Process optimization software can help support organizations in several ways. From streamlining operations to identifying and remedying flaws in current processes, process optimization software is a cutting-edge tool that industrial enterprises can use to enhance performance and maximize throughput. Fero Labs is an industrial process optimization software company based in New York. Analytics Insight has engaged in an exclusive interview with Berk Birand, co-founder and CEO of Fero Labs.
Kindly brief us about the company, its specialization, and the services that your company offers.
Fero Labs is an industrial process optimization software company based in New York. Our goal is to optimize factories with explainable machine learning to help manufacturers reduce emissions, optimize quality and increase profits. We provide plant operators, with little to no data science background, access to powerful algorithms, enabling them to understand the root cause of issues they are exploring and automatically generate explainable machine learning models that take into account key process parameters and variations. These models can be deployed in real-time to ensure that the plant operates at peak performance by continuously adjusting to the process and market changes, without the interference of highly skilled individuals, or the lack of understanding that comes with traditional AI models.
Brief us about the proactive Founder/CEO of the company and his/her contributions towards the company and the industry.
I am the co-founder and CEO of Fero Labs, and Alp Kucukelbir is the co-founder and Chief Scientist. We are committed to helping large industrial companies advance their digital transformation goals, and in turn, save the planet. I have a Ph.D. in electrical engineering and computer science from Columbia University. My academic research includes optimizing wireless and optical networks with efficient cross-layer algorithms. I have also developed scheduling algorithms for optimizing cellular base stations in 5G networks and have several patents in IoT systems for resilient fiber-optic networks. My expertise, combined with Alp’s proprietary technology developed based on Bayesian machine learning research, has played a crucial role in the development of superior industrial software that for the first time drives company growth and profits while also lowering costs and improving sustainability.
How is IoT/Big Data/AI evolving today in the industry as a whole? What are the most important trends that you see emerging across the globe?
AI and machine learning are now being looked at to solve some of the world’s most pressing issues – that being climate change and an ongoing labor shortage.
Traditionally, manufacturers have been reluctant to focus on sustainability as they believe it can impact short-term profits. However, today, AI solutions like Fero enable manufacturers to optimize for everything at the same time, including sustainability as well as costs, raw materials use, quality, throughput, and all other top-line KPIs.
On labor shortage, one crucial problem is the lack of trained workers to fill specific mid-level operational roles. Traditionally, training and certification for a role like a machinist or a toolmaker can take three to five years, dissuading many from pursuing these career paths. But with explainable machine learning options, the training process can be shortened to a few months.
Using a digital twin (a virtual copy of an industrial process), prospective operators can explore new scenarios and settings they might encounter on the factory floor. We can also experiment with hypothetical situations to test their decision-making prowess, without impacting production. This creates a new possibility for hiring and the potential to turn engineers and operators into data scientists.
How does your company’s rich expertise help uncover patterns with powerful analytics and machine learning?
Our explainable machine learning software collects data from manufacturers across a production process and creates an objective model for them to understand what works for their employees. The innovative technology provides individual operators recommendations that optimize processes and parameters in real-time. Essentially, the automatically generated machine learning models capture the skill set of top performers and turn it into recommendations for less skilled or experienced operators in the field.
The industry is seeing the rising importance of Big Data Analytics and AI. How do you see these emerging technologies impact the business sector?
By implementing predictive maintenance and explainable machine learning solutions like Fero Labs, businesses can save up to $2 million annually, increase efficiency up to 15%, and save over 34 million pounds of CO2 emissions per year, depending on the industrial plant at hand. The benefits far outweigh traditional means and truly create a win-win scenario.
How is Fero Labs contributing to the growth and transformation of analytics and big data education?
We, at Fero Labs, are dedicated to leveraging our own knowledge to educate others outside of the company. I am also an adjunct professor at Columbia University, where this spring I am teaching a course titled Machine Learning and Climate. The course will cover how artificial intelligence and machine learning tools can significantly reduce the carbon footprint of manufacturing as we know it today.
In addition, My research and innovations have earned me publications in Nature Methods, JASA and other academic publications and resources. Given my exceptional research experience (he has over 5,300 academic citations), I still continue to play an active role in the academic community, serving as area chair at the NeurIPS and ICML conferences and production editor at JMLR, the leading ML journal.
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