  Prescriptive business process monitoring provides decision support to process
managers on when and how to adapt an ongoing business process to prevent or
mitigate an undesired process outcome. We focus on the problem of automatically
reconciling the trade-off between prediction accuracy and prediction earliness
in determining when to adapt. Adaptations should happen sufficiently early to
provide enough lead time for the adaptation to become effective. However,
earlier predictions are typically less accurate than later predictions. This
means that acting on less accurate predictions may lead to unnecessary
adaptations or missed adaptations.
  Different approaches were presented in the literature to reconcile the
trade-off between prediction accuracy and earliness. So far, these approaches
were compared with different baselines, and evaluated using different data sets
or even confidential data sets. This limits the comparability and replicability
of the approaches and makes it difficult to choose a concrete approach in
practice.
  We perform a comparative evaluation of the main alternative approaches for
reconciling the trade-off between prediction accuracy and earliness. Using four
public real-world event log data sets and two types of prediction models, we
assess and compare the cost savings of these approaches. The experimental
results indicate which criteria affect the effectiveness of an approach and
help us state initial recommendations for the selection of a concrete approach
in practice.
