MID-SIZED MID-ATLANTIC UTILITY – USA
In order to reduce replacement costs and failures, a mid-size Mid-Atlantic utility engaged Xylem for help developing a machine learning approach to building a focused and cost-effective pipeline renewal strategy.
A mid-sized Mid-Atlantic utility with a reputation for taking a proactive and focused approach to continuously improving service reliability to their 270,000 customers was facing all too common situation. More than 1,000 miles of water mains across their system, with an average age of about 50 years. This had led to an increase in water main breaks, and so they started seeking innovative strategies that would improve service reliability while minimizing repair and replacement costs.
With water main breaks increasing, the customers served by the utility were challenged with unpredictable service outages and costly repairs as well as highly disruptive road closures. They desired to take a more proactive approach to prevent main breaks and improve their customer level of service (LoS) by focusing on the pipes that needed the greatest attention.
Previous experience in working with Xylem to manage their PCCP (prestressed concrete cylinder pipe) inventory led the utility to seek out a better replacement prioritization strategy than traditional techniques such as age and break history.
What solutions did Xylem and the utility come up with to solve this challenge? Find out and explore the results we achieved together by downloading the full case study below.
Will help the utility lower their annual costs related to pipeline replacement from $90 million to just $20 million while achieving a dramatic four-fold reduction in failures.
Developed a plan to reduce customer outages and improve service reliability, while cutting replacement spending by over 70% compared to other prioritization methods.
Developed a real-time, field mobile tracking application to improve break record accuracy that reduces labor time required to update their Computerized maintenance management system (CMMS) and their geographic information system (GIS), as well as improve the output of the AI model
|• Pipeline failure and risk analysis|
|• Mobile field data collection application|
|• Data integration with the utility’s existing systems|