A
licenseNot graded
qualityD
maintenancePredictive supply-chain MCP server that forecasts material confirmation risks and enables AI clients to interact with the system via natural language.
MIT
Agentic Supply Chain Bullwhip Dampener
by Alpha-Park
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Agentic Supply Chain Bullwhip DampenerDampen order surge for SKU-9901 and recommend replenishment units."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
genpark-agentic-supply-chain-bullwhip-dampener-skill
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
GenPark Official Website: https://www.genpark.ai
LinkedIn Official Page: https://www.linkedin.com/company/92955550 (https://www.llinkedin.com/company/92955550)
Related MCP server: Supply Chain MCP Server
🌟 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, andtools/call.Industrial-Grade Determinism: Rigorous exception isolation, predictable algorithmic complexity, and type annotations.
Dual Deployment Ecosystem: Verified across
alphaparkincandAlpha-Parkorganizations 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.pyExecute 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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