Anomaly Detection: The low-hanging fruit of AI
Key takeaways:
- Traditional approaches to AI, such as developing and maintaining custom models for each location and failure mode, are labor-intensive and not conducive to scale.
- Automated anomaly detection eliminates the need for manual intervention and ongoing maintenance, making it a more efficient solution for a wide range of manufacturers.
- Falkonry's productized anomaly detection provides unmatched visibility into plant operations with minimal effort or risk, enabling the surfacing of deviations at scale.
During the course of our conversations with organizations looking to transform their operations using AI, we hear a few common apprehensions. The questions range from the very practical – is it expensive? – to the more crucial – is it risky? Or does it require extensive preparation?
While large-scale transformations may be difficult, substantial benefits are still attainable without too much exposure or effort. These benefits may be obtained with the help of anomaly detection – the so-called low-hanging fruit in AI.
Anomaly detection, particularly the kind Falkonry has recently productized is incapable of missing anomalies and provides operations personnel unmatched visibility into their plant operations. The ability to extract anomalies from terabytes of machine and process data without manual intervention gives operations teams the ability to focus their attention on emerging hotspots of the production process that are deviating from normal operations. This makes several types of equipment failures and quality issues preventable at scale.
But why is anomaly detection low-hanging fruit?
Well, consider the alternative: developing and maintaining custom models or point solutions for each and every location and each and every failure mode. With this traditional approach to AI, you don’t have to imagine the workforce effort and data sanitization that it requires, as you probably already know. Not only is this not conducive to scale, but it begs the question – who will maintain the AI through the lifecycle of the production? The vendor? An in-house data science team?
It’s one thing to go through the effort of creating the model, but entirely another to tune it to production changes. If this maintenance is also manual (no matter who does it), then it’s a hurdle that should give pause.
Understandably, it’s a challenge many manufacturers are currently grappling with and it’s one of the main reasons why there is an industry-wide shift towards products rather than projects, subscriptions rather than capitalization, and automation as opposed to manual creation. To this end, the solution for most manufacturers is to look to AI and particularly to automated anomaly detection to reap those higher-order benefits with minimum effort or investment.
If this seems interesting, hit us up for a demo, and we’ll guide you on how you can hit the ground running with automated anomaly detection.
