JOURNAL ARTICLE

Direct Trade Sourcing Strategies for Specialty Coffee.

  • Published In: Manufacturing & Service Operations Management (M&SOM) (INFORMS), 2024, v. 26, n. 5. P. 1712 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Webster, Scott; Kazaz, Burak; Gheibi, Shahryar 3 of 3

Abstract

This article analyzes how specialty coffee roasters optimize their direct trade (DT) sourcing strategies for single-origin (SL) coffee beans under uncertainties in harvest yield and blend-label (BL) coffee prices. It identifies three distinct optimal sourcing strategies—specialized (no downward substitution of SL beans into blends), diversified (consistent downward substitution), and mixed (intermediate use)—and characterizes the conditions under which each strategy is preferred, primarily influenced by the mean BL price and the marginal cost of SL beans. The study introduces the concept of the "farmer's curse," where negative correlation between yield and price lowers the weighted average price received by growers, suggesting benefits from fixed pricing set before harvest to mitigate this effect. Additionally, it highlights a virtuous feedback loop in DT relationships, where roasters’ investments to reduce yield volatility strengthen grower partnerships and potentially improve grower welfare. The findings are robust across alternative demand models and have implications beyond coffee, applicable to other agricultural and quality-differentiated supply chains.

Additional Information

  • Source:Manufacturing & Service Operations Management (M&SOM) (INFORMS). 2024/09, Vol. 26, Issue 5, p1712
  • Document Type:Article
  • Subject Area:Nutrition and Dietetics
  • Publication Date:2024
  • ISSN:1523-4614
  • DOI:10.1287/msom.2021.0586
  • Accession Number:179561465
  • Copyright Statement:Copyright of Manufacturing & Service Operations Management (M&SOM) (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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