Software : Data & Analytics : Asset Performance Management
Falkonry was founded with the mission to enable step improvement in operational excellence through data and computation. Today, Falkonry’s predictive operations solutions are used by companies, both large multinationals & regional manufacturers, to power their digital transformation & achieve significant improvements in production uptime, quality, yield and safety. Just like an expert eye, Falkonry can discover insights hidden in your operational data and deliver timely, actionable intelligence. We empower our users — plant personnel, process or maintenance engineers, line operators, analysts — to make better operational decisions with evidence-based approaches.
Where is 'The Edge' and why does it matter?
The Edge is not a place – It is an optimization problem. Edge computing is about doing the right things in the right places. As with all optimization problems, getting to the “right” answer requires considering a number of tradeoffs that are specific to your situation and then applying the right technology to maximize the benefits for the cost you are willing to pay.
Part of what makes Edge confusing is that definitions of “The Edge” tend to focus on technologies rather than on use cases. Since use cases span a very wide range of requirements and the boundaries between those use cases don’t map directly to technologies, definitions in terms of technology can be difficult to use.
AI-based operational excellence in steel manufacturing
Modern steelmaking is heavily instrumented with several process parameters being monitored, yet there are limited operational insights available in real-time. Take, for instance, the continuous casting process − a facility producing 150 tonnes per hour can generate over US$5 million per day in production revenue, assuming current steel prices. Conversely, a single day of lost production is equivalent to US$5 million worth of losses. Therefore, a manufacturer can unlock tremendous value by eliminating these unscheduled production downtimes.
Casting molten steel, unsurprisingly, is hard on heavy equipment. Components wear under harsh conditions leading to failures or adverse product quality. Early detection of such conditions could warn the maintenance and production managers to schedule repairs before failures occur. Applying advanced analytics to machine and process data can help in predicting such unwanted events. Data-science projects are often designed for specific use cases thereby limiting the scope and interoperability of the model. The approach faces challenges in terms of model sustenance in production and scalability across use-cases or plants.
Assisting Continued Process Verification with AI
Patterns of behavior reflected in the data from equipment sensors can give insight into these performance affecting factors. In many cases, these patterns develop before product quality is significantly affected. Putting in place analytics that can detect these patterns gives the plant operations team actionable warning before CPV limits indicate a problem. This warning can be used to limit costly production impacts. Importantly, because the CPV process itself is untouched, these kinds of pattern detection analytics can be implemented without additional filings or regulatory delay. Assisting CPV does not mean replacing or even changing CPV.
Integrating Falkonry with Azure IoT
Falkonry Clue applies advanced analytics to multivariate time-series data to discover meaningful patterns. This valuable operational data is supplied to Clue’s powerful AI engine by leveraging Microsoft Azure’s IoT infrastructure. Clue is designed to fit seamlessly into Azure’s reference architecture thereby easing the integration process.
Connecting the plant to the cloud, the Azure IoT Hub acts as a bi-directional communications brain for all connected IoT devices – managing data transfers, updates, setting up credentials for every device, and defining access control policies. These connected devices include OPC UA enabled sources such as most SCADA systems that support the MQTT protocol for data transfer.
AI Solution for Operational Excellence
Falkonry Clue is a plug-and-play solution for predictive production operations that identifies and addresses operational inefficiencies from operational data. It is designed to be used directly by operational practitioners, such as production engineers, equipment engineers or manufacturing engineers, without requiring the assistance of data scientists or software engineers.
Falkonry Secures Series A Funding to Optimize Industrial Throughput, Quality and Yield With Operational Machine Learning
Falkonry, Inc. the leading provider of operational machine learning for Global 2000 industrial companies, today announced that it has raised $4.6 million in a Series A funding round. This round brings the total funding raised by Falkonry to $10.9 million. The Series A round is led by Presidio Ventures, the early stage venture capital arm of Sumitomo Corporation. Fortive Corporation, a diversified industrial growth company, has also joined the round as a strategic investor. The early seed stage investors will enhance their existing investment positions in Falkonry, and include Basis Set Ventures, Polaris Partners, Start Smart Labs and Zetta Venture Partners.