Case Study - Part 1

AI-Based OT Solutions

Client: JW Aluminium
Solution Provider: Synaptron India Pvt. Ltd.
Date: 04 February 2025

Key Achievements:

  • 20% Improvement in production efficiency
  • 15% Reduction in energy consumption
  • 30% Reduction in unplanned downtime
  • ROI achieved in 10 months
  • 30% Increase in marketing ROI
  • 25% Improvement in customer engagement

Business Challenge

Understanding the Challenges

Operational Context

JW Aluminium’s production lines involve aluminium rolling, annealing, and coating processes, which are energy-intensive and require precise control.

Manual monitoring and control led to:

  • Inefficient energy usage due to suboptimal furnace temperatures and rolling pressures.
  • Frequent equipment failures caused by lack of predictive maintenance.
  • Production delays due to unplanned downtime and manual interventions.
Business Impact
  • • High Operational Costs: Rising energy and maintenance costs impacted profitability.
  • • Production Losses: Unplanned downtime resulted in missed production targets.
  • • Competitive Pressure: Need to improve efficiency and reduce costs to stay competitive.

Technical Solution

AI-Driven Transformation

Synaptron deployed an AI-based OT solution to optimize JW Aluminium’s production processes.

Hardware
  • IoT Sensors:
    • Installed on critical equipment (e.g., rolling mills, annealing furnaces) to monitor parameters such as temperature, pressure, vibration, and energy consumption.
  • Edge Devices:
    • For real-time data processing and decisionmaking at the production line.
Integration

  • Seamless integration with JW Aluminium’s existing SCADA and MES systems for centralized control and reporting.

Software

  • AI Algorithms:

    • Predictive Maintenance: Machine learning models trained on historical data to predict equipment failures (e.g., bearing wear in rolling mills).

    • Energy Optimization: AI-driven optimization of furnace temperatures and rolling pressures to minimize energy usage.

    • Process Optimization: Real-time adjustments to improve production efficiency and product quality.

  • Dashboard:

    • Real-time visualization of production metrics, energy consumption, and equipment health.

Results & Impact

  1. Data Collection
    • IoT sensors collected real-time data from production lines, including temperature, pressure, vibration, and energy consumption.
  2. AI-Based Analysis
    • Predictive Maintenance Models: Analyzed vibration and temperature data to predict equipment failures (e.g., identifying bearing wear in rolling mills).
    • Energy Optimization Models: Adjusted furnace temperatures and rolling pressures in real time to minimize energy usage.
  3. Real-Time Optimization
    • The system dynamically adjusted production processes to maximize efficiency and product quality.
    • Alerts were generated for maintenance teams to address potential equipment failures before they occurred.
  4. Monitoring and Reporting
    • The dashboard provided real-time insights into production metrics, energy consumption, and equipment health.
    • Reports were generated for compliance and continuous improvement.

Business Benefits

Operational Efficiency
  • 20% improvement in production efficiency due to real-time process optimization.
  • Reduced downtime and maintenance costs.
Energy Savings
  • 15% reduction in energy consumption through AI-driven optimization.
  • Lower energy costs, saving $1.8 million annually.
    Reduced Downtime
    • 30% reduction in unplanned downtime due to predictive maintenance.
    • Improved ability to meet production targets.
    ROI
    • Achieved within 10 months of deployment.

    Synaptron’s Value Proposition

    Synap now.
    • End-to-End Solution: From hardware installation to AI model deployment and integration.
    • Domain Expertise: Deep understanding of aluminium manufacturing processes.
    • AI Innovation: Cutting-edge algorithms tailored for OT optimization.
    • Global Support: 24/7 technical support and continuous system optimization.