The Lead and Copper Rule Improvements (LCRI) require utilities to submit and maintain a baseline service line inventory, update it annually, and replace all lead and galvanized requiring replacement (GRR) service lines within mandated timeframes, often while inventories still contain significant unknowns.
"Unknown service lines directly drive your annual replacement targets, even when many of them ultimately turn out to be non-lead," says Katie Deheer, data science expert at Trinnex. "Predictive modeling helps utilities reduce uncertainty so they can meet LCRI obligations more efficiently and realistically."
With milestone deadlines approaching, utilities are using data-driven tactics such as predictive modeling to reduce uncertainty, set more realistic replacement targets and prioritize verification work.
"Unknowns increase replacement targets. Predictive modeling helps utilities reduce uncertainty before those targets are set."
Katie Deheer, AI Consultant and Strategist
The reality: unknowns inflate your targets
LCRI replacement planning is based on counts of known lead service lines, galvanized requiring replacement (GRR) and unknown service lines. When inventories are heavy on unknowns, utilities can find themselves planning for replacement volumes that realistically aren't feasible, outpacing available funding, workforce capacity or construction seasons.
"That's where utilities can get stuck," Deheer says. "If you don't reduce unknowns ahead of your baseline inventory and replacement planning, your annual targets can become unmanageable."
The following four tactics show how utilities can use predictive modeling to reduce unknowns and improve replacement planning.