Genesee County MentorAPM partnership has been expanded following a successful pilot programme, with the utility adopting additional digital tools to improve asset data management, risk modelling and capital planning.
The Genesee County Drain Commissioner Division of Water & Waste Services (GCDC-WWS), which serves around 155,000 residents across Michigan, has contracted for three solutions: MentorLens, Risk & Criticality Analyzer and Asset Investment Planner.
Genesee County MentorAPM partnership strengthens asset data capabilities
The MentorAPM platform is designed to support utilities in modernising asset management processes, particularly by improving the speed and accuracy of data collection.
MentorLens enables field crews to capture asset condition data using mobile devices, with artificial intelligence analysing images to generate condition scores and extract key asset attributes. This approach reduces reliance on manual data entry and enables faster, more consistent data collection.
According to the utility, the technology addresses longstanding challenges associated with traditional condition assessments, which are often time-intensive and quickly become outdated.
Integrated risk modelling and capital planning
By combining continuously updated asset condition data with operational history, GCDC-WWS can apply dynamic risk modelling through the Risk & Criticality Analyzer.
This enables the utility to identify and prioritise critical assets more effectively, supporting better-informed decision-making around maintenance and investment.
The data also feeds into the Asset Investment Planner, allowing for the development of capital improvement plans based on current, risk-informed insights rather than static assessments.
Supporting proactive infrastructure management
The expanded use of digital tools reflects a broader shift across the water sector towards proactive, data-driven asset management.
By integrating asset data with existing systems, including GIS and SCADA platforms, utilities are increasingly able to improve forecasting, optimise resource allocation and reduce the risk of asset failure.
This approach is particularly relevant for large and ageing infrastructure networks, where maintaining reliability and resilience requires more accurate and continuously updated information.
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