Inventory Statistics

Computes per-partition inventory KPIs — current inventory count, rolling sales counts, days-supply, and (optionally) average days-on-lot — grouped by configurable dimensions like dealer, neighborhood, country, or carline.

The component is a pure aggregation engine over enriched inventory: it does no date arithmetic of its own, but applies a configurable rolling window over the sold_date_column provided by Inventory Enrichment. One Dagster asset is emitted per grouping, partitioned by day.

Inputs

• inventory — Enriched inventory asset key. Defaults to <oem>/consolidated/inventory; typically set to <oem>/inventory_enriched so sold-date and active-filter columns are present.
• joins — Reference tables left-joined onto inventory before aggregation, exposing the dimension columns referenced by group_by (e.g. dealer → country, model → carline).
• sold_date_column — Column used for the rolling sales window. May be all-null for OEMs without a sold-date signal — KPIs degrade gracefully (inferred_sales_count_{n}D = 0, days_supply_{n}D = inf).
• days_on_lot_column — Optional. When set, every grouping also emits avg_days_on_lot_{n}D per window.
• active_filter — Polars-SQL expression selecting active vehicles for current_inventory_count. Omit to count all rows.
• windows — Rolling window sizes in days (default [90]).

Groupings

Each grouping defines the GROUP BY dimensions for one output asset:

type: ai_core.components.InventoryStatisticsComponent
attributes:
  oem: mb
  name: inventory_statistics
  start_date: "2025-01-01"
  inventory: mb/inventory_enriched
  sold_date_column: inferred_sold_date
  days_on_lot_column: days_on_lot
  active_filter: "inferred_sold_date IS NULL"
  windows: [90]
  joins:
    - asset: mb/consolidated/dealers
      join_on: [dealer_id]
      fields: [country]
    - asset: mb/dealers/neighborhoods
      join_on: [dealer_id]
      fields: [neighborhood]
    - asset: mb/consolidated/models
      join_on: [model_id]
      fields: [carline_id]
  groupings:
    - key: dealer_neighborhood
      group_by: [dealer_id, neighborhood]
    - key: country
      group_by: [country]
    - key: carline
      group_by: [carline_id]
    - key: dealer_model
      group_by: [dealer_id, model_id]

Field	Description
key	Leaf segment of the asset key. Lands at <oem>/inventory_stats/<key>
group_by	Columns to aggregate by. Must be present on the joined frame

Output columns

Each grouping asset contains the group_by columns followed by:

Column	Description
current_inventory_count	Row count after applying active_filter (or all rows if unset)
inferred_sales_count_{n}D	Rows with sold_date_column in [partition_date − n, partition_date)
days_supply_{n}D	current_inventory_count / (inferred_sales_count_{n}D / n) — inf when no sales fall in the window
avg_days_on_lot_{n}D	Mean days_on_lot_column across the same sold-window (only when days_on_lot_column is set)

{n} substitutes each entry from windows.

Downstream consumers

The grouping assets feed two parts of the derived graph:

• Days on Lot — Joined onto each vehicle as features for both training and inference. Configurable per stats input via stats_inputs.
• Take Rates — inventory_stats/country is implicitly joined onto every featurized vehicle so attribute aggregations have country-scope context.