Every business aspires to improve asset performance and reduce operational risks to control costs. In some sectors, the purchase of necessary industrial machinery represents a massive investment and having unscheduled maintenance to repair or replace equipment can dramatically increase operational costs and adversely affect operations.
When high-value assets break, companies typically conduct a post-mortem to identify the root cause and then scramble to make sure the same fate doesn’t befall other assets. But imagine the savings if an intelligent fix was presented before a malfunction or catastrophic equipment failure ever occurred? This is the promise of predictive analytics.
Predictive Analytics at the Edge
Whether operating oil drills or monitoring a smart grid, companies are looking to get in front of unplanned downtime and increase ROI by integrating predictive analytics into their operations. This capability can be game-changing for organizations with extensive IoT deployments, especially for the industrial and manufacturing sectors, where predictive capabilities dramatically reduce maintenance costs and optimize overall operations.
Edge-based predictive analytics examines vast quantities of data starting at the edge, at the device. It learns patterns and compares them against past performance, ultimately providing insight into what is likely to happen to an asset in the future. Since much of the data from IoT deployments is time-sensitive, it’s crucial that businesses have a window into understanding the asset state at any given time. Thanks to advances in connectivity and the reduced compute footprint of edge analytics engines, data gleaned from sensors can now be analyzed in real-time. This can have enormous financial implications.
Performance swings may be normal in some contexts, but they may also be early indicators of upcoming serious issues. Poor performance is a general problem which should be detected and addressed quickly, but asset non-operation is serious, and complete asset failure can be catastrophic. Predictive analytics for asset maintenance addresses all of these issues.
While costs across industries vary, a 2017 ITIC study found a single hour of unplanned downtime costs businesses an average of $100,000. Gartner puts that number even higher — $5,600 per minute, which comes out to whopping $300,000 per hour on average.
But with edge-based predictive analytics, manufacturers can drastically cut down on those costs, opting to do maintenance only when needed and direct resources in a more informed and cost-effective way.
The industrial IoT has always been “connected,” but companies are looking into edge intelligence to up-level visibility and take control of asset performance by augmenting their predictive analytics and maintenance abilities.
Many platforms utilize cloud-based analytics models, which require data to be pushed to and analyzed in the cloud before being sent back to the device. This might not seem like a big deal, but ferrying loads of data back and forth takes up valuable time and bandwidth. In situations where a delay in response can have serious financial ramifications and mean the difference between a cheap repair or a total asset replacement, it’s important to be able to take action within a narrow window of time. To do this and support the growing number of IoT devices that enable faster decision making, analytics need to be pushed as close to the device edge as possible.
Greenwave’s AXON Predict is an edge analytics platform that focuses on solving Industrial IoT and predictive maintenance problems. Our platform includes built-in analytics, patterns, and highly interactive visual analytics that provide real-time insights and actions for commercial and industrial IoT deployments. AXON Predict is used for real-time and historical analytics and pattern detection to identify and predict machine behaviors, with the goal of optimizing performance and avoiding unscheduled maintenance or downtime. It’s the ideal solution for businesses in all verticals that need a window into the inner workings of their assets.
Predictive analytics is the new standard for reducing costs and risks, and for optimizing overall operations. All industrial organizations, and any business with high-value assets, should look to implement real-time predictive analytics instead of waiting for something to go wrong and racing to put out fires when it’s already too late.