PurposeThe purpose of this paper is to review and integrate the extensive literature base which examines judgment and decision‐making biases, to introduce this literature to the field of supply management, to create a valid, mutually exclusive, and exhaustive taxonomy of decision biases that can affect supply managers, and to provide guidance for future research and applications of this taxonomy.Design/methodology/approachThe authors use a qualitative cluster analysis, combined with a Q‐sort methodology, to develop a taxonomy of decision biases.FindingsA mutually exclusive, and exhaustive taxonomy of nine decision biases is developed through a qualitative cluster analysis. The Q‐sort methodology provides initial confirmation of the reliability and validity of the cluster analysis results. The findings, along with numerous examples provided in the text, suggest that supply management decisions are vulnerable to the described biases.Originality/valueThis paper provides a comprehensive review of the judgment and decision bias literature, and creates a logical and manageable taxonomy of biases which can impact supply management decision making. The introduction and organization of this vast extant literature base provides a contrasting perspective to much of the existing supply management research, which has incorporated the assumption of the rational agent, or what is known in the economics literature as homo economicus. In addition, the authors describe the use of qualitative cluster analysis and the Q‐sort methodology, techniques which have been used rarely if at all in within the field of supply chain management.

Human judgment and decision making under uncertainty are vulnerable to decision biases leading to deviations from the standard assumptions of the rational paradigm in economics. This fact is currently not widely reflected by research on decision making in sourcing contexts. However, supply managers are aware of the judgment and decision challenges that result from existing and increasing levels of uncertainty in the external, upstream supply chain, and deploy decision supporting strategies for debiasing their judgments. The analysis of supply management decisions using 441 data units from 133 embedded cases from 15 buying organizations revealed high levels of such debiasing strategies. However, the seemingly most effective mitigation strategy recommended in the general debiasing literature ‐ creating awareness of the underlying mechanics causing decision biases ‐ was only employed by one buying organization, indicating a need to further investigate debiasing strategies specifically in supply management contexts.