Organizing Brownfield Data Across Multiple Plants.
Renewable Energy Provider
Operational Excellence - Reducing Operating Costs through Predictive Maintenance.
A leading renewable energy company faced frequent equipment failures across its geographically dispersed sites, leading to unplanned downtime and increased maintenance costs. By integrating Kobai's semantic graph with their analytics platform, the company aimed to enhance predictive maintenance capabilities and optimize resource allocation.
PROJECT
Implementation of predictive maintenance strategies using semantic data integration.
WHAT WE DID
Data Integration, Semantic Modeling, Predictive Analytics.
CLIENT
Confidential Renewable Energy Company.
TIMELINE
Start: February 2025, 5 Weeks Design, 9 Weeks Implementation
PROJECT INFORMATION
The company dealt with frequent outages due to equipment failures and maintenance issues.
Kobai's approach involved integrating diverse data sources, including IoT and historical equipment data, into a flexible semantic model. This enabled the identification of patterns and failure modes, leading to improved maintenance strategies and resource planning.
CHALLENGES
- Frequent unplanned equipment failures
- Disruption in production due to outages
- Inefficient resource allocation for maintenance activities
- Difficulty in integrating diverse data sources
RESULTS
- Proactive maintenance strategies reducing downtime
- Optimized resource allocation for maintenance tasks
- Improved efficiency and reduced operational losses
- Enhanced decision-making through scenario planning
35%
Reduction in Unplanned Downtime
25%
Decrease in Maintenance Costs
30%
Improvement in Resource Allocation
Integrating Kobai's semantic graph into our maintenance operations has significantly reduced downtime and optimized our resource planning.
Michael ThomsonOperations Manager, Confidential Renewable Energy Company