The pre-aggregated operational-analytics rollup for the 3PL fulfillment domain. One row per (ship_month × client_segment × site_region × temp_class × carrier) carrying raw count columns (never pre-divided rates): order_count, units_shipped, otif_count, on_time_count, in_full_count, accurate_lines, total_lines. Only shipped orders are counted (ship_date is not null), so the counts are correct denominators for on-time/OTIF rates. ship_month is toStartOfMonth(ship_date), so this is a trend surface. It feeds two metrics — fulfillment (OTIF/on-time/in-full rates) and pick_accuracy (accurate_lines/total_lines) — which recompute rates with nullif formula measures so multi-bucket roll-ups stay window-correct. Nullable dims are coalesced to unknown so they stay MergeTree-safe and visible.
- highDividing count columns client-side
OTIF/pick-accuracy are ratios of two count columns; divide them client-side across buckets and you get a biased average-of-averages. Request the otif_rate / pick_accuracy formula measures — the metric layer applies nullif and re-divides window-correctly.
- mediumReading a single ship_month as a full-period result
Grain is keyed on ship_month; sum counts across the relevant month range before dividing, don't read one month in isolation.
- mediumExpecting per-client rows here
This rollup is de-identified to segment/region — it carries no client_id. For client-scoped fulfillment use mart_client_scorecard / mart_order_outcomes (both row-policy scoped).
- “What was OTIF for life_sciences clients in the EU last quarter?”
- “How does pick accuracy trend by temp class?”
- “Which carrier has the best on-time rate for frozen goods?”