JOURNAL ARTICLE

OM Forum—Barriers to Implementing Diversity, Equity, and Inclusion (DEI) Programs in Supply Chains: Lessons from Comparing Public and Private Firms.

  • Published In: Manufacturing & Service Operations Management (M&SOM) (INFORMS), 2025, v. 27, n. 2. P. 339 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Shalpegin, Timofey; Browning, Tyson R.; Kumar, Ajay 3 of 3

Abstract

This article examines the barriers to implementing diversity, equity, and inclusion (DEI) programs in supply chains by comparing the DEI performance of publicly traded (public) and privately held (private) companies. It identifies transparency, access to resources, and the ability to customize DEI initiatives as key factors contributing to public companies' relatively better DEI outcomes, while private companies face challenges such as limited external accountability, smaller budgets, and less tailored programs. Additional barriers include skepticism about DEI benefits, internal opposition from advantaged groups, and differences in global operations. The article highlights research opportunities to support private firms in overcoming these obstacles through empirical studies, incentive alignment, organizational culture development, and cost-effective DEI strategies, emphasizing the need for cross-disciplinary approaches to advance DEI in supply chain management.

Additional Information

  • Source:Manufacturing & Service Operations Management (M&SOM) (INFORMS). 2025/03, Vol. 27, Issue 2, p339
  • Document Type:Article
  • Subject Area:Politics and Government
  • Publication Date:2025
  • ISSN:1523-4614
  • DOI:10.1287/msom.2023.0621
  • Accession Number:184090840
  • 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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