Joline Uichanco

This paper studies inventory commingling in third-party (3P) marketplaces, a fulfillment practice where identical items from different sellers are pooled and shipped interchangeably. While commingling improves efficiency for both the platform and the sellers, it creates quality risks when sellers differ in product quality and this information is privately held. We develop a game-theoretic model of a platform that sets its fulfillment fee while sellers, differentiated by quality, decide whether to participate in commingling. We characterize the resulting equilibrium outcomes and uncover a form of market stratification: lower-quality sellers either avoid the platform entirely or join only by commingling, whereas higher-quality sellers always remain but in some cases may opt out of commingling to shield their customers from quality spillovers. We show that commingling consistently improves the profits of the platform and lower-quality sellers. However, it may hurt higher-quality sellers, even if they opt out: not through quality risk, but via the platform’s endogenous increase of the fulfillment fee. Importantly, we identify market conditions where commingling improves outcomes for all parties. Our paper provides platform managers with a framework to evaluate \textitwhen and \textithow to offer inventory commingling. By pinpointing market conditions where commingling boosts profits, expands seller participation, or threatens product quality, we offer actionable guidance for aligning fulfillment design with strategic objectives.

We study a multilocation newsvendor network when the only information available on the joint distribution of demands are the values of its mean vector and covariance matrix. We adopt a distributionally robust model to find inventory levels that minimize the worst-case expected cost among the distributions consistent with this information. This problem is NP-hard. We find a closed-form tight bound on the expected cost when there are only two locations. This bound is tight under a family of joint demand distributions with six support points. For the general case, we develop a computationally tractable upper bound on the worst-case expected cost if the costs of fulfilling demands have a nested structure. This upper bound is the optimal value of a semidefinite program whose dimensions are polynomial in the number of locations. We propose an algorithm that can approximate general fulfillment cost structures by nested structures, yielding a computationally tractable heuristic for distributionally robust inventory optimization on general newsvendor networks. We conduct experiments on networks resembling U.S. e-commerce distribution networks to show the value of a distributionally robust approach over a stochastic approach that assumes an incorrect demand distribution.

Consider the newsvendor model, but under the assumption that the underlying demand distribution is not known as part of the input. Instead, the only information available is a random, independent sample drawn from the demand distribution. This paper analyzes the sample average approximation (SAA) approach for the data-driven newsvendor problem. We obtain a new analytical bound on the probability that the relative regret of the SAA solution exceeds a threshold. This bound is significantly tighter than existing bounds, and it matches the empirical accuracy of the SAA solution observed in extensive computational experiments. This bound reveals that the demand distribution’s weighted mean spread affects the accuracy of the SAA heuristic.