When improving maintenance, reliability, energy, emissions compliance or quality – is there a way to adopt a uniform approach to overcome inherent challenges? Short of specialized hardware or external expertise, you often end up firefighting and reacting to surprises.
Predictive Maintenance to increase uptime and yield in vertical casting and rolling operations
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Explore More >>"Falkonry’s AI not only aims to facilitate significant customer impact through better decision making but has also proven to be surprisingly easy to adopt, integrate, and use by our engineers and operations experts."
“The most complex problems faced in crisis operations & contingency planning involve analysis of large, complex data sets. Machine-learning approaches, such as those provided by Falkonry, are required to augment humans in mining electronic intelligence (ELINT) data to identify system behavior patterns.”
"With our asset performance management solution, powered by Falkonry’s AI, Ternium operations teams receive actionable warnings before an event could impact the business."
“Falkonry’s AI software enables our manufacturing engineers to build predictive models themselves which is critical to deploy these solutions at scale. Our engineers were very quickly able to predict critical failures with a high degree of accuracy 2-3 months in advance.”
"The AF DCGS program is eager to bring Falkonry’s AI technology into the weapon system to provide actionable insights into our critical mission data. We look forward to enhancing our mission effectiveness by leveraging their system."
“The Falkonry system was able to leverage existing time series data from our Sierra Gorda mining operations & in less than a few weeks we were generating valuable insights that can improve our operations. The intuitive end-user interface required little training & could easily be used by our own operations team.”
"Falkonry has been a great partner in applying AI and ML to exploit operational data for both operational readiness and design improvements. Their AI finds important patterns in large volumes of multivariate time-series data of complex system-of-systems."