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SugarNXT

Overview

SugarNXT is a real-time AI-powered Command Center for sugar manufacturing plants. It monitors critical machines, predicts failures, and provides operators with a unified dashboard for zero-touch, cane-to-bag automation. Built for the AI-Driven Smart Sugar Factory Hackathon, it leverages advanced AI, 3D visualization, and real-time analytics to eliminate unplanned downtime and maximize efficiency.

Features

  • Live Command Center Dashboard: Animated KPIs, real-time plant health, ROI savings ticker, live telemetry charts, and critical failure simulation.
  • 3D Digital Factory Twin: Interactive 3D factory floor with live machine status, steam effects, and immersive inspection panels.
  • AI Alert Engine: Multi-layer anomaly detection, actionable alerts, severity filtering, session retention, and export options.
  • AI Chat Assistant: Natural language queries with live sensor context, critical alert badges, and quick query chips.
  • Predictive Failure Countdown: Per-machine timers, urgency badges, animated progress bars, and accelerated countdowns for critical states.
  • Alert Sound & Notifications: Web Audio alarms and browser push notifications for critical events.
  • Analytics Page: Efficiency trends, risk history, alert heatmaps, health indicators, and maintenance cost breakdowns.
  • Alerts Management: Card and terminal views, severity filters, export, clear/dismiss, and real-time alert counts.

Architecture

  • Presentation Layer: Next.js 15 (App Router), React 19, TypeScript
  • Component Layer: Modular components for dashboard, digital twin, analytics, alerts, and more
  • State Management: React Context API for machines, alerts, KPIs, session state
  • Logic/Hooks: Custom hooks for simulation, risk calculation, spike detection, trend analysis, alert generation, and ROI tracking
  • Utilities: Thresholds and risk calculators for machine-specific logic
  • Rendering Engines: Recharts (2D charts), Three.js + React Three Fiber (3D), Framer Motion (animations), Tailwind CSS (styling), Web Audio API, Notification API

Technology Stack

Technology Role
Next.js 15.1.0 React framework, routing
React 19.0.0 UI, hooks, context
TypeScript 5.x Type safety
Tailwind CSS 3.4.x Styling
Framer Motion 12.x Animations
Three.js 0.183.x 3D rendering
React Three Fiber 9.x Three.js bindings
@react-three/drei 10.x 3D controls/effects
Recharts 3.x Charts
Lucide React 0.575.x Icons
Web Audio API Alarm sounds
Notification API Push notifications
Bun Package manager

Project Structure

  • app/ — Main pages: dashboard, digital twin, analytics, alerts
  • components/ — UI components: AIChatAssistant, AlertNotificationEngine, ROITicker, DigitalTwin, etc.
  • context/ — FactoryContext for global state
  • hooks/ — Custom hooks for simulation and logic
  • utils/ — Thresholds and risk calculation utilities

Setup & Installation

  1. Clone the repository:
    git clone <your-repo-url>
  2. Install dependencies:
    bun install
  3. Start the development server:
    bun run dev
  4. Open http://localhost:3000 in your browser.

Usage

  • Use the dashboard for live KPIs, alerts, and ROI tracking.
  • Explore the 3D digital twin for machine status and inspection.
  • Trigger critical failure simulation for demo/testing.
  • Chat with the AI assistant for predictive insights.
  • Export alerts and analytics for maintenance and reporting.

Future Roadmap

  • IoT sensor integration (MQTT, Node.js API)
  • Real-time backend (WebSocket, REST)
  • Time-series database (InfluxDB, TimescaleDB)
  • ML predictive models (TensorFlow.js, scikit-learn)
  • Multi-factory deployment (AWS IoT Core, Azure IoT Hub)
  • Mobile app (React Native/PWA)

Hackathon & Team

  • Built for: AI-Driven Smart Sugar Factory Hackathon (Feb 2026)
  • Team Name: DreamWork
  • Demo URL: coming....

License

[Specify your license here]


SugarNXT v1.0 — Built for the AI-Driven Smart Sugar Factory Hackathon Challenge


References

  • See HACKATHON_PPT_CONTENT.md for detailed pitch, features, architecture, and demo script.

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AI-powered Smart Factory Command Center for Zero-Touch Sugar Manufacturing with 3D Digital Twin and Predictive Maintenance Simulation.

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