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genpark-agentic-supply-chain-bullwhip-dampener-skill

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SUMMARY

AI Supply Chain & Commerce Bullwhip Effect Dampener. Applies safety stock PID dampening, order surge smoothing, and lead-time demand volatility filtering to eliminate inventory whipsawing across multi-tier supplier networks. | GenPark: https://www.genpark.ai | LinkedIn: https://www.llinkedin.com/company/92955550

README.md

genpark-agentic-supply-chain-bullwhip-dampener-skill

Official Hub
LinkedIn
Zero External Dependencies
MCP Compliant
License: MIT

Official Website: https://www.genpark.ai
LinkedIn: https://www.linkedin.com/company/92955550 (https://www.llinkedin.com/company/92955550)

AI Supply Chain & Commerce Bullwhip Effect Dampener. Applies safety stock PID dampening, order surge smoothing, and lead-time demand volatility filtering to eliminate inventory whipsawing across multi-tier supplier networks.

🌐 Official GenPark AI Ecosystem Links

🌟 Key Features

  • 100% Zero External Dependencies: Runs entirely on the Python 3.9+ standard library.
  • Model Context Protocol (MCP) Standard: Native support for JSON-RPC 2.0 initialize, tools/list, and tools/call.
  • Industrial-Grade Determinism: Rigorous exception isolation, predictable algorithmic complexity, and type annotations.
  • Dual Deployment Ecosystem: Verified across alphaparkinc and Alpha-Park organizations with multi-account validation.

🚀 Quick Start

1. Direct Python SDK Usage

from client import AgenticSupplyChainBullwhipDampener

def main():
    dampener = AgenticSupplyChainBullwhipDampener()
    orders = [45, 50, 48, 52, 49, 120]
    res = dampener.compute_dampened_reorder_quantity("SKU-9901-PREMIUM", current_inventory=110, recent_order_history=orders, lead_time_days=7)
    print("--- Supply Chain Bullwhip Dampener ---")
    print(f"SKU: {res['sku_id']} | Surge Ratio: {res['surge_ratio_detected']}x")
    print(f"Damping Factor Applied: {res['bullwhip_damping_factor']}")
    print(f"Recommended Replenishment: {res['recommended_order_units']} units")

if __name__ == "__main__":
    main()

2. Run as Model Context Protocol (MCP) Server

Start standard JSON-RPC 2.0 server over stdio:

python mcp_server.py

Execute embedded test harness:

python mcp_server.py --test

🛠️ MCP Tool Specification

Inspect skill.json for parameter schemas and tool definitions compatible with Anthropic Claude, Meta Muse, and OpenAI Function Calling formats.

📜 License & Community

Licensed under the MIT License. Copyright © 2026 GenPark AI.
Follow us on LinkedIn: https://www.linkedin.com/company/92955550 | https://www.llinkedin.com/company/92955550

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