Qflow features in BBC building waste report

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The big problem of building waste and how to tackle it

By Emma Woollacott (Technology of Business reporter)

The construction industry produces a huge amount of waste - indeed, construction, demolition and excavation accounted for an astonishing 62% of the UK's total waste in 2018, according to the Department for Environment, Food and Rural Affairs.

Meanwhile, a study from 2013 suggested that 13% of materials delivered to a site go direct to waste without being used.And a large portion of construction waste is not recyclable.

It was the waste problem that inspired Brittany Harris and Jade Cohen to create Qualis Flow, which sells a product designed to better manage building materials.

Currently, says Ms Harris, the construction industry only has incomplete, inconsistent data covering the materials that are on the building site, and how they are being used or wasted.

Generally, firms rely on paper delivery notes and the collation of email receipts. This paper trail is then manually entered into a project reporting tool, such as a spreadsheet.

"Firstly, this manual translation of data between systems results in around 60% of it going missing or being inputted wrong. So, the data we have in these reports is next to useless," she says.

"Secondly, the industry is made up of hundreds of huge contracting organisations and thousands of smaller companies working together to deliver these complex projects.

Each of these companies tracks and reports on their material consumption and waste generation in different ways, into different systems."

While QR codes or RFID tags can be used to help track materials, they're by no means universal, meaning that any firm relying on them would be limited in the suppliers they could use.

Qflow, by contrast, allows all materials to be clocked-in and out, simply by using a mobile app to capture an image of the docket, with a web portal used to view and export the data.

"It can be deployed in an instant anywhere you have a smartphone, and can support all types of construction, from small commercial re-fits up to major rail infrastructure projects."

Machine learning algorithms then allow the data to be structured to identify ways of eliminating waste and saving time, money and carbon on site.

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