Client
A leading beverage manufacturer in the USA
Industry
Manufacturing
Technology
React, Node, Azure, Cube, Databricks, Confluent
Engagement Details
Service Type: Asset continuous health monitoring system.
Model: T&M
Business Need
Build a near real-time health monitoring system for the automated bottling lines that facilitates predictive maintenance.
Challenges
- None of the 54 automated plants had monitoring systems provided by the OEM.
- Limitations of traditional BI tools (PowerBI, Tableau) to render highly granular data.
- The lack of predictive maintenance capabilities resulted in substantial costs as well as a risk of potential losses from production delays.
Services
- Headless BI with Databricks pipelines were used to render KPIs.
- Streaming ETL with kSQL and Cube connectivity was used for near real-time rendering.
- Using COX was of death prediction, a machine learning pipeline and reinforcement learning algorithm were used to predict machine breakdown about 6 hours earlier.
Results
- Automated Data Aggregation.
- Precision & Granularity of KPIs up to a 5-minute level.
- 10x Faster than PowerBI.
- Saved up to $1.7 Million.
- Up to 65k data points rendered in <7 secs.
- <6 mins latency.