AI-Powered Supply Chain Intelligence System
A multi-agent AI orchestration system continuously monitors inventory, consumption patterns, supplier performance, and market conditions to generate predictive insights and actionable recommendations. Autonomous actions can be triggered for routine decisions while complex situations are escalated with full evidence for human approval.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Supply Chain Dashboard - Real-time inventory monitoring and risk indicators
Predictive Analytics - Stockout prediction and demand forecasting with confidence intervals
Agent Orchestration - Multi-agent workflow with reasoning traces and observability
Recommendations Panel - AI-generated actions with confidence scores and cost impact
AI Agents
Specialized autonomous agents working in coordination
Mission Orchestrator
Complex supply chain analysis requires coordination of multiple specialized agents with proper dependency management, resource allocation, and error recovery.
Core Logic
Plans execution workflows, coordinates inter-agent communication via message passing, manages agent states and dependencies, allocates compute resources (CPU/GPU) dynamically, handles failures with retry policies, and generates comprehensive audit trails for compliance.
Data Retrieval Agent
Supply chain analysis requires data from disparate sources including IoT sensors, ERP systems, and external APIs, each with different formats, latencies, and access patterns.
Core Logic
Connects to IoT sensor networks for real-time inventory counts, queries ERP systems for production schedules and historical data, fetches supplier information via APIs, aggregates and normalizes data for downstream analysis agents.
Analysis Agent
Raw data requires statistical analysis to identify meaningful patterns, detect anomalies, and distinguish between normal fluctuations and structural changes requiring action.
Core Logic
Performs statistical analysis including trend detection, changepoint analysis, and hypothesis testing. Identifies consumption patterns, detects anomalies using configurable thresholds, and provides explanatory context for observed changes.
Prediction Agent
Proactive supply chain management requires accurate forecasts of future states including stockout timing, demand levels, and risk probabilities with quantified uncertainty.
Core Logic
Generates time-series forecasts with confidence intervals, runs Monte Carlo simulations for probability distributions, predicts stockout timelines with multiple confidence levels (P50, P95), and identifies key factors driving predictions.
Recommendation Agent
Analysis and predictions must be translated into concrete, prioritized actions with cost-benefit analysis, risk assessment, and implementation guidance.
Core Logic
Synthesizes insights from all agents to generate ranked recommendations with confidence scores, estimated financial impact, risk levels, and implementation timelines. Evaluates multiple options including standard orders, expedited shipping, production adjustments, and alternative suppliers.
Validation Agent
AI-generated recommendations must be validated for factual accuracy, checked for hallucinations, and verified against business rules before being presented for decisions.
Core Logic
Validates all claims against source data with grounding scores, runs hallucination detection, checks outputs against business policy guardrails, detects potential bias, verifies cost reasonableness, and ensures recommended actions are safe and reversible.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
8 technologies
Architecture Diagram
System flow visualization