Data models for service failure prediction in supply-chain networks

Abstract--We aim to predict and explain service failures in supply-chain networks, more precisely among last-mile pickup and delivery services to customers. We analyze a dataset of 500,000 services using (1) supervised classification with Random Forests, and (2) Association Rules. Our classifier reaches an average sensitivity of 0.7 and an average specificity of 0.7 for the 5 studied types of failure. Association Rules reassert the importance of confirmation calls to prevent failures due to customers not at home, show the importance of the time window size, slack time, and geographical location of the customer for the other failure types, and highlight the effect of the retailer company on several failure types. To reduce the occurrence of service failures, our data models could be coupled to optimizers, or used to define countermeasures to be taken by human dispatchers. Service failures are pervasive in supply-chain networks, with important consequences on their cost-efficiency and customer experience. We aim at predicting and explaining the cause of such failures, focusing on the last-mile pickup and delivery of items at customer locations. Such services are planned by optimizers solving some variations of the Vehicle-Routing Problem, in our case the Pickup and Delivery Problem with Time Windows (PDPTW [1]).