Supply chain risk assessment: Content Analysis and Reinforcement Learning based literature review | ||||
| المجلة العربية للآداب والدراسات الإنسانية | ||||
| Volume 9, Issue 35, April 2025, Page 545-562 PDF (887.34 K) | ||||
| Document Type: المقالة الأصلية | ||||
| DOI: 10.21608/ajahs.2025.420395 | ||||
 View on SCiNiTO | ||||
| Author | ||||
| Salwa Ali Yousef Alshafai | ||||
| Abstract | ||||
| In recent years, with the rise of globalization, risk management has become an integral component of supply chain management. As a result, supply chain risk assessment has emerged as a critical and actively researched area. Significant debate exists regarding the most effective approaches and methods for assessing risk to support informed decision-making in supply chain risk management. This paper synthesizes the existing literature on supply chain risk assessment by systematically analysing 140 peer-reviewed articles from high-quality journals using content analysis. The study identifies and categorizes the diverse definitions, focal points, procedures, methods, and indicators employed in previous research. Through a comprehensive classification framework, this review provides insights into the field, highlights research gaps, and outlines directions for future studies. Risk management is one of the critical activities which needs to be done well to ensure supply chain activities operate smoothly. The first step in risk management is risk identification, in which the risk manager identifies the risk events of interest for further analysis. The timely identification of risk events in the risk identification step is crucial for the risk manager to be proactive in managing the supply chain risks in its operations. Undertaking this step manually, however, is tedious and time-consuming. With the increased sophistication and capability of advanced computing algorithms, various eminent supply chain researchers have called for the use of artificial intelligence techniques to increase efficiency and efficacy when performing their tasks. In this paper, we demonstrate how reinforcement learning, which is one of the recent artificial intelligence techniques, can assist risk managers to proactively identify the risks to their operations. | ||||
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