A Global OEM Leveraged Data Mesh to Restructure its After-sales Quality Assurance System
Our client was looking for a solution to enhance quality control systems by leveraging data from 50+ sources in a variety of formats and complexity levels.
operational expenditure with predictive maintenance and optimized repair processes
vehicles involved in customer insights for product development
in the data structure to accommodate the growing data volume
Background and Challenge Story
A global OEM (Original Equipment Manager) faced challenges with its after-sales quality assurance systems. Their original systems struggled to analyze vast, diverse data that originated from over 50 sources in various formats and complexity levels. The data and processes displayed redundancies, inconsistencies, and delays, which made it difficult to consolidate inputs for decision-making.
The main goal was to improve the after-sales quality assurance system to ensure that clients could efficiently leverage data to monitor vehicle and parts quality, predict field failures, and enhance overall quality. The project also considered how to reduce associated costs.Â
Solution Story
Adastra replaced the legacy system with a Data Mesh and deployed a scalable on-premises central data management system. This new system easily accommodated the client's diverse data sources and formats, which provided a solid foundation for streamlined data processing. The solution identified, prepared, and structured relevant data for specific demands and use cases, overall optimizing data structures for maximum efficiency and relevance.
Furthermore, user-friendly interfaces were developed to facilitate seamless communication between multiple stakeholders. This made it easy for users to access, interact with, and interpret relevant data. Adastra also provided support and training to empower over 2,000 users in creating their own data analysis solutions. This helped facilitate a culture of continuous improvement and innovation within the organization.
Adastra also introduced tailored analysis frameworks, which enabled on-demand problem-solving and efficient monitoring of the assurance systems. These frameworks help the client identify improvement opportunities, which enables quick action and helps maintain high-quality standards.
Impact
Operational expenditure was reduced significantly thanks to predictive maintenance, optimized repair processes, and enhanced warranty claim management. Product development was also significantly improved because customer insights – gathered from over 200 million vehicles – were now able to be conveniently interpreted.
Furthermore, the restructuring ensured data structure scalability, which allowed the solution to easily accommodate the growing volume of data generated by its ever-growing IoT-enabled fleet. This not only streamlined the data management process but it also future-proofed the system, making it adaptable to the evolving requirements of the industry.
- Developed analysis frameworks empowers the solution and allows on-demand problem solving.
- Seamless identification of improvement opportunities through analysis frameworks integrated with monitoring systems.
- User-friendly Interfaces were deployed for multiple stakeholders' use, enabling cross-function compatibility.
