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About the Client

The client is one of the top automotive parts producers in Europe. It manufactures original equipment as well as replacement parts for the automobiles and supplies them to different production plants across Europe. The company was looking for a cloud-based advanced analytics solution which could enable it to attain real-time analytics and predictive maintenance for its equipment.

Business Challenges

The client was finding it hard to produce actionable analytics and monitor the performance of equipment that could eventually help it anticipate the downtimes and retain its productivity. Therefore, it needed:

SQL Server–based solution struggled to deliver adequate performance to end users.
Real-time monitoring of anomalies, machine failures, and predictive maintenance
A reliable system to produce insightful dashboards based on historical data analysis.

Business Solution

Royal Cyber used Azure Stream Analytics and Databricks to meet the requirements of the client. These platforms were chosen as part of our solution since they are capable of handling large volumes of data, building high-performance pipelines, and supporting real-time analytics generation that could eventually help the business optimize its operations and increase its productivity.

Key Outcomes

Reduction in annual loss i.e.,
up to 400K
US dollars

Reduction in time required to prepare and deploy machines 3 weeks to a few days

Intelligent optimization of inventory through sales forecasting with incurred loss reduced to 1% only

Reduction in data request time from days to just a few minutes

Related Case Study & White Paper

Case Study
Achieving Predictive Maintenance with Databricks
Case Study
Demand Forecasting Solution Accelerator
Case Study

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