Revenue Assurance ConsoleWater billing · 29 months

Update the dashboard from Excel

Download the template (pre-filled with the current data), edit the figures in Excel, then upload it here. Every chart and metric refreshes from your workbook. The data is stored in this browser, so it stays until you reset.

Required sheets: Billing_Performance, Customer_Classes, By_Meter_Type, Risk_Register, Top_Consumers, Major_Consumers_Monthly, Reading_Intervals, Water_Losses. Keep the sheet names and column headers as in the template; edit only the values. Months stay as text like "Jan-26".
Billing performance report · Jan 2024 — May 2026
Latest closed month: May 2026
The win · billing quality

Actual meter reads

96.6%
76.9% 96.6% of billed accounts

Estimated bills have all but disappeared — from 4.3% of accounts down to 0.2%. Unbilled accounts fell from 18.8% to 3.3%. The read-to-bill process is now reliable.

The concern · revenue leakage

Zero-consumption accounts

32,684
16,293 32,684 · now 34.8% of all accounts

The count has roughly doubled since Jan 2024 and keeps climbing (+29% YoY). A third of the meter base is billed but consumes nothing — the single biggest revenue and data-integrity question in the data.

01

Water losses (non-revenue water)

Share of water that does not convert into billed revenue each month — the gap between what enters the network and what reaches a bill. The single largest leak in the system is the system itself.

Monthly vs year-to-date losses
 
Monthly losses Year-to-date average 2026 target (39%) Khareef (Jul–Sep)
Losses = volume not converted to billed revenue (non-revenue water). Year-to-date average resets each January.
02

Billing quality transformation

Share of accounts billed on an actual read, an estimate, or not billed at all. Higher actual share means cleaner revenue and fewer disputes.

Actual read % Estimated % Unbilled %
03

Reading cadence & at-risk volume

Two views of the same story: accounts read more than 32 days apart, and the billed volume tied to them. Switch between them. Outliers are flagged automatically; the meter-replacement peak (Jul-25) and the recovery window where missing days were re-billed (Aug–Oct-25) are marked.

Long reading intervals (>32 days)
 
Trend Outlier Meter replacement (Jul-25) Recovery (Aug–Oct-25)
COUNTIF(month, ">32") over Days_Difference_Analysis, and the billed volume (m³) of those accounts. Outliers flagged beyond mean ± 2 standard deviations.
04

Consumption trend

Total billed water volume (m³) as a controlled process. Switch between the full total and the adjusted base with late-read accounts removed — the outliers disappear once catch-up billing is taken out.

Output vs statistical control band
 
Volume billed Outlier Trend Khareef
Total billed volume vs. the adjusted base (late-read accounts removed). Control band uses Tukey fences (Q1−1.5·IQR to Q3+1.5·IQR).
05

Account base vs. silent meters

Billed accounts keep growing (+7.2% YoY), but zero-consumption meters are growing faster. Net active, paying meters are barely moving.

Billed accounts Zero-consumption accounts
06

Where the water goes

Consumption by customer class for May 2026. A very small set of major accounts drives the majority of volume — concentration that is both an opportunity and a risk.

CONCENTRATION READ-OUT
07

Revenue-at-risk register

The accounts that need field action, broken out by how long they have been silent or unbilled. Permanent zeros and long-term unbilled meters are the highest-priority investigations.

Zero-consumption accounts over time
08

By meter type

Billed accounts and consumption split by tariff/meter type code for May 2026. Codes 611 and 615 dominate the account count; types 611 and 711 carry the heaviest volume.

Type codeBilled acctsVolume (m³)m³ / acct
09

Top 20 consumers (rolling 12 months)

The accounts carrying the most volume. These are the meters where read accuracy, leak checks, and tariff correctness matter most — and where any silent month is materially expensive.

#AccountType12-month m³Monthly avg
11

What the data says to do

Reading the trends as decisions, not just numbers. Ordered by likely revenue and risk impact.