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<p>Artificial intelligence (AI), machine learning (ML) and decision-support (DS) are gaining increasing interest with widening adoption. This article investigates the enabler role of AI and ML for providing decision-support in procurement&amp;purchasing domain. The study follows a systematic review approach via taxonomic analysis. Comprehensive analysis and discussions are provided for: (a) the relevance and applicability of AI and ML in procurement&amp;purchasing decision-support; (b) functionalities/processes for which they are utilized; (c) related methodologies; and (d) implementation benefits as well as challenges. Findings reveal that procurement&amp;purchasing area holds significant potential in terms of AI-ML applications for decision-support almost every related sub-process. This study is original by offering a process-oriented approach to the research domain; providing unique clustering and classification; and presenting detailed analyses via unique taxonomy tables with respect to approach, topic, focus, context and methodologies of the literature items reviewed. The study offers further research opportunities and has significant potential to provide managerial insights by the identified sectoral applications, benefits and challenges.</p>

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Artificial intelligence (AI) and machine learning (ML) in procurement and purchasing decision-support (DS): a taxonomic literature review and research opportunities

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月20日

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  • Dursun Balkan,
  • Goknur Arzu Akyuz

摘要

Artificial intelligence (AI), machine learning (ML) and decision-support (DS) are gaining increasing interest with widening adoption. This article investigates the enabler role of AI and ML for providing decision-support in procurement&purchasing domain. The study follows a systematic review approach via taxonomic analysis. Comprehensive analysis and discussions are provided for: (a) the relevance and applicability of AI and ML in procurement&purchasing decision-support; (b) functionalities/processes for which they are utilized; (c) related methodologies; and (d) implementation benefits as well as challenges. Findings reveal that procurement&purchasing area holds significant potential in terms of AI-ML applications for decision-support almost every related sub-process. This study is original by offering a process-oriented approach to the research domain; providing unique clustering and classification; and presenting detailed analyses via unique taxonomy tables with respect to approach, topic, focus, context and methodologies of the literature items reviewed. The study offers further research opportunities and has significant potential to provide managerial insights by the identified sectoral applications, benefits and challenges.