AI Agentic Predictive Demand Planning
This digital worker orchestrates 11 specialized AI agents through a 6-phase mission framework with DAG dependencies. It analyzes 24 months of historical data, monitors real-time market conditions, generates ML-powered demand forecasts, optimizes inventory levels, engineers menu profitability, tracks customer behavior, scores sustainability, and produces autonomous action plans with configurable approval thresholds.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Mission Configuration Screen - Setup interface showing business context inputs, model configuration settings, optimization objectives selection (waste reduction, stockout prevention, cost optimization), and 6-phase agent architecture overview.
Live Mission Execution - Real-time orchestration at Phase 1 (8% complete) for Coastal Grill & Seafood with Mission Control agent coordinating workflow and reasoning chain showing 6 completed steps including mission analysis and agent configuration.
Mission Complete Results - Inventory optimization results showing 68% waste reduction, 100% stockout prevention, $178/wk projected savings, immediate actions with auto-executed recommendations, and risk assessment for critical items.
Autonomous Performance Tracking - Decision-making dashboard displaying 11 agents with 3 auto-executed actions, 1 pending approval, 92.4% average confidence, autonomous decisions with confidence scores, and real-time market alerts.
AI Agents
Specialized autonomous agents working in coordination
Mission Control - Orchestration & Strategic Planning
Coordinating complex multi-agent missions with dependencies, parallel execution, and phase sequencing.
Core Logic
Acts as mission control (model: procuresense-agent-v2.1, icon: hub, color: #667eea) to create intelligent execution plans, route tasks to specialized agents, manage the 6-phase pipeline, generate market alerts, coordinate autonomous decisions, and synthesize final mission results. Handles errors and maintains mission state. No dependencies (root agent). Utilizes tools: create_execution_plan, route_mission_tasks, generate_alerts, finalize_mission.
Data Analyst - Historical Pattern & Trend Analysis
Raw historical data needs transformation into actionable patterns, seasonality factors, and trend insights.
Core Logic
Analyzes 90 days of historical sales, inventory, and customer data (model: procuresense-agent-v2.1, icon: query_stats, color: #4CAF50). Identifies demand patterns, seasonality coefficients, anomaly events, and trend directions. Outputs structured datasets for downstream forecasting agents. Dependencies: orchestrator. Utilizes tools: analyze_historical_patterns, calculate_seasonality, detect_trend_anomalies.
Risk Assessor - Waste, Stockout & Compliance Risk
Undetected expiration risks, stockout probabilities, and compliance gaps lead to waste and operational issues.
Core Logic
Scans for expiration risks with time-to-expiry tracking (model: procuresense-agent-v2.1, icon: warning, color: #FF9800), calculates stockout probabilities based on demand forecasts and inventory levels, verifies food safety compliance (HACCP, temperature logs), and prioritizes risks by severity. Dependencies: orchestrator. Utilizes tools: assess_expiration_risk, calculate_stockout_probability, verify_compliance_status.
Supplier Intel - Supplier Performance & Market Analysis
Lack of visibility into supplier performance, pricing competitiveness, and alternative sourcing options.
Core Logic
Evaluates supplier performance (model: procuresense-agent-v2.1, icon: business, color: #E91E63) across quality, reliability, price competitiveness, and sustainability dimensions. Monitors market prices, identifies alternative suppliers, tracks pricing trends, and generates supplier scorecards with improvement/declining indicators. Dependencies: orchestrator. Utilizes tools: evaluate_supplier_performance, monitor_market_prices, identify_alternatives, generate_scorecards.
Supply Chain Monitor - Real-time Disruption Detection
Supply chain disruptions (weather, logistics, labor) detected too late to mitigate impact.
Core Logic
Real-time monitoring of supply chain status (model: procuresense-agent-v2.1, icon: local_shipping, color: #795548) including port delays, weather impacts, regional disruptions, and logistics status. Provides early warning alerts with estimated resolution times and affected supplier/item lists. Dependencies: orchestrator. Utilizes tools: scan_supply_chain_status, detect_disruptions, estimate_resolution_time.
Demand Forecaster - ML-Powered Demand Prediction
Inaccurate demand forecasts that don't account for external factors like weather, events, and supply constraints.
Core Logic
Generates ML-powered demand forecasts (model: procuresense-agent-v2.1, icon: trending_up, color: #2196F3) incorporating historical patterns, seasonality, weather data, local events, and supply chain constraints. Provides item-level predictions with confidence intervals and optimal order timing recommendations. Dependencies: data-analyst, supply-chain-monitor. Utilizes tools: generate_demand_forecast, integrate_external_factors, calculate_confidence_intervals.
Menu Engineer - Menu Profitability & Optimization
Menu items with poor profitability, suboptimal pricing, and missed promotional opportunities.
Core Logic
Analyzes menu item profitability (model: procuresense-agent-v2.1, icon: restaurant_menu, color: #FF5722), identifies top performers and underperformers, recommends pricing adjustments based on demand elasticity, suggests promotional strategies, and tracks seasonal menu trends. Dependencies: forecaster. Utilizes tools: analyze_menu_profitability, optimize_pricing, recommend_promotions, track_seasonal_trends.
Customer Analyst - Customer Behavior & Preference Analysis
Lack of insight into customer segments, ordering patterns, churn risk, and retention opportunities.
Core Logic
Segments customers by behavior (model: procuresense-agent-v2.1, icon: groups, color: #673AB7) including regular, occasional, and new categories. Analyzes ordering patterns and preferences, identifies at-risk customers with churn probability, and recommends targeted retention actions and loyalty incentives. Dependencies: data-analyst. Utilizes tools: segment_customers, analyze_ordering_patterns, predict_churn_risk, recommend_retention_actions.
Sustainability Agent - Carbon Footprint & Waste Analysis
Inability to measure, track, and improve environmental sustainability of operations and sourcing.
Core Logic
Calculates carbon footprint by category (model: procuresense-agent-v2.1, icon: eco, color: #4CAF50) covering protein, produce, and dairy. Tracks waste metrics and prevented waste, evaluates sustainable sourcing options, and generates sustainability reports with improvement recommendations and certification tracking. Dependencies: supplier-intelligence, risk-assessor. Utilizes tools: calculate_carbon_footprint, track_waste_metrics, evaluate_sustainable_sourcing, generate_sustainability_report.
Inventory Optimizer - Order & Stock Optimization
Suboptimal order quantities leading to excess inventory (waste) or insufficient stock (stockouts).
Core Logic
Applies multi-objective optimization (model: procuresense-agent-v2.1, icon: inventory_2, color: #9C27B0) to calculate optimal order quantities balancing cost, waste reduction, stockout prevention, and service level targets. Generates supplier-specific orders with confidence scores, delivery scheduling, and sustainability impact. Dependencies: forecaster, risk-assessor, supplier-intelligence. Utilizes tools: calculate_optimal_quantities, balance_objectives, generate_orders, schedule_deliveries.
Action Planner - Autonomous Recommendation Engine
Translating complex analysis into prioritized, actionable recommendations with appropriate human oversight.
Core Logic
Synthesizes outputs from all agents (model: procuresense-agent-v2.1, icon: checklist, color: #00BCD4) to generate prioritized action plans with type classification (waste reduction, inventory, supplier, menu, customer). Applies auto-approval logic based on confidence thresholds, tracks autonomous decision history, and provides full audit trail. Dependencies: inventory-optimizer, menu-engineer, customer-behavior, sustainability-analyst. Utilizes tools: generate_action_plan, apply_auto_approval, track_decisions, create_audit_trail.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
7 technologies
Architecture Diagram
System flow visualization