IFS, the global cloud enterprise software company, today announced it has signed a definitive agreement to acquire Falkonry, Inc. a California-based Industrial AI software company that provides automated, high-speed data analysis to the manufacturing and defense industries. The AI-based, self-learning solution continuously monitors large volumes of data for assets, machines, systems, and industrial processes to discover and analyze unusual behavior and causes of failures.
Over the past two decades, the growing scale of assets, machines, and fleets has generated unprecedented amounts of data, making real-time operational monitoring highly complex and hindering immediate operational enhancements, such as maintenance and process adjustments. By leveraging Falkonry’s automated and self-learning AI, organizations can democratize intelligence, enabling operational users to take timely actions to prevent asset downtimes, quality issues, and emission violations and automate process and workflow improvements.
The addition of the Falkonry self-learning anomaly detection solution to existing IFS enterprise simulation and AI-based scheduling and optimization capabilities further evidences the company’s strategy to use AI pervasively to provide end-to-end intelligent insights in EAM (Enterprise Asset Management), across ERP (Enterprise Resource Planning), MES (Manufacturing Execution System), PSO (Planning, Scheduling, Optimization), FSM (Field Service Management) and ESM (Enterprise Service Management) technology to increase people and asset productivity.
Headquartered in California, USA, and regional presence in Mumbai, India, Falkonry was founded in 2012 by CEO Nikunj Mehta. The company has customers across North America, South America, and Europe, including the U.S. Navy and Air Force, Ternium, North American Stainless, Harbour Energy, and SSAB, demonstrating its focus on industries in industrial manufacturing and Defense agencies.
IFS CEO, Darren Roos, commented: “Falkonry is unique in the market because its technology is agnostic and also it does not require data scientists. These are great differentiators for Falkonry that means the solution is both scalable and low-cost to implement—two fundamental attributes that very much align to our own values.” Roos added: “Falkonry’s technology can be applied in all industries, and whilst the team has some hugely impressive references in IFS’s focus markets on asset performance management, manufacturing execution systems, servitization, and configurable workflows, we see a really broad addressable market to capitalize on.”
Nikunj Mehta, CEO of Falkonry, commented: “The convergence of artificial intelligence and industrial processes has become increasingly crucial for organizations seeking to enhance productivity through data”. He added: “We are thrilled to join forces with IFS and looking forward to combining our unique strengths to provide a truly compelling value proposition to our existing customers as well as IFS customers.” He concluded: “Becoming part of IFS will enable us to further innovate and extend the value we create for our customers.”
“Today’s enterprise is continuously collecting asset performance data, making it a challenge across a multitude of industries from manufacturing to service to put it in the right context and take action in real-time. Organizations using artificial intelligence and machine learning models with their data for self-learning asset performance anomaly detection will generate critical insights faster, boosting productivity and business performance,” said Brian O’Rourke, IDC Research Manager, EAM and Smart Facilities.
This acquisition follows soon after the IFS acquisition of Poka, a provider of connected worker technology that empowers factory and field operatives to work smarter, safer and drive productivity. The combination of Falkonry and Poka with IFS Cloud makes IFS the most compelling vendor for organizations wanting to establish the most progressive and effective Smart Factories of the future.
IFS expects the acquisition of Falkonry to complete in Q4 2023.