Supply Chain & Logistics Intelligence

Capabilities

| # | Capability | Input | Output | |---|-----------|-------|--------| | 1 | Freight Rate Dashboard | Route (e.g., Shanghai-LA) / mode (ocean/air) | Spot rate, 1Y range, trend, capacity outlook, booking lead time | | 2 | Port Congestion Monitor | Port(s) / region | Vessel queue length, dwell time, gate hours, labor status, weather impact | | 3 | Trade Flow Analyzer | Country pair / commodity (HS code) | Volume, value, growth rate, seasonality, tariff impact, alternative routes | | 4 | Commodity Bottleneck Scanner | Commodity (semiconductors, batteries, etc) | Key suppliers, geographic concentration, lead time, price volatility, substitution options | | 5 | Supply Chain Risk Heatmap | Company / product / region | Geopolitical risk, climate exposure, labor disruption probability, regulatory compliance burden | | 6 | Transit Time Estimator | Origin-destination + mode | Current transit days, historical variability, delay probability, expedited options cost | | 7 | Inventory Optimization Model | Demand forecast + lead time variability | Safety stock level, reorder point, EOQ, service level vs. carrying cost trade-off | | 8 | Sourcing Intelligence | Component / raw material | Supplier landscape, pricing benchmarks, quality ratings, ESG compliance, dual-sourcing feasibility | | 9 | Logistics Cost Benchmark | Shipment profile (weight, volume, value) | Cost breakdown (freight, fuel surcharge, customs, insurance), vs. industry average | | 10 | Disruption Alert System | Watchlist (ports, suppliers, routes) | Real-time alerts (strikes, weather, sanctions), impact assessment, contingency plan suggestions |

Workflow

User Query
  │
  ├─ [Step 1] Classify → logistics mode + commodity + geography + time horizon
  │
  ├─ [Step 2] Multi-source data retrieval:
  │   └─ Freight rates: Drewry, Freightos
  │   └─ Port data: Port of LA/LB, China Ports Association
  │   └─ Trade: UN Comtrade, US Census
  │   └─ Risk: Resilinc, Bloomberg SCM
  │   └─ Equipment: Container xChange
  │
  ├─ [Step 3] Cross-validate & flag discrepancies
  │
  ├─ [Step 4] Apply supply chain models:
  │   └─ Inventory optimization (EOQ, safety stock)
  │   └─ Network design (facility location, routing)
  │   └─ Risk quantification (VaR for lead time)
  │
  ├─ [Step 5] Generate structured output with actionable insights
  │
  └─ [Step 6] Cite data vintage, source URLs, confidence intervals

Output Formats

Freight Rate Snapshot

| Route | Mode | Spot Rate | 1W Change | 1Y Range | Capacity | Booking Lead Time | |-------|------|-----------|-----------|----------|----------|-------------------| | Shanghai-LA | Ocean | $X,XXX/TEU | +X% | $X,XXX-$X,XXX | Tight | 3-4 weeks | | Frankfurt-ORD | Air | $X.XX/kg | -X% | $X.XX-$X.XX | Available | 1-2 days |

Port Congestion Dashboard

| Port | Vessels Waiting | Avg Dwell Time (days) | Gate Hours | Labor Status | Weather Alert | |------|----------------|----------------------|------------|--------------|---------------| | Los Angeles | 12 | 4.2 | 24/7 | Normal | None | | Rotterdam | 8 | 3.8 | 6am-10pm | Strike warning | High winds |

Commodity Bottleneck Matrix

| Commodity | Key Suppliers | Geographic Risk | Lead Time (weeks) | Price Volatility | Substitution Options | |-----------|--------------|-----------------|-------------------|------------------|---------------------| | Advanced Semiconductors | TSMC, Samsung, Intel | Taiwan Strait, US-China | 26-52 | High | None (critical) | | Lithium-ion Batteries | CATL, LG, Panasonic | China, DRC, Chile | 12-24 | Medium | Sodium-ion (emerging) |

Usage Guidelines

Real-time data priority — supply chain data decays rapidly; flag any data >7 days old
Multi-modal comparison — always present air vs. ocean vs. rail trade-offs (cost vs. speed vs. reliability)
Risk quantification — express disruptions in $ impact and lead time extension, not just qualitative
Actionable recommendations — each insight should link to a decision (reroute, expedite, buffer stock, dual-source)
Regulatory compliance — include customs, sanctions (OFAC), forced labor (UFLPA), carbon border (CBAM) considerations
Scenario planning — provide best-case/worst-case/base-case for critical decisions

Examples

Example 1: Freight Cost Optimization

User: "Best way to ship 100 TEU from Shenzhen to Chicago in Q3 2026?" Output: Ocean vs. rail vs. air cost/speed comparison; port pair recommendations (Shenzhen→LA vs. Shenzhen→Vancouver); transit time variability; fuel surcharge forecast; contingency for Panama Canal drought.

Example 2: Disruption Impact Assessment

User: "What's the impact of a potential ILWU strike at LA/LB ports?" Output: Historical strike duration (days), backlog buildup rate (TEU/day), alternative ports (Oakland, Tacoma, Mexico), cost premium for air freight, inventory burn-down timeline for key industries.

Example 3: Sourcing Strategy

User: "Should we dual-source rare earth magnets from China and Vietnam?" Output: Supplier capability comparison, quality variance, lead time differential, tariff implications, ESG risk (China Xinjiang concerns), total landed cost model.

Data Base: references/supply_chain_sources.json — 14 authoritative data sources, 5 key commodities, 5 risk factors, 4 logistics modes. Last Updated: June 2026 Free Tier: Available. This skill aggregates public supply chain data; no proprietary carrier contracts accessed.