Draft scaffold outlining how AI augments logistics—from demand forecasting to real-time exception handling. Content will expand with benchmarked case patterns.
1. Core Leverage Domains
- Demand forecasting (multi-SKU, seasonal, promotion-aware).
- Dynamic routing & dispatch optimization.
- Warehouse picking path optimization & slotting.
- Predictive maintenance (vehicle / equipment telemetry modeling).
- Anomaly detection (late shipment, temperature excursion, fraud).
2. Data Foundations
- Event streams: orders, scans, GPS, ELD logs.
- Static reference: lane definitions, hub capacities, vehicle specs.
- External: weather, traffic, macro signals.
- Quality: late/missing scans, clock drift, inconsistent SKU naming.
3. Model & Tool Patterns
- Time-series hybrid: classical + transformer for robust seasonality.
- Graph optimization + heuristic LLM reasoning for constraint explanation.
- Embedding-based route similarity for anomaly triage.
- LLM summarizers for daily ops briefs (exceptions first).
4. KPI Impact Frames (Planned)
- OTIF improvement % vs baseline.
- Empty miles reduction.
- Dock dwell time variance shrinkage.
- Inventory turns acceleration.
5. Governance & Risk
- Data drift monitoring (seasonal shift, promotion spikes).
- Human override protocols for routing anomalies.
- Regulatory constraints (HOS compliance awareness).
6. Roadmap (Planned Sections)
- Case Study: Regional carrier dispatch optimization.
- Cold Chain Monitoring AI pattern.
- Sustainability metrics integration.