Experimental Designs for Multi-Item Multi-Period Inventory Control
arXiv:2501.11996
The paper studies how to run A/B tests in multi‑item, multi‑period inventory systems, showing that common designs like switchbacks and item‑level randomization can be biased, and proposes a pairwise randomization design to reduce this bias.
Abstract
Randomized experiments, or A/B testing, are the gold standard for evaluating interventions, yet they remain underutilized in inventory management. This study addresses this gap by analyzing A/B testing strategies in multi-item, multi-period inventory systems with lost sales and capacity constraints. We examine two canonical experimental designs--switchback experiments and item-level randomization--and show that both suffer from systematic bias due to interference: temporal carryover in switchbacks and cannibalization across items under capacity constraints. Under mild conditions, we characterize the direction of this bias in different scenarios. Motivated by two-sided randomization, we propose a pairwise design over items and time and analyze its bias properties. Controlled stochastic simulations verify the theoretical predictions, and trace-driven experiments on real-world fresh-retail data show that the same mechanisms persist in realistic environments with stockout substitution.
Accepted by the 27th ACM Conference on Economics and Computation (EC '26)