CASE STUDY

Solution Implemented 

UCBOS implemented an AI-driven predictive maintenance solution integrated with existing IIoT sensor network, cloud-based data platform, and ERP/CMMS systems. Our experts leveraged data from the production line’s existing sensor network, analyzing real-time information with AI to predict equipment health and possible maintenance: 

• Intelligent Anomaly Detection: Utilized AI-powered algorithms to identify subtle deviations from normal operating conditions based on sensor data (vibration, temperature, pressure, etc.), flagging potential equipment failures before they occurred. 

• Predictive Failure Modeling: Built predictive models based on historical maintenance data, equipment specifications, and real-time sensor readings to forecast the remaining useful life of critical components. 

• Automated Maintenance Recommendations: Developed automatically generated prescriptive recommendations for maintenance actions, like part replacements, lubrication adjustments & operational parameter optimizations, based on the predicted failure modes.  

• Dynamic Scheduling & Work Order Management: Integrated CMMS enabling automated work order creation and scheduling based on the predictive maintenance recommendations. The system dynamically adjusted maintenance schedules based on real-time equipment health and production demands. The system warns in the event of a Cyber threat on equipment, while adhering to the regulatory standards for the equipment. 

• Real-time tracking: Engineers can observe equipment in real time and address any urgent issues. 

• Environmental monitoring: Alerts if the operating environment is outside of normal parameters. 

This integrated solution created a centralized view of equipment health and maintenance activities, accessible to authorized personnel via dashboards and mobile devices. 

Business Outcome 

By leveraging AI-driven Contract Analysis and Automation, UCBOS transformed the contract management process from a manual, error-prone system to a fully automated, data-driven operation. The results were significant: improved efficiency, reduced costs, enhanced compliance, and better business outcomes.  

• 100% Automation: The manual data entry and spreadsheet tracking were completely eliminated, freeing up staff to focus on more strategic tasks. 

• 90% SLA Improvement: The automated tracking of key performance metrics and timely alerts enabled the hospital to proactively manage its contracts and ensure compliance with SLAs, resulting in a 90% improvement. 

• Improved Visibility: The centralized contract repository provided a single source of truth for all contract information, enabling better decision-making and risk management. 

• Enhanced Negotiation Power: Access to accurate and up-to-date contract data empowered the hospital to negotiate more favorable terms with vendors and partners, resulting in significant cost savings.