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Case Study · AI Agents
Developing a Planner Agent
for Supply Chain
An intelligent autonomous agent that orchestrates demand forecasting, inventory optimization, and logistics coordination across a modern supply network.
12
Distribution Centers
8K+
SKUs Managed
3
Specialized Sub-Agents
~90ms
Decision Latency
Agent Decision Pipeline
📡
Data Ingestion
→
🧠
Demand Forecast
→
🔍
Risk Analysis
→
⚙️
Plan Generation
→
✅
Constraint Check
→
🚀
Execution
Core Capabilities
- Demand Forecasting — Multi-horizon prediction using time-series models blended with LLM reasoning on market signals.
- Replenishment Planning — Autonomous reorder trigger with dynamic safety-stock recalculation.
- Disruption Detection — Real-time monitoring of supplier lead times, weather, and geopolitical feeds.
- Route Optimization — Last-mile and cross-dock route planning across carrier networks.
- Natural Language Interface — Planners interact via chat; agent surfaces rationale & confidence scores.
Technology Stack
| Layer | Component | Role |
|---|---|---|
| LLM | Claude 3.5 | Reasoning core |
| ML | Prophet + XGBoost | Forecasting |
| Orch | LangGraph | Agent flow |
| DB | PostgreSQL + Pinecone | State & memory |
| API | SAP S/4HANA | ERP connector |
Implementation Roadmap
🗺️
Phase 1 — Discovery & Data Audit Weeks 1–3
Inventory data quality assessment, ERP integration mapping, stakeholder interviews, and KPI baseline establishment.
🔧
Phase 2 — Agent Architecture Weeks 4–8
Build forecasting sub-agent, replenishment planner, and risk monitor. Define tool schemas and memory architecture.
🧪
Phase 3 — Pilot & Validation Weeks 9–13
Shadow mode deployment on 2 distribution centers. Human-in-the-loop feedback loop for edge-case correction.
🚀
Phase 4 — Full Rollout Weeks 14–18
Enterprise-wide deployment, planner training, dashboard go-live, and continuous improvement pipeline activation.
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