Outbound Modeling for Inventory Management

TL;DR

This paper develops a probabilistic model for forecasting outbound inventory drain and shipping costs, crucial for inventory planning and reinforcement learning, by addressing the challenges of differentiability, robustness, and counterfactual evaluation.

Contribution

It introduces a differentiable probabilistic forecasting approach for inventory drain and costs, enabling better RL integration and robustness to out-of-distribution scenarios.

Findings

Model accurately predicts in-distribution drain and costs.

The approach enhances RL policy evaluation with counterfactual scenarios.

Preliminary results show promising accuracy and robustness.

Abstract

We study the problem of forecasting the number of units fulfilled (or ``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 customers' orders fulfillment, i.e. from where and how to ship a unit to be delivered to a customer. 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 we desire to be differentiable. While simulating the calls to the internal software 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.