Quality assurance is a continuous and well-documented process.  At Olameter we add an extra dimension through the use of AI techniques and Big Data analytics.

All employees are audited annually in conformance with the ISO 9001:2015 quality management system.  ISO 9001:2015 sets out the criteria for Olameter’s internal auditors, as well as verification of their work by third-party inspectors.  The standard is based on a number of quality management principles including:

  • A strong customer focus
  • The motivation and implication of top management
  • A process approach
  • A program for continuous improvement

Olameter’s workforce management systems are designed to ensure that the prescribed workflow has been properly completed.  The technician must indicate compliance at each step. 

The Next Generation of QA

An increasing number of Olameter’s workforce management tools have been integrated with artificial intelligence-based analytical systems.   These provide independent corroboration that the work on the day was done correctly.  When embedded in the edge devices (handhelds, etc.) they do so even before the technician leaves the site.

  • For instance, a technician installing a new meter must enter the readings appearing on the old meter, as well as the new.  The technician must also take pictures of both meters.  Olameter’s workforce management system computes the readings from the photos.  The system then ensures that the values entered by the technician match those derived from these photos – all before the technician leaves the site.
  • Similarly, when an underground locate technician marks a site, Olameter’s workforce management system uses the photos, GPS data, azimuth readings (pitch, roll and yaw), as well as inertial sensor data to dynamically track the work done by the technician and ensure that it was properly completed.  Coupled with public survey data, these analytics also serve to produce accurate mapping of the utility’s “as-built” assets.

Finally, using additional AI techniques, all evidence gathered at the site is compared with other sources of data such as:

  • Previous and subsequent field surveys carried out at the same site, possibly for different clients  
  • Big Data (private and public) such as client drawings and schematics, land surveys, aerial photos or street view imagery.  Changes from past observations or deviations with respect to previous assessments are automatically highlighted.