Near real-time KPIs and predictive maintenance for a bottling enterprise
A US-based bottling organization needed a near real-time health monitoring system to improve insight, reduce latency, and enable predictive maintenance across automated production lines. CES implemented a headless BI solution with Databricks streaming pipelines, kSQL, Cube connectivity, and a machine learning pipeline to deliver granular KPIs, faster dashboards, and earlier machine breakdown prediction.
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The Challenge
No OEM Monitoring Across 54 Plants
Traditional BI Too Slow for Granular KPIs
No Predictive Maintenance for Bottling Lines
the client
Beverage Bottling / Manufacturing
Technology Stack
- React, Node.js, Azure
- Databricks kSQL, Cube.js
- Confluent
- Azure Data Lake, Azure Functions
- Litmus
Solution Area
- Near Real-Time Health Monitoring & Predictive Maintenance Analytics
the impact
Automated Data Aggregation
5 minute KPI Precision
10x
Faster Rendering Than PowerBI
<7 second Load for up to 65K Data Points
The shift was analytics-led. The result?
Faster, predictive bottling operations.
The Need
The bottling enterprise needed a near real-time health monitoring system for automated bottling lines that could support predictive maintenance and reduce unplanned downtime. Existing approaches relied on traditional BI dashboards that were not designed for streaming telemetry or highly granular KPIs, making it difficult to monitor line health, react to anomalies, and avoid production delays across 54 automated plants.
Challenges
- No OEM Monitoring Across Plants: None of the 54 automated plants had monitoring systems provided by the equipment manufacturer, leaving operations without consistent visibility into asset health and performance.
- Traditional BI Limits for Granular KPIs: Tools such as PowerBI and Tableau struggled to render highly granular data at the scale required, limiting the ability to view near real-time KPIs for high-volume bottling lines.
- No Predictive Maintenance Capability: Without predictive maintenance models, the organization faced substantial costs and risk of production losses due to reactive maintenance and delayed detection of potential failures.
CES designed and implemented a near real-time, analytics-driven health monitoring solution combining headless BI, streaming ETL, and machine learning–based failure prediction.
Headless BI with Databricks Pipelines
- Implemented a headless BI model where Databricks pipelines prepared and rendered KPIs without relying on traditional BI dashboards.
- Structured KPI computation to support automated data aggregation and high-frequency updates.
Streaming ETL with kSQL and Cube Connectivity
- Built streaming ETL using kSQL to process data in motion from bottling lines.
- Connected the streaming layer to Cube to enable near real-time KPI rendering for operations teams.
- Supported precision and KPI granularity at up to a five-minute interval.
Machine Learning & Reinforcement Learning for Failure Prediction
- Applied Cox time-of-death prediction in a machine learning pipeline to estimate early failure risk.
- Used reinforcement learning algorithms to refine prediction quality and timing based on operational feedback.
- Enabled machine breakdown prediction approximately six hours in advance, giving maintenance teams time to act.
Performance and Scalability at Plant Network Scale
- Tuned the solution to render up to 65,000 data points in less than seven seconds.
- Achieved end-to-end latency of under six minutes from data capture to KPI visualization.
- Designed the architecture to serve 54 automated plants without OEM-provided monitoring tools.
- Automated Data Aggregation – Telemetry from bottling lines was continuously aggregated and processed without manual intervention.
- High-Granularity KPIs – KPIs achieved precision and granularity at a five-minute level across plants.
- 10x Faster Than PowerBI – KPI rendering performance was approximately ten times faster than prior PowerBI-based reporting.
- Significant Cost Savings – The solution helped save up to $1.7 million through improved maintenance timing and reduced unplanned downtime.
- High-Volume Rendering Performance – Up to 65,000 data points were rendered in under seven seconds with less than six minutes of end-to-end latency.
- Earlier Breakdown Prediction – Predicting machine breakdown about six hours earlier reduced risk of production delays and associated losses.
strategic client partner
Take the Next Step
Looking to build near real-time monitoring and predictive maintenance for your production lines? Let’s design a streaming analytics and KPI model that improves visibility, response time, and operational savings. Talk to our Strategic Client Partner.
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