Improving Billing and Meter Reading Quality to Reduce Non-Revenue Water

Service Census Project for Data Clean-Up at Nairobi City Water & Sewerage Company

Non-Revenue water levels in 2011 was 40% according to WASREB Data. 65-70% of the NRW are commercial losses. Unlike leaks, the losses are not physical and not easily detectable. After validating the available data as an initial step, the project audits the water and sewerage services per premises and confirms that the data from the field matches the data in the billing system.

The company is divided into 6 regions or administrative boundaries. The pilot region was the central region which has 24500 Accounts (10% of customer base) with a mix of commercial and residential areas.

Contact: Philip Wambua Msafiri | Phone +254  3988000-405 |  Cell: +254 725847441 | wambua(at)nairobiwater.co.ke | Nairobi City Water and Sewerage Company

Lessons Learnt

  • Use of spatial data (GIS) is critical for meter management, billing and revenue collection
  • Partitioning data enables effortless management but provides an excellent venue for data manipulation
  • Data clean up can only be done internally
  • Resistance from some staff should be anticipated

The exercise should be well planned and objectives documented as new ideas keep popping up from all and sundry hence may derail the project team.

Objectives

Ensure 95% of water delivered to customers is billed by:

  • Accounting for all water connection by ensuring all connections are metered
  • Ensuring all non-conforming connections are properly maintained for meter reading and billing
  • Ensuring all meters in the filled are maintained with correct accounts in the system, all direct connections are metered, damaged meters are replaced etc.
  • Geo-referencing all meters for monitoring and consumption analysis

Project Description

First activities: Development of a data collection sheet, the database for data storage and updating cadastral maps Training In using the data collection sheets, field training for data collection as well as office analysis

Data Collection: By plot to plot registering the water connection situation and checking meter condition, possible illegal connections, customer complaints, leaks

Office Analysis: By passing the collected data through the billing database (CMS - Customer Management System)

Results

Improvement in Meter Reading efficiency from the initial 54% to above 90% for the completed areas.

Billing efficiency improved from 40% to 89% in the 2011 -2012 financial year.

Non-Revenue Water reduction in the region associated to:

  • Direct Connection since inception of the company (with NRW implications) - 1500 unmetered premises were identified and metered
  • 3000 unmatched meters were added to the billing database
  • A total of 8000 accounts were resolved during the exercise moving revenue collection from 86 million to over 105 Million per month

Sustainability

The geo-referencing of meters and premises allows effortless monitoring and accurate analysis of meters. The basic problem of NRW in the company has been identified as lack of data accuracy and lack of analysis e.g. of meter condition, connection points and consumption figures.

The implementation of GIS is highly dependent on the accuracy of the available data. The data collected from the exercise is at 95% accurate and forms the basis for GIS implementation. There is also continuous capacity building for the GIS support staff for sustenance.

Due to the non-revenue water reduction, the increase revenue automatically funds its continuity. The company database is consistent with the information from the field and links to the GIS database. This enables easy monitoring and analysis of consumption and collection based on physical buildings.

WAVE Impact

The need for the current approach was identified through action plans developed by the customer care staff who attended the WAVEplus-funded courses on Customer and Commercial Orientation (CCO), who upon return consistently raised the need for data cleanup and authentication of customer data.