Machine learning and pattern matching
The are potential option with the approach that are positive.
Remember the garages that do MOT’s all sit somewhere on the Goldilocks scale, as do the punters that take their cars in and the cars themselves.
Daddy bear garages will fail more cars, but that doesn’t mean to say they are better at an MOT than anyone else, quite frankly they may be ripping off their customers.
Baby bear garages will pass nearly everything, that doesn’t mean they are allowing unsafe cars on the road, it may be that the cars they examine are maintained better during their service cycle.
Where cars move between garages you will begin to see patterns, looking at the difference between the number and type of “advisories” you will see patterns, How you scale these patterns remains to be seen, but if somebody could dip into vehicle service details (even the tiny garages now use online recording systems, which also help them with billing and accounts) you may suddenly be onto a really good thing.
Obviously this won’t all happen on day one. Worryingly somebody may decide they are happy with a partial view and accept the road accident because of faulty vehicle statistics as being cost effective, but we can but hope that the approach provides better visibility than the existing MOT of MOT stations that seems to have its own Goldilocks problems.
Biting the hand that feeds IT
