Global
Case Study
A Smart Public Transportation Provider Leverages a Data-driven Performance Management Platform to Improve Customer Satisfaction
The solution reduces overcommitment, velocity variance, and estimation accuracy issues, leading to significant improvement in performance.
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 to maintain its position as a leader in the market, providing a reliable ticketing solution, which required software for ticketing devices with outstanding quality and background solutions to improve customer satisfaction. They wanted to sustain and improve their delivery process in multiple areas, especially in estimation accuracy and business value during the delivery process, accompanied by outstanding quality for all Ness-managed agile teams.
Solution
Ness leveraged its intelligent engineering accelerator, Matrix, to identify the following common patterns for the client:
- Delivery: Overcommitment and delivery issues—from 57 sprints, 20 were delivered within 20% estimated tolerance
- Predictability: Estimation accuracy issues
- Quality: Outstanding bugs oscillated around the same value that may result in delivery risk (~ 200 open bugs)
Ness, based on Matrix findings, performed the following action steps to improve:
- Productivity: Improved sprint focus and planning sessions with the product management team
- Predictability: Minimized delivery variance by applying the estimation accuracy metric
- Quality: Introduced backlog health check metric for each team, along with bug rate and outstanding bug metric goals/benchmark
Result
Matrix reduced overcommitment and velocity variance issues by improving planning sessions and applying agile best practices, leading to an almost 14% improvement in the completion rate for a comparable number of sprints.
Estimation accuracy issues needed immediate intervention. We enabled discussions between product and delivery teams to focus more on estimates during the planning session. The tickets were broken down into smaller ones with better estimates, with more than 13% improved estimation accuracy.
