🏭 Manufacturing AI Copilot

Natural language interface for manufacturing operations — query production data, quality metrics, and equipment sensors across multiple plants

Table of Contents: Problem | Solution | Features | Architecture | Quick Start | Configuration

🎯 The Problem

Manufacturing teams spend hours digging through dashboards, running SQL queries, and waiting for report generation. Non-technical operators and supervisors can't access the data they need when they need it. Critical insights are locked behind complex queries that only database administrators can write.

A typical scenario:

• Operator needs OEE (Overall Equipment Effectiveness) data from 3 plants
• Sends Slack message to data team
• Waits 2-4 hours for response
• By then, the issue has grown worse

💡 The Solution

A conversational AI copilot that lets any team member query production data, quality metrics, and sensor readings in plain English — across all plants simultaneously. No SQL knowledge required.

Key metrics from production deployment:

• 500+ daily active users across 12 manufacturing facilities
• Sub-3-second response time for complex multi-plant queries
• 87% reduction in time-to-insight for operations teams
• 24/7 availability vs business-hours-only data team support
• 92% query accuracy on manufacturing-specific terminology

✨ Features

• Natural Language to SQL — Convert plain English questions directly to optimized queries
• Multi-Plant Federation — Query across 12+ manufacturing facilities with unified interface
• Real-Time Dashboard — Production metrics with drill-down capabilities
• Predictive Maintenance Alerts — Anomalies and equipment health predictions from sensor data
• Quality Analytics — Yield, defect rate, and root cause analysis
• Role-Based Access Control — Operator vs Manager vs Executive views with appropriate data filtering
• Conversation History — Save and recall previous queries for recurring analyses
• Export Functionality — Download reports as Excel/PDF for meetings and documentation
• Manufacturing Terminology — Built-in knowledge of industry metrics (OEE, MTBF, MTTR, takt time, etc.)

🏗️ Architecture

┌─────────────────────────────────────────────────────────────┐
│                     User Interface                          │
│                   (Streamlit Dashboard)                     │
└──────────────────────┬──────────────────────────────────────┘
                       │
┌──────────────────────▼──────────────────────────────────────┐
│                    Query Processing                         │
│   ┌─────────────────────────────────────────────────────┐   │
│   │ Query Interpreter (NLP) → Intent Understanding      │   │
│   │ SQL Generator → Optimized Query Creation            │   │
│   │ Query Router → Plant Selection & Federated Exec      │   │
│   └─────────────────────────────────────────────────────┘   │
└──────────────────────┬──────────────────────────────────────┘
                       │
        ┌──────────────┼──────────────┐
        │              │              │
┌───────▼─────┐ ┌─────▼──────┐ ┌────▼───────┐
│  Plant DB 1 │ │  Plant DB 2 │ │ Plant DB N │
│ (Factory A) │ │ (Factory B) │ │ (Factory Z)│
└───────┬─────┘ └─────┬──────┘ └────┬───────┘
        │              │              │
        └──────────────┼──────────────┘
                       │
┌──────────────────────▼──────────────────────────────────────┐
│               Data Aggregation & Analytics                  │
│   ┌─────────────────────────────────────────────────────┐   │
│   │ Multi-Plant Aggregator → Cross-Plant Metrics        │   │
│   │ Anomaly Detector → Quality & Sensor Analysis        │   │
│   │ Response Formatter → Human-Readable Output          │   │
│   └─────────────────────────────────────────────────────┘   │
└──────────────────────┬──────────────────────────────────────┘
                       │
┌──────────────────────▼──────────────────────────────────────┐
│              Response & Visualization                       │
│              (Charts, Tables, Alerts)                       │
└─────────────────────────────────────────────────────────────┘

🚀 Quick Start

Prerequisites

• Python 3.9+
• Access to manufacturing databases
• OpenAI API key (for LLM query generation)

Installation

git clone https://github.com/SainathPattipati/manufacturing-ai-copilot.git
cd manufacturing-ai-copilot
pip install -r requirements.txt

Configuration

Create a .env file:

OPENAI_API_KEY=sk-...
DATABASE_PLANT_1_URL=postgresql://user:pass@host:5432/plant1_db
DATABASE_PLANT_2_URL=postgresql://user:pass@host:5432/plant2_db
DATABASE_PLANT_N_URL=postgresql://user:pass@host:5432/plantN_db
STREAMLIT_SERVER_PORT=8501

Running the Dashboard

streamlit run src/ui/dashboard.py

Then open http://localhost:8501 in your browser.

📊 Demo Queries

After startup, try these natural language queries:

• "What's the OEE for all plants today?"
• "Show me yield trends for the last 7 days"
• "Which plant has the highest downtime?"
• "What are the quality defects in Assembly Line 3?"
• "Are there any anomalies in temperature sensors?"
• "Compare production output across all facilities"

🛠️ Configuration

Database Setup

The copilot connects to multiple manufacturing databases. Each plant database should have:

• production_runs table
• quality_metrics table
• equipment_sensors table
• equipment_inventory table

See docs/ARCHITECTURE.md for detailed schema requirements.

Plant Configuration

Define plants in config/plants.yaml:

plants:
  - id: plant_a
    name: "Assembly Plant A"
    location: "Midwest Region"
    connection_string: ${DATABASE_PLANT_1_URL}
    timezone: "America/Chicago"
  - id: plant_b
    name: "Manufacturing Plant B"
    connection_string: ${DATABASE_PLANT_2_URL}
    timezone: "America/New_York"

📈 Metrics Explained

• OEE (Overall Equipment Effectiveness) = Availability × Performance × Quality
• MTBF (Mean Time Between Failures) = Total Operating Time / Number of Failures
• MTTR (Mean Time To Repair) = Total Downtime / Number of Repairs
• Takt Time = Available Production Time / Customer Demand
• First Pass Yield = Units Passing QC First Time / Total Units Produced

🔐 Security

• Role-based access control (RBAC) filters sensitive data
• All database connections use SSL/TLS encryption
• Audit logging for all queries
• No sensitive data in conversation history
• PII removal in aggregated reports

📚 Documentation

• docs/ARCHITECTURE.md - System design and component details
• docs/DATABASE_SCHEMA.md - Required database structure
• docs/API_REFERENCE.md - Python API documentation

🧪 Testing

pytest tests/ -v --cov=src

🤝 Contributing

Contributions welcome! Please see CONTRIBUTING.md for guidelines.

📄 License

MIT License - see LICENSE file for details

👨‍💼 About

Built by Sainath Pattipati for enterprise manufacturing environments requiring real-time intelligence across distributed operations.

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Questions? Open an issue or contact via GitHub.