service.r_and_d()
Industrial R&D often lives as a series of disconnected experiments: many POCs, few decisions, no transfer to production.
Experiments start out of technical curiosity but never state what they should prove, so there is no clear stopping point.
Without exit criteria, each POC becomes an endless project: the team keeps polishing the prototype without ever deciding whether to industrialize it.
Local datasets, personal scripts and diverging versions make results hard to reproduce and compare between iterations.
If the data baseline is not governed, even good results cannot be defended in front of a technical committee or an auditor.
The prototype stays in the lab, while the people who should industrialize it were never involved in the definition.
To avoid orphans, engineering and operations must be involved from the charter stage, not as passive recipients at the end.
Precise definition of the problem, constraints and success criteria for the PoC.
Exploratory analysis of available data, quality assessment and gap identification.
PoC development with weekly iterations, testing on real data and continuous feedback.
Report with results, limitations, recommendations and plan for potential industrialisation.
We make experimentation readable and governable so it does not remain a side project without an outcome.
Components, milestones and priorities to reach a useful proof, not just a demo.
Results, surfaced limits, next decisions and conditions for industrialization.
Hypotheses, constraints, data and validation criteria defined up front — so experimentation produces decisions.
