Repository Docs & Knowledge Base

Consolidated Spensara documentation repository (722 Markdown & architecture documents in ../repository)

Built Word Documents (.docx): Spensara Overview (.docx) Capability Assessment (.docx) Brink POS Ingestion Analysis (.docx) The Borg Ingestion Architecture (.docx)

Branches (328)

Branch-specific documentation across extract-raw-xml, feature-fleet-import, kds_cdc_improvments, life-cycle, and tenat-admin.

branches/kds_cdc_improvments/monitor-specs/backend/Kds.Ai.Data/MonitorSpecs/waste-reduction.md 4,301 bytes · Updated Oct 23, 2026 10:44 AM EDT

Raw File

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 waste-like demand and overproduction patterns using current curated schema, and adjust forecasts only when supporting stock data exists.

The process should analyze historical waste patterns and incorporate them into forecast and ordering adjustments only when visible curated tables support that conclusion.

The output must contain forecast adjustments, waste alerts, and ordering adjustments only, without explanations or descriptions of methodology.

Known Tables & Columns

`curated.pos_order_items`

order_id (varchar) - parent order identifier
item_id (int) - menu item identifier
item_name (varchar) - sold item name
quantity (int, nullable) - quantity sold
unit_price (decimal, nullable) - selling price
category_name (varchar, nullable) - category name when available
business_date (date, nullable) - business date
created_at_utc (timestamp, nullable) - sale timestamp when available

`curated.pos_orders`

order_id (varchar) - order identifier
created_at (timestamp, nullable) - order time
business_date (date, nullable) - business date
order_type (varchar, nullable) - sales channel/type
destination (varchar, nullable) - service destination

`curated.pos_menu_items`

item_id (int) - menu item identifier
item_name (varchar) - canonical item name
cost (decimal, nullable) - configured item cost when available
category_name (varchar, nullable) - menu category
availability_status (varchar, nullable) - active/inactive style flag

Analysis Approach

Stage 1 — Historical Waste Extraction

Extract waste-related signals from the last 90 days using demand and sell-through patterns from curated.pos_order_items, curated.pos_orders, and curated.pos_menu_items.

If current curated schema also contains stock tables, you may include overstock signals from those tables. Otherwise keep analysis at menu-item demand level.

Identify menu items frequently associated with:

spoilage
unused stock
overproduction.

Stage 2 — Waste Pattern Analysis

Aggregate waste occurrences by:

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

Identify recurring patterns such as:

over-preparation during specific days
menu items with recurring low sell-through after forecasted demand
waste spikes after certain delivery cycles.

Stage 3 — Demand vs Waste Correlation

Compare:

historical demand from curated.pos_order_items
menu metadata from curated.pos_menu_items.

If stock or recipe tables are not available in visible curated Redshift tables, say so clearly and keep analysis at menu-item level.

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 focus on menu items with repeated low sell-through
adjusting safety stock thresholds
modifying delivery schedules where waste occurs after deliveries.

Consider only fields actually visible in current curated schema.

Do not assume quantity-tier pricing tables, stock tables, or supplier tables exist unless they are present in the Redshift schema.

Stage 6 — Continuous Waste Monitoring

Generate structured insights highlighting:

menu items with persistent waste-like 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.