The use of IoT sensor data to dynamically assess maintenance risk in service contracts

Abstract

We explore the value of using operational sensor data to improve the risk assessment of service contracts that cover all maintenance-related costs during a fixed period. An initial estimate of the contract risk is determined by predicting the maintenance costs via a gradient-boosting machine based on the machine’s and contract’s characteristics observable at the onset of the contract period. We then periodically update this risk assessment based on operational sensor data observed throughout the contract period. These sensor data reveal operational machine usage that drives the maintenance risk. We validate our approach on a portfolio of about 4,000 full-service contracts of industrial equipment and show how dynamic sensor data improves risk differentiation.