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By leveraging collaborative AI agents powered by **Google Gemini 2.5 Flash**, the system identifies supply chain bottlenecks and generates actionable, data-driven optimization strategies.\n\n---\n\n## 📖 Table of Contents\n\n- [Overview](#-overview)\n- [Agentic Workflow](#-agentic-workflow)\n- [Key Features](#-key-features)\n- [Tech Stack](#-tech-stack)\n- [Project Structure](#-project-structure)\n- [Installation & Setup](#-installation--setup)\n- [Usage Guide](#-usage-guide)\n- [License](#-license)\n\n---\n\n## 🌟 Overview\n\nThe **Logistics Optimization Analysis** system move beyond simple data processing. It simulates a professional supply chain team where specialized agents collaborate to solve complex logistics problems. \n\nThe system takes a list of products as input and processes them through a multi-stage pipeline to produce a comprehensive optimization strategy that covers route efficiency, inventory turnover, and KPI improvements.\n\n---\n\n## 🏗️ Agentic Workflow\n\nThe system employs a sequential process where output from the analytical phase directly informs the strategic phase.\n\n```mermaid\ngraph TD\n    User([User Input: Products]) --> Crew[CrewAI Orchestrator]\n    Crew --> Task1[Logistics Analysis Task]\n    Task1 --> Agent1[Logistics Analyst Agent]\n    Agent1 --> Gemini[Gemini 2.5 Flash]\n    Gemini --> Report[Logistics Analysis Report]\n    Report --> Task2[Optimization Strategy Task]\n    Task2 --> Agent2[Optimization Strategist Agent]\n    Agent2 --> Gemini\n    Gemini --> FinalStrategy[Final Optimization Strategy Document]\n    FinalStrategy --> User\n```\n\n---\n\n## 🛠️ Key Features\n\n- **🤖 Multi-Agent Collaboration:** Sequential delegation between a Logistics Analyst and an Optimization Strategist.\n- **⚡ Flash-Speed Reasoning:** Powered by Gemini 2.5 Flash for rapid analysis and strategy generation.\n- **📈 Comprehensive Analysis:** Covers route efficiency, last-mile delivery, and inventory turnover trends.\n- **📋 Actionable Strategies:** Delivers prioritized plans with effort/impact ratings and estimated KPI gains.\n- **🔄 Parametric Execution:** Tailor analysis to any product mix (e.g., electronics, perishables, automotive).\n\n---\n\n## 💻 Tech Stack\n\n- **Orchestration:** [CrewAI](https://www.crewai.com/)\n- **LLM:** [Google Gemini 2.5 Flash](https://aistudio.google.com/)\n- **Language:** Python 3.10+\n- **Environment:** `python-dotenv` for secret management\n\n---\n\n## 📂 Project Structure\n\n```bash\nLogistics_Optimization_Analysis-Crew_AI/\n├── Flow/                  # Workflow diagrams (.mmd)\n│   └── workflow.mmd\n├── .env                   # Private API keys\n├── .env.example           # Environment template\n├── .gitignore             # Git exclusions\n├── LICENSE                # MIT License\n├── logistics_crew.py      # Main CrewAI implementation\n├── README.md              # Project documentation\n└── requirements.txt       # Dependencies\n```\n\n---\n\n## ⚙️ Installation & Setup\n\n### 1. Clone the Repository\n```bash\ngit clone https://github.com/SANJAI-s0/logistics-optimization-crewai.git\ncd logistics-optimization-crewai\n```\n\n### 2. Prepare Environment\n```bash\n# Create virtual environment\npython -m venv venv\nsource venv/bin/activate  # Windows: venv\\Scripts\\activate\n\n# Install dependencies\npip install -r requirements.txt\n```\n\n### 3. Configure API Keys\n1. Get your Gemini API Key from [Google AI Studio](https://aistudio.google.com/).\n2. Setup environment:\n   ```bash\n   cp .env.example .env\n   ```\n3. 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