"1st Data Analytics Platform for water meter replacements"According to Google Brasil.
Water Meter Ranking with scoring for expected return or payback according to planned investment
Geolocation of prioritized water meters to support route planning. Heatmap of regions, locations and sectors for analysis of areas based on inefficency and payback potential
Yield analysis and degradation curves by region/locality entities.
Individual analysis of substitutions and monitoring of results after substitutions.
Visualize financial impact on billing based on replacement of water meter suggestion list.
More assertive water meter replacements have a positive impact on commercial operation, the correct replacement enables direct increase in revenues while providing a conscious and fair water consumption billing.
By applying state-of-the-art technologies such as Big Data and Artificial Intelligence, Scora Acqua can process and analyze multiple variables directly related to equipment degradation and consumption patterns with great speed and accurancy.
We collect all variables(70+), including geolocations necessary a water meters evaluation, this enabled Scora Acqua to support management by provinding indicators and priority of the important points of your install base.
Support for hydrometric work and smart identification of water meters that contribute to increased Operational / Commercial Losses.
Water distribution is precarious in most parts of Brazil. According to Brazil’s National Sanitation Information System (Sistema Nacional de Informações sobre Saneamento, SNIS), on average, 38.3 per cent of water is wasted, mainly because of measurement errors, leaks, and frauds.This is equivalent to 6.5 trillions of litres of drinking water, which is estimated to cost about USD 4 billions. Measurement errors mainly occur because of badly performing water metres that result from water companies' incapacity to maintain and update these equipments with low degradation levels. Water and sanitation companies can only afford maintenance costs of 15% of their installed base. In order to tackle this issue, we developed an Analytical and Machine Learning Product that can properly priorize the right and most degraded water meters installed in the house, to achieve the higher reduction of water losses, and ROI .