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Conestoga Applied Research
Conestoga Applied Research

Turning raw data into real-time insights for advanced manufacturing for Karmax

June 17, 2025
Manufacturing

Developing artificial intelligence tools that help transform operational data into actionable insights for advanced manufacturing environments

Arkem El-Ghazal and Bassim Elhassan, SMART Centre

About Karmax

Karmax is a leading manufacturer focused on operational excellence, innovation, and continuous improvement across its manufacturing operations.

The challenge

Like many manufacturers, Karmax generates large volumes of operational data. The company sought to make better use of this information to support decision-making, improve visibility into performance, and create additional value from its existing digital infrastructure.

Approach

  • Collaborated with Karmax to explore artificial intelligence and machine learning applications
  • Developed a prototype solution that integrates with existing digital systems
  • Evaluated opportunities to convert operational data into actionable insights
  • Tested the concept within a manufacturing environment
  • Delivered supporting documentation and technical resources

Results

  • Developed a functional proof of concept demonstrating the use of AI and machine learning in manufacturing operations
  • Integrated the prototype with Karmax’s existing digital tools
  • Delivered software, technical documentation, and a final research report
  • Demonstrated a framework for generating real-time operational insights
  • Established a foundation for future expansion and adoption across additional operations

Why it matters

This project demonstrates how manufacturers can use advanced analytics to gain greater value from existing data. The work supports:

  • More informed operational decision-making
  • Improved visibility into manufacturing performance
  • Greater use of AI and machine learning within industrial environments
  • Future scalability across additional processes and facilities
  • Continued advancement of data-driven manufacturing practices

Get started with your next project

We acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC).

Natural Sciences and Engineering Research Council of Canada


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