Recipe Platform Future-State Analysis

Future-State Analysis

Basis and Desired Outcome

The prototype models a future commercial-kitchen operating approach that addresses gaps in the current recipe collection and menu-planning environment.

User-provided future-state examples:

• Proper mass-to-volume conversion.
• Unconventional commercial-kitchen volume units and conversions, including hotel pans.
• Scaling from sub-recipe ingredients through recipe rollups.
• Forecasting tied to production records.
• Automated ordering forecasts.
• Analysis of forecast-to-production accuracy.

Target Operating Model

Future-State Area	Target Capability	Prototype Evidence	Evidence Quality
Recipe measurement authority	Recipes and base foods carry enough mass/volume authority data to support cross-family scaling.	src/services/unit_conversion_service.py; src/services/recipe_service.py; recipe scaling tests	Observed/User-provided
Hotel-pan production units	Commercial-kitchen units such as hotel pans are supported in scaling and Forecasting where operators need them.	src/config/hotel_pan_units.py; src/config/units.py; forecast/advanced scaling tests	Observed/User-provided
Sub-recipe rollups	Nested recipes can be viewed hierarchically or flattened so ingredient demand can roll up accurately.	src/services/recipe_flattening_service.py; src/services/recipe_scaling_service.py	Observed/User-provided
Menu-to-forecast workflow	Menu assignments become forecastable production plans by service date, meal period, and concept.	src/services/menu_service.py; src/services/menu_forecast_service.py; menu docs/tests	Observed
Forecast-to-production tie-in	Forecast summaries become production record snapshots with actuals, variance, implied demand, and posted facts.	src/services/production_record_service.py; docs/agent_context/menu_builder.md	Observed/User-provided
Ordering forecast	Inventory planning converts menu/recipe demand and current on-hand data into shortage/reorder signals.	src/services/inventory_usage_service.py; src/services/inventory_ordering_service.py; inventory roadmap	Observed/User-provided
Forecast accuracy analysis	Production history and item trends analyze actual production outcomes against forecast expectations.	src/services/production_record_service.py; recent Git history; README roadmap	Observed/User-provided
Vendor and invoice bridge	Vendor catalog/invoice data supports future ordering, costing, substitutions, and item-specific sourcing.	database/schema.sql; src/services/inventory_bridge_service.py; src/services/inventory_invoice_service.py	Observed
Analytics readiness	Analytics consumes stable menu, usage, inventory, purchasing, cost, and production-variance contracts after operational workflows settle.	README.md; AGENT.md	Observed

Future-State Process

flowchart LR
    A[Recipe Truth with Measurement Authority] --> B[Sub-Recipe Rollups]
    B --> C[Menu Builder]
    C --> D[Forecasting with Kitchen Units]
    D --> E[Production Record Actuals]
    E --> F[Forecast Accuracy Analysis]
    D --> G[Ingredient Demand]
    G --> H[Inventory On Hand]
    H --> I[Ordering Forecast]
    I --> J[Vendor / Invoice / Catalog Bridge]
    F --> K[Future Analytics]
    J --> K

Success Measures

• User-provided/Inferred: Operators can scale recipes across mass, volume, each, and hotel-pan contexts without manual spreadsheet work.
• User-provided/Inferred: Nested recipes roll up to ingredient needs so production and inventory demand are not understated.
• User-provided/Inferred: Forecasts become production records without duplicate entry, and production records produce actual-vs-forecast learning.
• User-provided/Inferred: Ordering forecasts are generated from menu/recipe demand, current on hand, vendor preferences, and catalog/invoice data.
• Inferred: Analytics begins only after the operational facts are stable enough to trust.

Future-State Guardrails

• Observed: Hotel-pan and other advanced units should be used in scaling/forecasting contexts, not blindly accepted in base recipe authoring fields.
• Observed: Temporary each conversion assumptions should not overwrite Recipe Collection item truth.
• Observed: Inventory planning should keep calculated need separate from rounded purchase suggestions until purchase rules are validated.
• Inferred: Automated ordering should remain explainable, with visible demand source, on-hand basis, vendor source, delivery timing, and conversion assumptions.
• Inferred: Forecast accuracy should distinguish draft operational memory from posted production facts.

BA Alignment

• Activity areas: Analyze strategy; evaluate solution; analyze requirements and define design options.
• Techniques used: Future-state modeling, process modeling, document analysis, data modeling, acceptance criteria.