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We aim to predict and explain service failures in supply-chain networks, moreprecisely among last-mile pickup and delivery services to customers. We analyzea dataset of 500,000 services using (1) supervised classification with RandomForests, and (2) Association Rules. Our classifier reaches an averagesensitivity of 0.7 and an average specificity of 0.7 for the 5 studied types offailure. Association Rules reassert the importance of confirmation calls toprevent failures due to customers not at home, show the importance of the timewindow size, slack time, and geographical location of the customer for theother failure types, and highlight the effect of the retailer company onseveral failure types. To reduce the occurrence of service failures, our datamodels could be coupled to optimizers, or used to define counter-measures to betaken by human dispatchers.

Data models for service failure prediction in supply-chain networks