Case Study
The Client
TVS is an Indian business conglomerate that has a combined turnover of over $ 6.5 billion. With a workforce of around 39,000 people, it is India’s leading supplier of automotive parts. TVS Group has over 90 subsidiary companies such as TVS Supply Chain Solutions and TVS Motor Company. The group is well-known for their adoption of Total Quality Management and five of their companies have won the Deming Prize, a prestigious TQM award.
Project Objective: Telematics Solutions
TVS envisioned deploying an automotive telematics solution that locates their vehicles and its events in real-time. The objective is to cater users with live information related to various aspects of the vehicle operation with telematics fleet management.
Business Solution
The platform was designed to operate for 5000 vehicles in the first phase. We provided an API to mobile as well as a dashboard app for real time information of the vehicles such as trip details and vehicle health, for the customer. The telematics platform was deployed to consume data in real time from the IOT core and enables building a pipeline to process the incoming data and load appropriate data endpoints. Other key implementations of the platform include providing notifications and alerts, a high performing data layer for large analytical and operational workloads in real time, an enterprise data warehouse with aggregated data for each vehicle and a scalable API layer with monitoring and easy integration. Monitoring provides a range of safety precautions such as monitoring speed, notification of accident-prone areas and harsh braking locations, continuous driving etc.
The Impact
Real-time data and analytics on vehicle operation
Improves driver safety through accident prevention measures
Boosts customer satisfaction by providing users with information such as the nearest service center and RSA to vehicles.
Enhanced safety standards insights such as vehicle location, driving patterns, engine diagnostics and vehicle activity
Collection of predictive insights and geo-fencing
Reduced operating costs from simplified access to information for users
Implementations
- BigTable as the primary real time Database.
- Apache Beam with Java with Dataflow as the pipeline builder and runner respectively.
- Pub/Sub as the messaging service.
- Kafka to handle real time data feeds
- Google Kubernetes Engine to host the API Layer
- Google Cloud Function for event based triggers with NodeJS as the backend language and React as front end application
- Google Cloud Memory Store for In-memory trip state and lookups.
Low application latency
Technology Stack
Google Kubernetes Engine
Cloud DataFlow
Cloud Pub/Sub
Cloud Memory Store
Cloud Storage
Cloud BigTable
Cloud SQL
Cloud BigQuery
Cloud Function
Virtual Private Cloud
Cloud Load Balancer
Cloud Armor
