours.
The result is a structured business case — with best-case and worst-case scenarios — that supports the investment decision with verifiable quantitative data. The service includes support for dataset cleaning and preparation in the preliminary phase.
Main areas of application: predictive maintenance in manufacturing, demand forecasting in supply chain, identification of anomalies and fraud in the financial sector, and predictive process quality.
