In 2019, the City of Fort Wayne’s Water Division decided to perform a check on its meter fleet. Specifically, the utility wanted to evaluate the effectiveness of their existing meter replacement program – which relied on random testing and factor-based criteria such as meter age and throughput to prioritize replacement.
They suspected that taking an innovative approach involving machine learning and analytics might help them more accurately identify underperforming meters and locate water lost as a result. This could be a big benefit to the utility since apparent water loss can account for about two percent of a utility’s top-line revenue.
Xylem deployed their Revenue Locator solution, a cloud-based, SaaS subscription platform. The solution leverages machine learning and analytics to provide utilities with data-driven operational guidance to identify metering inaccuracies, prioritize field maintenance activities, implement efficient meter testing and replacement programs and maximize revenue recovery.
Over the course of the program, the Revenue Locator solution analyzed all of Fort Wayne’s 2-inch meters. The data that was gathered successfully demonstrated which meters had inaccuracies and provided the quantifiable impact of how much volume or revenue would be lost if the meter were not repaired or replaced.
- Identified recoverable revenue of $264,871 over two years
- Provided an 8x improvement in meter inaccuracy identification over traditional methods
- Pay-back period of just over one year
–Ben Groeneweg, Utility Asset Management and Sustainability Manager
Before working with Xylem, we were managing our meter fleet and making inspection and replacement decisions without true visibility