Remarketing
AI-powered inspection gantries examine cars for damage by capturing 200 to 300 photos as the vehicles passes through before more than 25 algorithms are employed to identify various types of damage.
AI of the beholder
Remarketing firms are increasingly looking to artificial intelligence to appraise used vehicles. Jack Carfrae asks how it works.
P
lease forgive us for writing about the most notorious buzzword of our times, but please also trust that we are doing so because this corner of the industry is experimenting with and applying artificial intelligence in ways that could change how certain parts of it operate.
Remarketing companies have started to get serious about AI and its application to vehicle appraisals, broadly to make the process speedier, more thorough, and to remove the earliest and the most remedial aspects of inspection from human assessors.
In January, auction firm Aston Barclay announced what it claimed was a UK remarketing first with the rollout of AI-powered inspection gantries at its Wakefield and Donington Park sites. The gantries were supplied by Proovstation –
28 | April 2024 |
www.businesscar.co.uk
which says it operates more than 200 stations in 12 countries – and take 2-300 photos of a vehicle as it drives through. They entered service after a six-month trial and are said to apply more than 25 algorithms to identify different types of observable damage, such as chips, dents, scratches, and tyre wear down to millimetres.
Data is allegedly collected in 90 seconds and transmitted to a human vehicle appraiser’s mobile device. It is then transferred to the firm’s damage appraisal software, which calculates repair costs and assigns a National Association of Motor Auctions (NAMA) grade – the industry-standard one-to-five vehicle condition metric.
The company says it is primarily using the gantries to carry out the first stage of inspections, so human assessors – who remain part of the process – already have
an idea of what is wrong with the vehicle before they inspect it.
“Where before, an inspector would have gone up to a car completely fresh and done a full appraisal, this… has done a lot of that first detection for us,” says Aston Barclay’s head of change delivery, Mark Wilson, “it shows areas of potential damage that we just need to validate rather than do a fresh inspection.”
Wilson says the firm does not plan to reduce its headcount by automating part of the appraisal process but hopes to use AI as an efficiency tool to avoid hiring more staff as the business grows. “What we’d like to do is grow our volume but not necessarily recruit more people to meet that demand,” he explains, “historically, what everybody has always done is throw people at the problem – get more cars, get more people. We want to
see if we can really leverage and utilise emerging technologies such as this to make sure we have the right people in the right places, but we don’t have an oversized headcount.”
He says that the firm has used the tech to get ahead with tactical refurbishments, identifying defects early on and offering to carry out limited repairs so vehicles meet a more marketable NAMA grade – say, a smart repair to bump it from a grade four to a grade three – and adds that the gantries’ primary purpose has been to improve consistency.
“There’s always subjectivity when a human looks at a car and then another human looks at the same car. What this does is deliver consistency. It’s the same machine, in the same temperament and the same environment, the same lighting, so the quality control validation is
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53
