Use an operational data model
Orders, operations, resources, material and timestamps should be modeled consistently so analysts do not rebuild joins differently for every dashboard.
Standardize KPIs
Publish formula, unit, time boundary, exclusions and data source for each KPI. This is especially important for OEE, yield and schedule attainment.
Preserve drill-through
Aggregate metrics should link back to production events. Users trust analytics more when they can see the underlying downtime, quantity or quality records.
Advanced analytics
Statistical and machine-learning models can identify patterns, but deployment should include validation, monitoring and clear decision ownership rather than treating model output as automatic truth.
MES terminology and boundaries vary among plants and vendors. Validate data ownership, safety impacts, interfaces and change control against your actual manufacturing architecture and approved procedures.