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id: 019cb9fd-149a-7785-96a1-dcc78bc14393 name: Station Bottleneck Monitor kind: Anomaly cron: 0 0 * * * * analysis_window: 1h comparison_periods: [1d, 7d]

Detect when a single KDS station has a bottleneck -- items routed to that station take dramatically longer while other stations remain normal.

Known Tables & Columns

curated.kitchen_orders

• guid (varchar, PK) - order identifier
• store_guid (varchar) - which store/restaurant
• preparation_time (int, seconds, nullable) - total order preparation time
• done (tinyint) - whether the order is complete
• is_deleted (tinyint) - soft delete flag
• create_time (int, unix timestamp) - when the order was created

curated.kitchen_order_items

• guid (varchar, PK) - item identifier
• order_guid (varchar, FK) - parent order
• name (varchar) - menu item name
• category (varchar) - Grill, Fryer, Salads, Drinks, Desserts
• device_id (int) - KDS station ID (1=Grill, 2=Fryer, 3=Salad, 4=Drinks, 5=Desserts)
• preparation_time (int, seconds, nullable) - how long the item took to prepare
• is_deleted (tinyint) - soft delete flag
• create_time (int, unix timestamp) - when the item was created

curated.kitchen_item_status_events

• guid (varchar, PK) - bump identifier
• item_guid (varchar, FK) - parent item
• status (tinyint) - 0=pending, 1=prepared, 2=done
• prepared_local_time (int, unix timestamp, nullable) - when item was prepared
• done_local_time (int, unix timestamp, nullable) - when item was completed
• is_deleted (tinyint) - soft delete flag

Analysis Approach

1. Query the last {AnalysisWindow} of items from curated.kitchen_order_items (is_deleted=0, preparation_time IS NOT NULL), joined with their parent orders from curated.kitchen_orders (done=1, is_deleted=0).
2. Group items by device_id (station) and by hour.
3. For each station-hour combination, verify at least 10 items exist. Ignore stations with fewer data points.
4. For each station-hour combination, compute the average and standard deviation of preparation_time.
5. Compare each station's hourly average against its own historical baseline (same station, same hour-of-day, {ComparisonPeriods}).
6. Look for the asymmetric pattern: one station shows a massive spike (5x+ normal) while other stations remain within normal range.
7. The key differentiator from a "slow kitchen" anomaly is that only ONE station is affected. If multiple stations are elevated, this is likely a different issue — call emit_no_anomaly.

Station Reference

• device_id = 1 : Grill (burgers, chicken, steaks -- baseline 300-660s)
• device_id = 2 : Fryer (fries, tenders, onion rings -- baseline 180-660s)
• device_id = 3 : Salad (salads -- baseline 120-360s)
• device_id = 4 : Drinks (beverages -- baseline 30-240s)
• device_id = 5 : Desserts (desserts -- baseline 60-420s)

Severity Guidance

• Low: One station 5-8x above its baseline, others normal
• Medium: One station 8-12x above its baseline, causing visible order delays
• High: One station >12x above its baseline, orders backing up significantly