JOURNAL ARTICLE

Too Good To Go: Combating Food Waste with Surprise Clearance.

  • Published In: Management Science (INFORMS), 2026, v. 72, n. 3. P. 2307 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Yang, Luyi; Yu, Man 3 of 3

Abstract

This article analyzes surprise clearance (SC), a business model where stores sell "surprise bags" containing an uncertain quantity of surplus perishable food to reduce food waste and increase profits. It compares SC with transparent clearance (TC), which sells surplus food at a known discounted unit price, and no clearance (NC). The study finds that SC yields the highest store profit and production quantity, completely eliminates store waste by selling all leftover inventory, but generates the most consumer waste due to increased consumption uncertainty. While both clearance schemes can reduce total food waste relative to no clearance when targeting consumers with low valuation for the product, they may increase total waste under certain conditions, especially when production costs are high. Extensions of the model show SC’s robustness in various settings, including multiple product types and cannibalization threats, but also highlight practical challenges such as consumer inconvenience and the potential for increased consumer-level waste. The findings emphasize the importance of considering both store and consumer waste in evaluating food waste interventions and suggest that SC can be a "win-win-win" solution under specific market conditions but may not universally reduce total food waste.

Additional Information

  • Source:Management Science (INFORMS). 2026/03, Vol. 72, Issue 3, p2307
  • Document Type:Article
  • Subject Area:Nutrition and Dietetics
  • Publication Date:2026
  • ISSN:0025-1909
  • DOI:10.1287/mnsc.2023.03001
  • Accession Number:192085225
  • Copyright Statement:Copyright of Management Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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