Supply Chain Reasoning Engine

Tip:
This project is a work in progress and serves as a Proof-of-Concept for AI-powered supply chain reasoning and control. It is not intended for production use, but as a foundation for experimentation and further development.

Overview

The Supply Chain Reasoning Engine is an AI-powered Supply Chain Digital Twin & Control Tower focused on resilience and real-time adaptation. This Proof-of-Concept leverages advanced reasoning models, simulation, and optimization tools to proactively manage and respond to disruptions in supply chain operations.

Key Features:

• Real-time disruption detection and response
• Simulation of physical goods flow and "what-if" scenarios
• AI-driven reasoning for creative, context-aware solutions
• Optimization module for actionable, cost-effective plans
• Interactive UI for visualization, alerts, and human-in-the-loop decisions

Architecture

• Data Layer: Defines the system state (nodes, network, assets, inventory, orders, constraints, real-time events).
• Simulation Engine: Projects future states and evaluates the impact of planned actions.
• Disruption Detection: Monitors internal and external data feeds for impactful events.
• Reasoning Engine: Assesses disruptions, generates strategic responses, and sets constraints for optimization.
• Optimization Module: Calculates detailed, actionable plans using mathematical techniques.
• User Interface: Visualizes the system state, solutions, and allows user interaction.

For a detailed breakdown, see docs/supplychain.md.

Example Workflow

1. Detect: System receives an alert (e.g., stockout at a retail outlet).
2. Reason: AI proposes strategies (reroute, expedite, source from alternate locations).
3. Optimize: Module calculates costs, ETAs, and impacts for each strategy.
4. Visualize: UI displays options and highlights disruptions.
5. Act: User approves a solution; system updates plans and monitors execution.

Getting Started

Quickest Start:

• Use the VS Code Launch App compound runner (from .vscode/launch.json) after running npm install in both the frontend and backend directories.

Manual Start:

1. In both frontend and backend directories, run:

   • npm install
   • npm run dev

2. Backend: See backend/README.md for API details.

3. Frontend: See frontend/README.md for UI usage.

4. Docs: Explore the docs/ directory for architecture, design notes, and future plans.

Azure Services Used

The application leverages the following Azure services:

1. Azure OpenAI Service – For LLM-powered responses and reasoning
2. Azure Maps – For geospatial visualization and mapping

License

MIT License

Next Steps

• Define MVP scope and data sources
• Select technology stack for simulation, optimization, and UI
• Build and iterate on the core detection-reasoning-optimization loop

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This project combines the creative power of reasoning models with the precision of simulation and optimization to deliver a truly adaptive supply chain management system.