📈 Live history is activating for this object — the automated backfill is in progress.
⌁ mcp.call("adw-201") v1.0 Enable shippers to determine if current inventory buffers are sufficient to absorb port delays, preventing stockouts or excess holding costs.
📈 Live history is activating for this object — the automated backfill is in progress.
⌁ mcp.call("adw-201") v1.0 Ingest real-time vessel arrival data, dwell times, and crane productivity metrics to calculate a probabilistic delay distribution, outputting a lead-time variance score that indicates buffer adequacy.
Version 1.0 · validated to beat a naive baseline · benchmark: Portcast/GoComet
One call returns the answer with its reasoning attached — the live Intelligence Object for ADW-201.
{} Every product conforms to the Intelligence Object Model — typed, versioned, and discoverable.
Dashboard
Read the score + drivers in the console.
REST API
/v1/intelligence/adw-201
MCP tool
adw.adw_201
Marketplace
Discoverable by any MCP agent via the MCP registry.
White-label
Embed under your own brand (Platinum).
How stressed are global supply chains?
Method: Normalize freight indices + GSCPI z-score → 0-100
Quantify the financial impact of supply-chain disruptions to determine whether immediate inventory hedging or strategic waiting yields the lower total cost.
Method: Input lead-time volatility, demand variance, and holding costs into a stochastic optimization model to calculate the expected cost of stockouts versus excess inventory, outputting a break-even disrupt
Enable logistics managers to proactively diversify suppliers by identifying geopolitical or operational disruptions before they impact inventory levels.
Method: Aggregate real-time news sentiment and official trade alerts from GDELT-derived feeds, apply natural language processing to extract entity-specific risk events, and output a weighted disruption probab