Interpretable Reinforcement Learning via Neural Additive Models for Inventory Management

TL;DR

This paper introduces an interpretable reinforcement learning method using Neural Additive Models for dynamic inventory management in supply chains, balancing interpretability with flexibility and demonstrating competitive performance.

Contribution

It presents a novel RL approach with Neural Additive Models that is both interpretable and adaptable for multi-echelon inventory policies, unlike traditional static methods.

Findings

Neural Additive Models provide competitive performance with standard policies.

The approach offers interpretability for complex supply chain strategies.

Insights into inventory ordering strategies are gained through model interpretability.

Abstract

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.