MaintenanceX360
MaintenanceX360 is an industrial maintenance benchmarking and KPI intelligence solution that transforms existing maintenance, cost, reliability, and asset data into ranked and actionable performance insights. It works with existing SAP and CMMS/reliability solutions to identify which plants, asset classes, assets, and KPIs are driving maintenance performance and cost.
Rather than introducing a new maintenance-management system, MaintenanceX360 provides the benchmarking and KPI intelligence layer needed to compare performance across the organization, identify persistent deterioration, and focus engineering attention on the areas creating the greatest reliability and cost impact.
Salient Features
Client Benefits
Identify High-Cost Plants & Assets
Rank plants and assets against peers to quickly identify the areas contributing disproportionately to reactive maintenance cost and reliability performance.
Detect Performance Deterioration Earlier
Identify persistent KPI decline before it develops into a larger maintenance-cost or reliability issue, enabling teams to investigate emerging problems earlier.
Prioritize Maintenance Improvement
Identify the KPI, asset, or asset class having the greatest impact on fleet performance so engineering teams can focus improvement efforts where attention is needed most.
Improve Total Cost of Ownership Visibility
Bring acquisition, operating, maintenance, disposal, and production-loss costs together at the asset level to provide a more complete view of lifecycle cost.
Dashboard & Snapshots
![Solution configuration [Defining the Objective: Maximize Production, Minimize Utility]:](https://ingenerox.ai/wp-content/uploads/2025/05/Picture3-3.png)
![Solution configuration [Defining cases of interest – Multiple cases can also be added]:](https://ingenerox.ai/wp-content/uploads/2025/05/Picture4-3.png)
Case Studies
Development of Soft Sensor to predict the C5 contents in Debutaniser column Overhead:
Background
In modern petrochemical operations, maintaining product specifications within tight limits is critical for both quality assurance and process efficiency. One of the key challenges faced by a leading chemical plant was the need for real-time prediction and monitoring of C5 content in the overhead stream of the debutanizer column — a crucial step to ensure product specification compliance and optimize operations.
Challenge
Traditional laboratory testing methods for measuring C5 content introduced significant time delays, making real-time adjustments difficult. The plant required a fast, accurate, and reliable solution to continuously monitor C5 levels, allowing operators to take timely corrective actions and avoid off-spec products.
Solution with AnalyticX
Using AnalyticX, the engineering team was able to develop a soft sensor — a machine learning-based predictive model — specifically designed to estimate the C5 content in the debutanizer overhead stream in real time.
Key steps included:
- Seamless ingestion of historical operational data into AnalyticX without any coding requirements.
- Applying statistical analysis to identify key process variables influencing C5 concentrations.
- Rapid development and training of a machine learning model tailored to predict C5 content with high accuracy.
- Deployment of the predictive model into live operations for real-time product spec monitoring.
Results
- Enhanced Product Monitoring:
Operators now have a real-time view of the predicted C5 content, significantly reducing reliance on delayed lab measurements. - Improved Process Control:
By closely tracking C5 levels, the plant was able to make timely adjustments to operational parameters, ensuring consistent product quality. - Reduced Off-Spec Production:
Real-time insights helped minimize off-spec batches, leading to material savings and reduced reprocessing costs.
Key Takeaways
- AnalyticX enabled the rapid development of a soft sensor customized for critical KPI monitoring.
