Case Study
Corsair optimizes supply chain with increased data insights and reduced costs
The U.S Computer Manufacturing industry has declined over the past five years, as the domestic market has become saturated. High import competition, exacerbated by an appreciating US dollar and advances in substitute technology have caused fierce price competition in the industry. – IBISWorld 2017 U.S Computer Manufacturing Industry Report
Corsair is one of the globally leading manufacturers of gaming PC components and peripherals, based out of the US. In this highly fragmented industry, Corsair has carved a unique brand position through a range of mid and high-end (enthusiastic) grade products.

The fierce price wars have led to lower unit selling prices and lower margins. Understandably, getting the pricing right and optimizing the supply chain are key to Corsair’s business agility and higher bottom line. To realize this goal, Corsair envisaged the below:
  • Upgrade to a newer enhanced globally accessible system that would scale to the exponentially growing data volume and enhance business agility.
  • Gain faster supply chain insights at region/hub/warehouse/product level, to effectively control costs and optimize supply chain processes for speedy product delivery.

The Challenge

Corsair’s existing system was built on traditional stack – Microsoft SQL Server database, SSAS cube, and Excel for visualizations. The system had below challenges that stifled business agility.

  • Late availability of sales & revenue information by more than 3 hours, delaying key business decisions.
  • Lack of data quality and governance resulted in siloed versions of facts.
  • Excel-centric reporting limited user experience.
  • High maintenance cost due to rigid cube architecture, and high on-premise software license costs.
  • Lack of continuous system availability whenever cube was refreshed.

Our Solution

We delivered an elastically scalable data processing and reporting solution on cloud, leveraging our advanced AWS Redshift and BI capabilities. Here’s how we did it:

  • Data from sources (Oracle EBS, CSV files) were retrieved and loaded into AWS S3 buckets.
  • This data was then transferred into the AWS Redshift Staging schema.
  • The staged data was then processed and loaded into AWS Redshift Reporting schema.
  • Comprehensive data engineering with audit balance controls were devised and developed in Snaplogic.
  • Data visualizations from Excel were transformed into rich intuitive visuals in Tableau, enabling slice and dice of KPIs
  • Row-level security framework was created enabling seamless single-sign-on.

Key Highlights

Technologies used – AWS S3, AWS Redshift, Tableau, Snaplogic

Team size – 8

Cluster – 8 node Dense Compute clusters (dc2.xlarge) 160*8 GB

Data size – Total 600 GB

Project duration – 8 months

Delivery model – Hybrid

Results and Benefits
  • Data processing time was dramatically improved by 65%, with latest data available for key business users under an hour
  • 52% performance gain in BI reports realized through right-fit cloud configuration
  • Zero downtime in BI reports enabled 24 * 7 application availability for decision makers
  • 30% reduction in TCO, thanks to industry standard scalable Cloud self-service BI architecture
  • Information redundancy was eliminated by establishing a standard data dictionary framework
  • Reduced IT dependency by transitioning end-users towards a self-service BI architecture