Budget battles

The case for Water Authorities to invest in predictive models

Many Water Authorities struggle with tightening budgets, as well as increasing maintenance costs to repair ageing water assets. Read on to learn how predictive modelling will become a game changer for managing and optimising water network infrastructure, and ultimately, providing better customer experiences.

With ageing water infrastructure, the chance of network failure grows each year, while customer service expectations continue to rise. The current economic climate is also making it harder for many organisations to secure funding,, including Water Authorities. This means smaller budgets must be spread across even more repair and maintenance events.

What if Water Authorities could accurately predict which networks were likely to fail in a given 12 month period, with a clear view of the most ‘at risk’ areas? What if they could understand the direct impact of investment on network performance and downtime? And what if, armed with the right data, they could reduce or even eliminate network downtime?

This was the challenge given to Veolia’s Quentin Bechet, Smart Water Manager in Australia, who says “Most water utilities rely solely on past experience and intuition to pilot million-dollar pipe renewal programs. We wanted to help our customers make more informed decisions, and be better prepared when it came to managing their water assets”’. 

Quentin and his team responded with the development of Hubgrade Blockage Prediction, at tool that takes the guesswork out of critical investment decisions for Water Authorities. “As part of our Hubgrade Digital solutions, we developed a predictive, machine learning model, so our clients could see where to best allocate network maintenance investment. Armed with accurate data, they can understand the cost, and likely benefit, associated with a particular level of investment in pipeline repairs and maintenance, over a given period of time.” says Bechet.

The other challenge faced by Water Authorities is how to prioritise asset management & repair activity. With accurate forward looking data, they can now pre-empt where to best allocate their limited resources. This helps not only from a planning perspective, but creates operational & service efficiency.

Hubgrade Blockage Prediction has been in development and testing for the last 3 years, using real time asset data. The tool can benefit organisations facing the trade off between short-term fixes and cleans, and long-term strategy and planning: while maintenance operations (such as cleaning) can reduce failures in the short term, this ‘band-aid’ approach is not a substitute for strategic pipe renewal in the context of ageing infrastructure over the longer term.  Thanks to smart data, Water Authorities can now predict network failures with a high degree of accuracy over a number of decades.

Armed with the right data, conversations about where to invest also begin to shift - rather than being a hypothetical (or finance led) discussion about how much money should be put aside for repairs and cleaning (short term) versus pipeline renewal (long term), the conversation changes to one about risk and downtime. How much risk and network downtime is the Water Authority willing to accept for a given level of investment? Over what time period are they willing to spread the investment? And what is the optimal level of (short term) investment in maintenance versus (longer term) investment in pipeline renewal? And how will this decision  impact overall network health, and the experience this brings to their customer?

In summary, a strong predictive modeling tool can put Water Authorities back in the driver seat. Where consumers are expecting more from their utilities providers, using a tool like Hubgrade Blockage Prediction empowers Water Authorities to make smart investment decisions, and understand the impact this will have on both their own operations, and the experience for their end user.

Want to learn more about Hubgrade Blockage Prediction