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This manufacturer needed an analytics solution to develop predictive models and provide meaningful proactive recommendations to their fleets.

Challenge

Our client, a large-scale automotive components manufacturer, had recently implemented a real-time health feedback solution for fleets of vehicles using their components. The system used IoT engine and sub-system data points to create real-time notifications of issues with the vehicles along with recommendations for correcting those issues. This system was implemented in Azure using Event Hub functionality and custom development.

While this solution was successful, it was also limited to only reactively responding to warnings and errors as they occurred.

The company needed an analytics solution to create functionality for developing predictive models that provide meaningful proactive recommendations to the fleets.

 

Solution

To support the high-level analysis and machine learning required to build predictive models, a large amount of data over an extended time frame was needed. Since the current system only retained data long enough to provide real-time responses, another big data solution was required to capture and retain the IoT data.

Additionally, there was a need to integrate manufacturing data with the IoT data to be used in the analytics processes. To do so, we designed and implemented a Data Warehouse to collect and organize the data and provide a data platform for high-end analytics and machine learning.

Working alongside the client, we also:

  • Developed an Azure and cloud solution architecture
  • Identified recommended technologies and planned for implementation
  • Designed and developed an integration model to ingest enterprise source data
  • Built the Azure data pipelines to process the near real-time data from devices
  • Integrated data into a curated data model for BI & analytics
  • Demonstrated the BI capabilities to prove the business value of the data

Ultimately, the solution uses the existing Event Hub functionality to output the data, Azure Data Factory to manage the data flow, and Snowflake Data Warehouse to store, organize, and integrate the data.

Outcomes

Getting the right tools and technologies in place has allowed this client to implement a process of analysis and predictive modeling to continually upgrade and enhance their notification system, providing increased value to the fleets they support.

It has also allowed them to seamlessly scale, adding more fleets and more data volume, without needing to replace or adjust any part of the existing solution. Because of this, they were able to expand from 10s of millions of records per month to 100s of millions of records per month with no intervention on their part.

Enabled predictive analytics
Enabled predictive analytics
Increased scalability
Increased scalability
All cloud-based data storage
All cloud-based data storage

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