Download This PaperWhen to Broadcast? Inventory Disclosure Policies for Online Sales of Limited Inventory
Information Systems Research, 0[10.1287/isre.2023.0746]
35 Pages Posted: 27 May 2020 Last revised: 25 May 2025
Date Written: May 25, 2025
Abstract
Online sales of limited inventory such as lightning and flash deals have become popular among e-commerce platforms including Amazon and eBay. Motivated by empirical studies of the impact of inventory information on demand in flash sales (Cui et al. 2019 and Calvo et al. 2020), we study the platform’s best timing of disclosing inventory information to maximize the expected sales in a finite horizon. We analyze the following common policies in practice: “always disclose,” “never disclose,” and the fixed threshold policy. The fixed threshold policy requires the platform to broadcast once the inventory level drops below a preset level. We also propose a time-dependent threshold policy, which requires a comparison between the current time and a time-threshold associated with the current inventory level, and show that the proposed policy is the optimal policy under certain assumptions that are consistent with the empirical findings of the extant literature. For both threshold policies, we devise efficient algorithms to optimize the policy parameters, and we compare them through a numerical study. We find that both threshold policies can significantly outperform the two simple policies of always or never disclose. In particular, when the observational learning (herding) effect dominates the scarcity effect on demand, the fixed threshold policy is near optimal. However, when the scarcity effect dominates, employment of a fixed threshold policy may backfire as the customers may interpret not disclosing as a signal of slow sales. Furthermore, when both effects are not profound, the proposed time-dependent threshold policy can significantly improve the fixed threshold policy, so the platform should employ the proposed policy instead of the fixed-threshold policy. Therefore, our study provides not only effective and efficient algorithms for policy optimization but also guidelines for policy selection.
Keywords: Inventory Management, Information Disclosure, Lightning and Flash Deals, Dynamic Programming, Stochastic Models, Optimization
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