BMS_Presentation_Slides.md

Slide 1: Title Slide

Title: BMS AI Assistant Subtitle: Enterprise Supply Chain Optimization System Footer: Technical Overview | Version 1.0

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Slide 2: Executive Summary

Key Points:

• Objective: Streamline access to supply chain data via natural language.
• Solution: Local AI Chatbot with integrated forecasting capabilities.
• Value Proposition: Zero external API costs, 100% data privacy, instant response time.

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Slide 3: System Architecture

Layers:

1. Presentation: Web-based UI (HTML5/JS).
2. API: FastAPI Backend (Python).
3. Logic: Intent Parser & Business Rules.
4. Data: In-memory high-speed data store.

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Slide 4: Core Capabilities

Features:

• Demand Forecasting: 30-day predictive analytics using ARIMA.
• Inventory Tracking: Real-time multi-warehouse stock visibility.
• Supplier Intelligence: Vendor lead times and contact details.
• Automated Reporting: On-demand PDF generation.

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Slide 5: Technical Stack

Technologies:

• Language: Python 3.10
• Framework: FastAPI + Uvicorn
• AI Model: TinyLlama 1.1B (Local Inference)
• Deployment: Docker Container

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Slide 6: Process Flow

Steps:

1. User Input: Natural language query.
2. Intent Recognition: Pattern matching algorithm.
3. Data Retrieval: SQL/CSV lookup or Model Inference.
4. Response Generation: JSON formatting and UI rendering.

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Slide 7: Future Roadmap

Next Steps:

• Integration with live ERP database (SQL).
• Advanced multi-variate forecasting models.
• User authentication and role-based access control.