In today’s fiercely competitive business environment, meeting and surpassing earnings expectations is paramount for public companies. This study focuses on how companies selling newsvendor-type products determine the order quantity to maximize the probability of achieving a target profit (known as profitability). Decision-makers often face challenges in real-life situations where the true demand distributions are unknown, and they have to rely on historical demand data. In some cases, they may only have access to sales data, which is referred to as censored demand. We propose data-driven ordering policies that aim to maximize profitability based solely on historical demand data and sales data respectively. Specifically, we first develop a data-driven nonparametric model using historical demand data, and then present a mixed-integer programming to solve the model. In the case of censored demand, we further propose an enhanced data-driven nonparametric model that leverages the Kaplan–Meier estimator to correct sales data. We prove that the proposed data-driven ordering policies are asymptotically optimal and consistent, regardless of whether the demand is censored or not. To avoid overestimation of true profitability due to sampling error, we propose nonparametric bootstrap methods to estimate the lower confidence bound of profitability, providing a conservative estimate. We also demonstrate the consistency of the lower confidence bound of profitability obtained through the bootstrap-based numerical methods. Finally, we conduct numerical experiments using synthetic data to showcase the effectiveness of the proposed methods.

中文翻译：

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数据驱动的订购策略，适用于需求受审查的目标新闻供应商


在当今竞争激烈的商业环境中，达到并超越盈利预期对上市公司来说至关重要。本研究的重点是销售新闻供应商类型产品的公司如何确定订单数量，以最大限度地提高实现目标利润（称为盈利能力）的可能性。决策者在真实需求分布未知的现实生活中经常面临挑战，他们必须依赖历史需求数据。在某些情况下，他们可能只能访问销售数据，这称为删失需求。我们提出数据驱动的订购政策，旨在分别根据历史需求数据和销售数据最大限度地提高盈利能力。具体来说，我们首先使用历史需求数据开发一个数据驱动的非参数模型，然后提出一个混合整数规划来求解该模型。在需求删失的情况下，我们进一步提出了一个增强的数据驱动的非参数模型，该模型利用 Kaplan-Meier 估计器来校正销售数据。我们证明，无论需求是否被审查，所提出的数据驱动排序策略都是渐近最优和一致的。为了避免由于抽样误差而高估真实盈利能力，我们提出了非参数 bootstrap 方法来估计盈利能力的置信下限，从而提供保守的估计值。我们还证明了通过基于 bootstrap 的数值方法获得的盈利能力置信下限的一致性。最后，我们使用合成数据进行数值实验，以证明所提出的方法的有效性。