There is no fixed correct number. Most manufacturing organizations should limit KPI categories to a small, governed set, often about 4 to 8, so reporting stays usable and ownership stays clear. The right count depends on operating model, data quality, regulatory traceability needs, and how well metrics can be maintained across MES, ERP, QMS, and legacy systems.

Prioritize AI use cases with a stage-gate approach, not by novelty. Rank opportunities by business impact, data readiness, workflow fit, validation burden, integration complexity, cybersecurity constraints, and ability to scale across plants without disrupting qualified processes. The best first use case is usually measurable, low-risk, and operationally adoptable, not the most ambitious.

100% OEE is a theoretical benchmark where equipment runs only good product, at maximum designed speed, with no unplanned stops or slowdowns. In real regulated, brownfield environments it is essentially unattainable. It can be useful as a reference point, but treating 100% OEE as a realistic target can drive bad behavior and data distortion.

An OEE of 85% means that, after accounting for losses in availability, performance, and quality, the asset is producing 85% of the theoretical good output it could deliver in the same scheduled time. It is not a certification or guarantee and can be misleading if data sources, definitions, or loss accounting are inconsistent across lines, shifts, or sites.