Case Study
A Smart Public Transportation Provider Implements Modern Engineering Practices to Accelerate their Platform Roadmap
The solution improves estimation accuracy and reduces deployment time and testing time while enabling continuous monitoring of engineering excellence.
Overview
The client provides innovative solutions for automatic fare collection, transit information, and transit analytics. As one of the world’s largest suppliers of smart public transportation systems, their software technology is deployed in major cities all around the globe, processing over five billion transactions annually.
Challenge
The client wanted a partner who could provide scale, deliver product development expertise with modern technologies, and bring modern engineering, such as continuous delivery practices, short release cycles, and back- and front-end test automation.
Solution
Ness’s engagement started in 2012 with a small team to enhance and support the client’s core platform services. Today, it has evolved into an Intelligent Engineering Center (IEC) with over a dozen PODs that build, enhance, and maintain key building blocks of their product portfolio. It includes high-performance, cloud-native fare collection systems, device monitoring, ticket booking engines, passenger management platform, and embedded software engineering for fare collection devices, deployed in the field. Ness worked on new product development initiatives, such as mobile ticketing, which started based on an innovative idea that Ness brought to the client. The company helped drive engineering excellence by enabling agile-based development methodologies, DevOps for faster releases, and test automation for speed and quality. The client leveraged Matrix, Ness’s data-driven engineering tool, which helped them measure their teams’ productivity and quality and identify trends and deviations with an overall aim to increase the predictability of delivery.
Result
Ness’s solution improved the estimation accuracy by 30% and reduced the deployment time for particular PODs by 80% and testing time by 75%. Further, it enabled continuous monitoring of engineering excellence through engineering metrics.