High-performance analytics to reduce downtime disruption across plants
A leading beverage manufacturer sought to improve machine health visibility and asset management across multiple plants while reducing maintenance disruption. Existing BI tools struggled with large datasets and lacked a customized user experience for operational teams. CES built a high-performance analytics layer using Cube.js and Cube Cloud with caching and pre-aggregation, improving query speed and mobile access for on-field tracking.
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The Challenge
Maintenance Disruption Across Plants
BI Performance Limits on Large Datasets
Limited Customized User Experience
the client
Beverage Manufacturing
Technology Stack
- React, NodeJS
- JavaScript
- Azure
- Cube.js / Cube Cloud
- Plivo
Solution Area
- Machine Health Monitoring Analytics & Asset Visibility
the impact
30%
Maintenance Costs Reduced
10x
Faster Data Retrieval for Large Datasets
3x
User Interaction Time
Improved Asset Visibility
The shift was analytics-led. The result?
Faster decisions with less disruption.
The Need
The manufacturer needed a faster, more accessible analytics experience to support machine health monitoring and asset management across multiple plants. Leadership aimed to improve query performance, increase mobile usage for on-field teams, and reduce the operational disruption caused by maintenance and downtime—without relying on BI tools that struggled at scale.
Challenges
- Plant-Scale Asset Management: Managing large assets across multiple plants was difficult due to maintenance and downtime impacts.
- Large-Dataset Query Performance: BI tools struggled with high-volume datasets, creating lag and slowing operational analysis.
- User Experience Gaps: Existing tools lacked a customized user experience aligned to day-to-day asset workflows and mobile usage.
CES built a high-performance analytics system designed to accelerate data access, improve usability, and streamline asset workflows across plants.
Cube.js + Cube Cloud Analytical Services
- Implemented Cube.js and Cube Cloud to improve query performance for analytical workloads.
- Enabled faster access to high-volume asset queries through an optimized analytics layer.
Pre-Aggregation and Data Caching
- Reduced system lag by using pre-aggregated datasets.
- Applied caching to speed up repeated and high-frequency queries.
Customized Analytics Experience
- Delivered a streamlined interface aligned to operational analysis and asset workflows.
- Improved accessibility and usability compared to traditional BI tooling.
Mobile Experience and Data Synchronization
- Enabled mobile access for on-field asset tracking.
- Supported data synchronization to keep operational views consistent.
Delivery Model and Execution
- Delivered under a 3-year program with an 8-engineer team, reflecting high-complexity, multi-plant requirements.
- Maintenance Costs Reduced by 30% – Lower disruption and improved operational control reduced maintenance cost impact.
- 10x Faster Data Retrieval for Large Datasets – High-volume asset queries returned significantly faster than before.
- Reduced System Lag – Pre-aggregation and caching improved responsiveness under scale.
- 3x User Interaction Time – Improved experience increased usage and time-on-system for operational workflows.
- Improved Mobile Accessibility – On-field teams gained better access for asset tracking and updates.
strategic client partner
Take the Next Step
Need faster analytics and mobile visibility for plant-scale asset management? Let’s design a Cube-based performance model that improves query speed, usability, and operational control. Talk to our Strategic Client Partner.
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