something he ’ s already identified increasingly in manufacturing .
“ When manufacturers speak to us , they ask if they can use machine learning to determine , along the way , if a batch is defective . They know we can detect things much earlier , so they can stop the process to minimise cost and loss .”
Does he have any specific examples of this in action ? “ We work with a large telecom provider in France and they had a real challenge with their set top box , which is not something you would assume requires heavy predictive maintenance . The problem they had was that every time one failed they had to go and identify the issue and replace it . Obviously , this is costly in terms of time and money . There ’ s also a huge risk of losing customers ,” he says .
“ We proactively identified that one third of their boxes had a problem which meant customer service could call and tell the people to update their box or send a replacement before a failure occurs .”
48 April 2018