One approach to machine learning (ML) requires extensive training using captured data on high-powered servers. It allows for the creation of a deep neural network (DNN) that can then be deployed on systems requiring significantly less storage and performance. This method of training and identification is just one, but not the only way, to implement ML.
I talked with Nikunj Mehta, Falkonry CEO and Founder, about the company’s technology that can be used for real-time analytics for the Industrial Internet of Things (IIoT).
Where do you see industrial operations and managed services evolve to in five years?
We’re seeing two key trends in manufacturing and process operations that we expect to be commonplace within five years.
The first area is analytics-driven digitized industrial operations, where data from sensors, machines, and production systems isn’t being collected to just enable a few, siloed use cases. Instead, the COOs and plant managers are taking an analytics-first approach to identify the next level of improvement in production throughput, batch quality, and process safety. This aligns with where analysts such as McKinsey and Company are predicting the next 20% productivity rise in manufacturing will come from.
The second area is the revenue stream of traditional industrial equipment manufacturers evolving from a “product-driven” model of hardware with some managed services, to an “outcomes-driven” revenue model where “software + managed services” is a larger revenue contributor than product hardware. We have seen some projections where software + services will be more than 60% of revenue. Within that model, remotely managed services for assets deployed at a customer’s site may be the largest recurring revenue contributor.
Falkonry has some heavy-hitter customers using its operational machine-learning system such as Toyota, Kawasaki, Ciner, and others. Tell me a little about your company and your technology.
Falkonry provides operational machine-learning and predictive analytics to Global 2000 industrial companies to help improve throughput, quality, and safety in their operations.