Models for risk management in the procurement process within organizational contexts: A systematic literature review

Abstract

Over the past decade, supply chain management has faced extreme volatility, culminating in the unprecedented disruptions of the COVID-19 pandemic. Consequently, resilience in procurement has transitioned from a secondary attribute to an indispensable requirement for operational continuity. This study classifies the state of the art in procurement risk management within the post-pandemic landscape (2021–2025). It analyzes the transition from reactive approaches toward predictive and prescriptive perspectives to address supply chain volatility. A Systematic Literature Review (SLR) was conducted following the PRISMA 2020 protocol. Based on a structured search in the Scopus and Web of Science databases, an initial pool of 1,109 records was screened. This resulted in a final sample of 107 high-impact peer-reviewed articles. These studies were evaluated using thematic content analysis and classified into ten technical categories based on their functional orientation. The evidence reveals a fragmented methodological ecosystem where Hybrid Models (21.5%) and Multi-Criteria Decision Making (MCDM, 16.8%) techniques predominate. A shift is observed from static approaches toward the integration of robust optimization, tail risk metrics like Conditional Value at Risk, and Machine Learning. However, a gap is identified between algorithmic complexity and corporate adoption. The study establishes that Explainable Artificial Intelligence (XAI) and Blockchain are indispensable governance requirements to overcome the black box problem in strategic decision-making. This research contributes a functional classification of tools based on their diagnostic, predictive, or prescriptive orientation. Furthermore, it provides managers with a tiered roadmap to achieve analytical maturity. This guides organizations from foundational visibility using synthetic indices, through fuzzy multi-criteria evaluations, toward the responsible implementation of stochastic programming and AI.