A New Edge AI Solution Enables the Smart Factory
Fleet management companies are moving to Edge AI predictive maintenance for their vehicles. The results are improved vehicle performance and reduced costs.
Predictive Maintenance
post-template-default,single,single-post,postid-24543,single-format-standard,bridge-core-1.0.4,mega-menu-top-navigation,ajax_fade,page_not_loaded,,qode_grid_1400,qode-content-sidebar-responsive,wpb-js-composer js-comp-ver-5.7,vc_responsive
Case Studies

The Smart Factory – Evolving from Automation to Intelligence


The Smart Factory – Evolving from Automation to Intelligence

Most manufacturing environments today are highly automated. Machine and process automation are essential to producing high volumes of high-quality products at a competitive cost. The evolution from the semi-automated to the fully automated is reaching its endpoint across most manufacturing sectors.

The next evolution will involve going beyond factory automation and into factory intelligence…from the automated factory to the smart factory. This will be a continuing imperative within many Industry 4.0 initiatives and will be driven by new Edge AI technologies.


The Challenge – Factory Intelligence

The fact that machines and devices are automated does not imply that they are smart. Automation does not equate to intelligence. Even within a highly automated production environment there exists intelligence gaps, both at the micro (individual asset) level and the macro (asset ecosystem) level. This lack of deep insight is the next gap that needs to be bridged. Current challenges include:

  • Management of assets: Today’s machines perform their mechanical tasks automatically (automation), but most other aspects of asset management still require human involvement. This has a negative impact on asset lifecycle management.
  • Self-monitoring and self-reporting: Machines and devices do not have the ability to self-monitor or self-report. Critical performance insights go undetected and/or unreported.
  • Inefficient maintenance procedures: Automated machines still require maintenance. Many manufacturers rely on preventive maintenance methodologies. The smart factory will utilize predictive maintenance intelligence to maximize the performance and uptime of critical assets.
  • No single point of command and control: Factory floor management is often too decentralized to achieve maximum efficiency. A centralized, Edge-AI enabled, command and control center is needed to aggregate, synthesize and react to the volumes of data generated within the factory floor.



The Solution – The Smart Factory – MicroAI Factory™

New Edge-native AI technology will provide the means for manufactures to evolve from mechanical automation to automation intelligence. MicroAI Factory is the latest advancement in the deployment of AI and ML capabilities that bridge the gap between automation and intelligence.

MicroAI Factory was developed specifically to provide manufacturers with AI-enabled intelligence that improves factory floor performance. Endpoint AI features that include:

  • Device and machine-centric training, monitoring and reporting: The creation of a complete factory ecosystem of intelligence.
  • Real-time performance alerts: Edge-native AI that is embedded into the asset endpoint.
  • Historical trend analysis: The ability to transition from preventive to predictive analysis.
  • Intelligent workflow integration: Process automation via workflows that learn and evolve.
  • Endpoint cyber-security: Customizable and scalable to provide asset-specific protection against sophisticated Zero-Day


The Impact

The evolution to the smart factory will have far-reaching implications. Manufacturers will have the means to reach levels of operational performance that had previously been unattainable. A sampling of these advancements will include:

    • Improved Overall Equipment Effectiveness OEE: MicroAI Factory provides the means to improve OEE performance from the current standard of 70% to a new standard of ~ 85%.
    • Holistic visibility: Non-siloed, at-a-glance, perspective of real-time performance and events across the entire factory floor.
    • Rapid response: Ability to fast-track issue identification and corrective action and to identify recurring problems based on historical data.
    • Improved resource allocation: Edge AI-enabled intelligent workflows reduce the reliance on human intervention and ensure optimum execution of processes.
    • Increased production: Predictive maintenance eliminates downtimes related to unnecessary maintenance activities and/or unforeseen malfunctions.
    • Longer asset lifespans: Real-time asset health monitoring, process-driven mitigation actions, and predictive maintenance capability all combine to extend the lifespan of expensive assets.