Experimental Designs for Multi-Item Multi-Period Inventory Control

Xinqi Chen, Xingyu Bai, Zeyu Zheng and Nian Si

Papers from arXiv.org

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, namely, 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, proving that switchback designs systematically underestimate, while item-level randomization systematically overestimate, the global treatment effect. Motivated by two-sided randomization, we propose a pairwise design over items and time and analyze its bias properties. Numerical experiments using real-world data validate our theory and provide concrete guidance for selecting experimental designs in practice.

Date: 2025-01, Revised 2026-01
New Economics Papers: this item is included in nep-exp
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