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id: 6e3f1c94-8a27-4b6d-9f52-1d8c7e3b5a41 name: Track waste and adjust forecasts kind: WasteOptimization cron: 0 55 6 * * * analysis_window: 90d comparison_periods: [7d, 28d]

Track ingredient and inventory waste and adjust demand forecasts to improve ordering accuracy, reduce overstock, and minimize future waste.

The process should analyze historical waste patterns and incorporate them into forecast and ordering adjustments so that inventory levels better reflect actual consumption.

The output must contain forecast adjustments, waste alerts, and ordering adjustments 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
• forecast deviation signals

Analysis Approach

Stage 1 — Historical Waste Extraction

Extract waste-related signals from the last 90 days using GNDSALE.

Identify ingredients or menu items frequently associated with:

• spoilage
• unused stock
• overproduction.

Stage 2 — Waste Pattern Analysis

Aggregate waste occurrences by:

• ingredient
• menu item
• day of week
• service period if available.

Identify recurring patterns such as:

• over-preparation during specific days
• ingredients consistently overstocked
• waste spikes after certain delivery cycles.

Stage 3 — Demand vs Waste Correlation

Compare:

• historical demand from GNDITEM
• waste indicators from GNDSALE
• ingredient usage derived from recipes in ITM.

Detect discrepancies where predicted consumption significantly exceeds actual usage.

Stage 4 — Forecast Adjustment

Adjust demand forecasts by incorporating detected waste patterns.

Reduce projected usage for ingredients or menu items that consistently generate excess inventory while preserving sufficient stock for high-demand periods.

Stage 5 — Ordering Strategy Adjustment

Modify purchasing recommendations based on waste insights by:

• reducing order quantities for ingredients with repeated waste
• adjusting safety stock thresholds
• modifying delivery schedules where waste occurs after deliveries.

Consider pricing tiers and supplier options using QTYPRICE.

Stage 6 — Continuous Waste Monitoring

Generate structured insights highlighting:

• ingredients with persistent waste patterns
• menu items contributing to overproduction
• opportunities to improve ordering or preparation practices.

Output Constraints

• Use historical data from the last 90 days only.
• Prefer aggregated summaries.
• Avoid requesting more than 3 database queries unless necessary.
• Return forecast adjustments, waste alerts, and ordering adjustments only.
• Do not include explanations, reasoning, or methodology.

Output Format

Return structured waste insights and forecast adjustments.