HYLA Mobile Blog Adding Automated Diagnostics to the Smartphone Grading Process

Assessing pre-owned smartphones manually is not easy nor efficient. First, there’s a problem of scale—massive amounts of trade-ins are conducted every year. Here at HYLA, we processed 3 million pre-owned phones in 2019, which amounts to over 10,000 phones per day. Hiring the number of workers needed to assess these phones purely by hand would be cost-prohibitive, especially since some of these phones aren’t able to return much value other than the cost of their recycled raw materials. In addition, there’s a subjectivity problem. No matter how much training is provided, a phone that looks badly damaged to one worker might look mildly damaged to another. Workers can make errors in judgement, get distracted, or slow down unpredictably. Meanwhile, we need to maintain extremely high throughput while also maintaining high accuracy. To solve these issues, we automated our operations. Using our automation and machine learning initiatives, we’ve been able to achieve throughput and accuracy while increasing both quality and efficiency which reduced our operating costs. Here’s a look at our approach.