Outbound Modeling for Inventory Management

Riccardo Savorgnan, Udaya Ghai, Carson Eisenach, Dean Foster

Keywords: Regional Inventory Management

TL;DR: We propose a practical framework to emulate and evaluate out-of-distribution inventory drain for an online retailer with multiple warehouses, with the objective of using the emulation for learning an inventory placement policy.

Abstract: We study the problem of forecasting the number of units drained from each inventory warehouse to meet customer demand, along with the associated outbound shipping costs. The actual drain and shipping costs are determined by complex production systems that manage the planning and execution of customer orders fulfillment. Accurately modeling these processes is critical for regional inventory planning, especially when using Reinforcement Learning (RL) to develop control policies. For the RL usecase, a drain model is incorporated into a simulator to produce long rollouts, which may need to be differentiable. While production systems can be used to recover this transition, they are non-differentiable and too slow and costly to run within an RL training environment. Accordingly, we frame this as a probabilistic forecasting problem, modeling the joint distribution of outbound drain and shipping costs across all warehouses at each time period, conditioned on inventory positions and exogenous customer demand. To ensure robustness in an RL environment, the model must handle out-of-distribution scenarios that arise from off-policy trajectories. We propose a validation scheme that leverages production systems to evaluate the drain model on counterfactual inventory states induced by RL policies. Preliminary results demonstrate the model's accuracy within the in-distribution setting.

Submission Number: 3