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id: c4f9b2e7-3d1a-4a8f-b6c2-9e5d7a1f4c83 name: Monitor inventory levels and low stock alerts kind: InventoryMonitoring cron: 0 /15 * * * analysis_window: 90d comparison_periods: [7d, 28d]

Monitor ingredient inventory levels and generate low stock alerts to prevent shortages and ensure continuous menu availability.

The process should analyze recent consumption patterns and compare forecasted ingredient usage with available stock to detect ingredients at risk of running out before the next replenishment cycle.

The output must contain low-stock alerts and reorder recommendations only, without explanations or descriptions of methodology.

Known Tables & Columns

GNDITEM

• item (integer) - menu item identifier — references itm.id
• bohname (varchar) - menu item name — join itm on gnditem.item = itm.id and use itm.longname
• price (decimal) - current selling price
• revid (integer) - revenue center identifier

ITM

• id (integer) - menu item identifier
• longname (varchar) - menu item name
• recipe relationships - mapping of menu items to ingredient usage quantities

QTYPRICE

• quantity tiers - thresholds for bulk purchasing
• supplier pricing - price per unit depending on ordered quantity

GNDSALE

• historical sales forecasts
• waste indicators
• shortage indicators

Analysis Approach

Stage 1 — Historical Consumption Analysis

Extract historical sales data from the last 90 days using GNDITEM.

Aggregate sales by:

• menu item
• day of week
• service period or time window if available.

Convert menu item demand into ingredient consumption using recipe relationships from ITM.

Identify high-usage ingredients and recurring demand patterns.

Stage 2 — Consumption Forecast

Estimate near-term ingredient consumption based on recent demand patterns.

Adjust forecasts using contextual signals such as:

• seasonal variations
• promotions
• scheduled events or traffic spikes.

Stage 3 — Inventory Level Evaluation

Compare forecasted ingredient consumption with current or expected stock levels.

Use signals from GNDSALE to identify:

• ingredients that frequently run out
• ingredients associated with shortage events.

Stage 4 — Low Stock Risk Detection

Determine whether ingredient inventory will fall below safe operating levels by evaluating:

• forecasted consumption
• remaining stock
• safety stock thresholds.

Identify ingredients at risk of running out before the next delivery cycle.

Stage 5 — Procurement Context Evaluation

Use QTYPRICE to determine available supplier options and quantity tiers.

Evaluate whether:

• restocking should occur immediately
• restocking can be combined with upcoming orders
• bulk tiers justify larger replenishment quantities.

Stage 6 — Alert Generation

Generate structured alerts for ingredients approaching low stock conditions.

Alerts should include:

• estimated time until stockout
• recommended reorder quantity
• urgency level.

Output Constraints

• Use historical data from the last 90 days only.
• Prefer aggregated summaries.
• Avoid requesting more than 3 database queries unless necessary.
• Return low-stock alerts and reorder recommendations only.
• Do not include explanations, reasoning, or methodology.

Output Format

Return structured inventory alerts.