  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 counter-measures to be
taken by human dispatchers.
