The vision of predictive asset management has been impeded by the reality that existing AI solutions are not close enough to the machines, devices, and processes they are meant to optimize. MicroAI™ Edge-Native AI solutions are making predictive analytics the new reality.
IT and OT Challenges
The inability to generate timely, accurate, actionable, predictive analytics is a prohibitive limitation in fully optimizing the performance and security of IT and OT assets. Common problems include:
Costly Asset Downtime
Continued reliance on legacy preventive maintenance processes that are unable to self-adjust based on current or predicted conditions of the asset. This results in maintenance that is performed too early or too late.
Lack of Predictive Security
Lack of deep asset observability creates cyber-security blind spots. IT and OT assets are increasingly vulnerable to attack (zero-day, ransomware, etc.).
Inability to break the 70% OEE (overall equipment effectiveness) barrier due to a lack of forward-looking analytics. Sub-optimized asset performance impedes operational excellence.
Lagging Customer Satisfaction
Today’s IT and OT end-users expect 100% uptimes and continuous performance improvement. The lack of meaningful predictive analytics makes it difficult to meet those expectations.
Loss of Market Share
Maintaining a strong competitive position requires the ability to predict rather than react to quickly evolving operational conditions and market demands. The inability to accurately predict leads to market erosion.
Predictive Analytics – More than Dashboards
MicroAI’s AI/ML edge and endpoint solutions embed and train predictive algorithms directly into IT and OT device and machine endpoints. This personalized technology provides developers, operators, and engineers with the predictive analytics required to achieve higher levels of operational efficiency and OEE. A synopsis of how this works:
Asset Data Acquisition
Data is leveraged from a variety of IT and OT devices and machines. MicroAI’s technology is agnostic to sensor values and types. It creates a multi-variant model that utilizes AI inference analysis to generate a wide range of predictive analytics.
MicroAI utilizes multidimensional behavioral algorithms to produce recursive analysis, training, and processing. This enables a continuous evolution of the Edge-native AI model that takes place directly at the endpoint or the edge.
Device and machine performance data is synthesized and analyzed locally—in real time. Sensitive data is also stored locally, minimizing the amount of data that is transferred to the cloud. Predictive analytics data latency is eliminated, and exposure of sensitive data reduced.
Analytics and alerts
AI at the extreme edge provides deeper, more intimate, insights into asset health and performance that have not been available with traditional AI solutions. Asset optimization is achieved via predictive insights instead of assumptions.
Presentation of real-time asset performance data via user-friendly, customizable, drag and drop dashboards. Data is customized to meet the specific requirements of various operational and business stakeholders.
MicroAI’s Edge-native predictive analytics provides IT and OT operators with the intelligence to realize several next-level operational and business objectives.
- Increase IT and OT asset uptimes via elimination of unnecessary maintenance interruptions
- Break the 70% OEE ceiling to attain scores of 80 to 85%
- Improve asset cyber security via the ability to predict and mitigate potential threats
- Leverage the power of predictive analytics without reliance on cloud support
- Increase customer satisfaction and retention levels by predicting vs reacting
- All the above result in improved operational efficiency, hardened security, reduced cost, and stronger competitive position
Interested in how MicroAI can benefit you?
MicroAI AtomML brings big infrastructure intelligence down into a single piece of equipment or device.