In this episode of The Manufacturers Network Podcast, Lisa Ryan interviews Berk Birand, the CEO of Fero Labs, about the intersection of manufacturing and explainable AI. Berk shares his background as an engineer and his journey from developing algorithms for telecommunications to co-founding Fero, where the focus is on sustainability in the manufacturing industry.
Key takeaways from our conversation with Berk Birand:
1. The Importance of Sustainability in Manufacturing: Sustainability is a significant concern for manufacturing companies, and many are working hard to reduce their emissions and improve efficiency. Berk explains how Fero's mission is to help companies achieve both profitability and sustainability through the use of AI and data.
2. Efficiency and Sustainability: Berk describes how Fero's AI-driven approach helps reduce waste and improve efficiency in manufacturing, citing examples within the steel and chemicals sectors. Companies can use AI to optimize processes to enhance their profitability while reducing their carbon footprint.
3. Understanding AI in Manufacturing: Berk demystifies AI, explaining that it is a tool for extracting complex patterns from large datasets. He emphasizes the role of machine learning in deciphering intricate manufacturing data and providing valuable insights for optimization.
4. Continuous Learning in AI: AI models in the industrial sector need continuous retraining to adapt to production, raw materials, and asset degradation changes. This ongoing learning process ensures that the AI's predictions remain accurate and valuable for manufacturers.
5. Challenges and Opportunities in AI Adoption: Convincing the manufacturing industry to adopt AI technologies requires building trust through explainable, white-box machine learning models. Berk emphasizes the need for AI to be a reliable and transparent tool that supports, rather than replaces, the expertise of engineers and operators.
Actionable ideas for listeners:
1. Start from the Problem: Identify specific challenges in manufacturing processes, such as quality issues, and assess the potential for leveraging explainable AI to complement existing tools.
2. Data Assessment Verify the availability of reliable data that can drive AI solutions, ensuring that the quality of input data aligns with the desired accuracy of AI predictions.
3. Exploring AI Solutions: Consider contacting experts and conducting feasibility studies to understand how AI technologies, like Fero's, can be tailored to specific production needs.
Fun facts:
- Fero Labs focuses on a wide range of industries, including steel, chemicals, and general process industries, showcasing the broad applicability of AI in diverse manufacturing sectors.
- Berk highlights the potential for AI to assist in knowledge transfer within the manufacturing industry, leveraging the expertise of experienced workers and aiding in the training of new engineers.
Engage with Fero Labs:
If you're interested in exploring AI solutions for your manufacturing processes, contact Fero Labs via their website at ferolabs.com. Their team is ready to discuss the feasibility of integrating AI into your production environment.
