Supply Chain Digital Twin

Name: Supply Chain Digital Twin
Author: apifyforge

@apifyforge

Supply Chain Digital Twin について

Supply chain digital twin simulation via 8 quantitative algorithms — connect any AI agent to live corporate, trade, sanctions, hazard, and financial data across 17 public data sources.

基本情報

カテゴリ

その他

ライセンス

MIT

公開者

apifyforge

設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "supply-chain-digital-twin-mcp": {
      "url": "https://ryanclinton--supply-chain-digital-twin-mcp.apify.actor/mcp"
    }
  }
}

ツール

ツールは検出されませんでした

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is Supply Chain Digital Twin?

A supply chain digital twin simulation server that connects any AI agent to live corporate, trade, sanctions, hazard, and financial data across 17 public data sources. It turns natural-language supply chain questions into structured risk intelligence using 8 quantitative algorithms from network science, operations research, and econometrics. Built for supply chain risk analysts, operations researchers, and AI agents.

How to use Supply Chain Digital Twin?

Add the server URL to an MCP client configuration (Claude Desktop, Cursor, Windsurf, LangChain, or any MCP-compatible client). The endpoint is https://supply-chain-digital-twin-mcp.apify.actor/mcp with no authentication headers required. Tools such as simulate_disruption_cascade, optimize_logistics_transport, and estimate_supplier_survival are invoked via natural-language queries.

Key features of Supply Chain Digital Twin

Buldyrev-Parshani-Stanley mutual percolation for cascade failure simulation
Cross-entropy importance sampling for rare-event risk estimation
Sinkhorn-Knopp optimal transport for logistics cost minimization
Tri-level Stackelberg attacker-defender game for adversarial interdiction
Competing risks Cox proportional hazard model for supplier survival
Leontief input-output analysis via Neumann series multipliers
17 parallel data sources fetched within a single tool call

Use cases of Supply Chain Digital Twin

Stress-test supplier networks and estimate cascade failure probabilities before board reviews
Optimize logistics routing and sourcing using entropy-regularized optimal transport
Identify critical defense supply chain links vulnerable to adversarial attack
Assess 12-month failure probability of every supplier decomposed by failure cause
Estimate how a production shock propagates through sectoral input-output networks
Generate uncertainty-quantified demand forecasts and optimal base-stock inventory policies

FAQ from Supply Chain Digital Twin

What data sources does the server access?

17 public data sources including OpenCorporates, UN COMTRADE, OFAC sanctions, USGS earthquakes, NOAA weather, GDACS disaster alerts, FEMA emergencies, Censys cyber data, OpenAQ air quality, and more — all called in parallel within a single tool.

How are missing geographic coordinates handled?

When supplier coordinates are unavailable, the server falls back to seeded probabilistic assignment: a 15% chance of exposure with a 0.3 weight, using a deterministic pseudo-random number generator for reproducibility.

Is authentication required to connect?

No. The server accepts MCP connections over HTTP without any authentication headers. The Apify token is managed server-side.

Are simulation results reproducible?

Yes. All stochastic components use a string-hash-seeded linear congruential generator, producing identical results across identical inputs.

How does the server handle tool timeouts?

Each actor call within a tool has a 180-second timeout and gracefully returns an empty array on failure, ensuring robustness when a data source is unavailable.

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