  The COVID-19 pandemic has highlighted the importance of supply chains and the
role of digital management to react to dynamic changes in the environment. In
this work, we focus on developing dynamic inventory ordering policies for a
multi-echelon, i.e. multi-stage, supply chain. Traditional inventory
optimization methods aim to determine a static reordering policy. Thus, these
policies are not able to adjust to dynamic changes such as those observed
during the COVID-19 crisis. On the other hand, conventional strategies offer
the advantage of being interpretable, which is a crucial feature for supply
chain managers in order to communicate decisions to their stakeholders. To
address this limitation, we propose an interpretable reinforcement learning
approach that aims to be as interpretable as the traditional static policies
while being as flexible and environment-agnostic as other deep learning-based
reinforcement learning solutions. We propose to use Neural Additive Models as
an interpretable dynamic policy of a reinforcement learning agent, showing that
this approach is competitive with a standard full connected policy. Finally, we
use the interpretability property to gain insights into a complex ordering
strategy for a simple, linear three-echelon inventory supply chain.
