Menu engineering · food cost analytics
Deterministic pipelines for menu engineering & food cost analytics
Production-grade Python and pandas workflows that turn fragmented recipes, POS exports, and inventory snapshots into auditable margin intelligence — built for multi-unit operators, culinary managers, and food-tech engineers.
Every stage is explicit, every transformation is logged, and every variance traces back to a single ingredient SKU. No spreadsheet reconciliation. No silent drift.
What you'll find here
A focused reference library on the engineering of automated food-cost systems. Each section is a structured guide with reproducible Python implementations, pandas idioms for vectorized cost roll-up, and the operational guard-rails needed to deploy these pipelines across distributed restaurant networks.
Core Architecture & Cost Mapping Systems
DAG-based recipe BOMs, POS taxonomy mapping, yield factors, and the multi-location cost-centre architecture that holds it all together.
- Designing Recipe BOM Databases
- Mapping POS Taxonomies to Ingredients
- Multi-Location Cost Center Architecture
- Yield Factor Calculation Frameworks
Data Ingestion & Recipe Parsing Workflows
PDF extraction, CSV bulk imports, POS API polling, and async batch processing — the ingestion contracts that keep theoretical models honest.
- Async Batch Processing Workflows
- CSV Bulk Import Automation
- PDF Recipe Extraction Pipelines
- POS API Polling Strategies
Theoretical vs Actual Food Cost Calculation
Variance mapping, dynamic alert thresholds, portion standardization, and the waste-tracking systems that close the gap between recipe and reality.
- Portion Size Standardization
- Threshold Tuning for Alerts
- Variance Mapping Methodologies
- Waste Tracking & Routing Systems
Engineering principles
Deterministic by default
Pure functions on DataFrames. Identical inputs produce identical outputs, every run, every location.
Schema-first ingestion
Strict type contracts on every boundary. Malformed records are quarantined, never silently coerced.
Vectorized at scale
No row-by-row loops. Merge, group, and roll up with hierarchical indexing built for enterprise catalogs.
Decimal-grade precision
Financial calculations use Python's decimal module to eliminate floating-point drift across SKUs.