Automate as-built Processing - Its About Time!

Processing a completed as-built into a GIS and AMS system is a huge hidden cost for most councils. Depending on complexity, this can take days or weeks to complete as they have to manually enter the data into their GIS and AMS systems one asset at a time. Most councils already have the ability to bulk-process their as-built data at one time. However, the issue with using those existing bulk-processing facilities lies in the asset manager’s lack of trust in the quality of the data received coupled with the fact it has to be in the correct file format to allow bulk-processing.

This paper will outline the strategies and principles to be undertaken by council and other as-built receiving organisations in order to update their internal as-built submittal processes and begin considering bulk-processing as-built documents. The heart of the solution relies on the as-built submitter submitting as-built documents that the asset managers has confidence that the data is ready for bulk-processing. This paper will discuss how Watercare Services Limited and Tauranga City Council have adopted a software solution provided by blackbox22 that integrates with the individual as-built submitter to insure the data submitted by them is of high quality and formatted correctly. When receiving as-built data generated using this system both organisations have the confidence that the as-built data received is of high quality and is in the right file type for their individual GIS and AMS systems thus allowing bulk-processing of the as-built at one time.

The direct result of using blackbox22 has brought the man-hours needed to completely process an as-built submittal down to only a couple of hours or less. By using the blackbox22 system to ensure the receipt of high quality data, both Watercare Services Ltd and the Tauranga City Council were able to realise substantial efficiencies and reduction of effort in processing as-builts. By adapting to this system both organisations dramatically reduced total processing time, increased data quality all the while minimising costs by better utilizing their existing infrastructure.