ASEAN Support Program for GX/DX Human Resource Development

Low-Cost OEE Visualization for Productivity Improvement

EEC Automation Park, Burapha University
  • Course Title :

    Low-Cost OEE Visualization for Productivity Improvement

  • Partner Organisation :

    Mitsubishi Electric Factory Automation (Thailand) Co., Ltd.
    Silpakorn University

  • Date :

    Batch 1 : 02/07/25 - 05/08/25
    Batch 2 : 30/07/25 - 29/08/25
    Batch 3 : 24/11/25 - 09/01/26

  • Period :

    8 days (48 hours) in total

  • Language :

    Thai

  • Venue :

    EEC Automation Park, Burapha University (Google Map)

  • Training fee :

    39,185 THB/person (19,592.50 THB will be subsidised)

*The main content of the course was developed by the institution, and Ameicc was not involved in the development of this course.

Unlock the power of smart manufacturing with our hands-on training program designed for the real world. This course equips you with practical skills using Arduino, Raspberry Pi, and Node-RED, combined with real factory case studies and on-the-job training. Learn how to implement low-cost, scalable digital solutions, visualize and analyze OEE data, and drive improvements in productivity, energy efficiency, and sustainability.

What You’ll Learn:

Module 1: Understanding OEE and Basic Arduino
Day 1 – Learn the fundamentals of Smart Manufacturing Kaizen Level (SMKL) and OEE concepts to identify production losses.
Day 2 – Get hands-on with Arduino programming and connect industrial sensors for real-time data collection.

Module 2: Raspberry Pi for Industrial Automation
Day 3 – Set up Raspberry Pi, build data flows with Node-RED, and store data using MySQL.
Day 4 – Install and test the system in a real factory during your first on-site training (OJT 1).

Module 3: OEE Calculation and Data Visualization
Day 5 – Extract production data from MySQL and calculate OEE using real machine inputs.
Day 6 – Create live dashboards to visualize performance and losses in real time.

Module 4: Low-Cost OEE factory application
Day 7 – Apply your OEE system in a second factory and refine it through real use (OJT 2).
Day 8 – Present your project, showcasing productivity gains, energy savings, and CO₂ reduction.

Exclusive Training Advantages

  • Gain real-world skills through hands-on training and factory-based OJT with Arduino, Raspberry Pi, and Node-RED.
  • Tackle real production challenges through case studies and project-driven learning.
  • Build affordable, scalable smart solutions with minimal coding.
  • Monitor live OEE data to identify losses and improve efficiency.
  • Reduce energy waste and carbon emissions with smart, sustainable practices.

Key Benefits of This Training

  • Maximize Operational Efficiency
    Experience the power of real-time machine monitoring to reduce downtime and increase production efficiency.
  • Reduce Energy Consumption and Cut Costs
    Implement smart systems that minimize energy waste, lower operational costs, and significantly reduce your carbon footprint.
  • Make Smarter, Faster Decisions with Better Data
    Use industry-ready tools like Arduino, Raspberry Pi, Node-RED, and MySQL to collect high-accuracy data that enables smarter, faster, and more strategic decision-making.
  • Accelerate Scalable Digital Transformation Develop flexible, low-cost SCADA-style platforms through hands-on training and solutions that grow with your business and support your Industry 4.0 roadmap.
  • Equip and Empower Your Workforce
    Equip your team with future-ready skills through real-world projects.

Who Should Join?

  • Engineers, production managers, and factory teams looking to improve processes and implement real-time monitoring solutions.
  • Manufacturing companies aiming to boost OEE, reduce downtime, and take the next step toward smart, digital operations.
  • Factories seeking cost-effective automation and self-developed solutions using practical tools like Arduino, Raspberry Pi, and Node-RED.

More information about this Training Program! Click here

Further Information & Support

E-Mail: automationpark@eng.buu.ac.th

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