  Many sectors nowadays require accurate and coherent predictions across their
organization to effectively operate. Otherwise, decision-makers would be
planning using disparate views of the future, resulting in inconsistent
decisions across their sectors. To secure coherency across hierarchies, recent
research has put forward hierarchical learning, a coherency-informed
hierarchical regressor leveraging the power of machine learning thanks to a
custom loss function founded on optimal reconciliation methods. While promising
potentials were outlined, results exhibited discordant performances in which
coherency information only improved hierarchical forecasts in one setting. This
work proposes to tackle these obstacles by investigating custom neural network
designs inspired by the topological structures of hierarchies. Results unveil
that, in a data-limited setting, structural models with fewer connections
perform overall best and demonstrate the coherency information value for both
accuracy and coherency forecasting performances, provided individual forecasts
were generated within reasonable accuracy limits. Overall, this work expands
and improves hierarchical learning methods thanks to a structurally-scaled
learning mechanism extension coupled with tailored network designs, producing a
resourceful, data-efficient, and information-rich learning process.
