Asian Journal of Research in Computer Science

This systematic review investigates the impact of Generative Artificial Intelligence (GenAI) on supply chain management (SCM). Though there is an acknowledgment of GenAI’s potential in SCM, there remains a lack of research that thoroughly examines the opportunities, tangible advantages, and challenges associated with GenAI adoption in SCM. This literature review seeks to analyze the role of GenAI in SCM and fulfill three primary objectives. First, it identifies the key opportunities for GenAI implementation in SCM, encompassing its applications in demand forecasting, inventory control, and decision-making support systems. Second, it assesses the tangible benefits observed from current implementations, such as cost savings and enhanced operational efficiency. Third, the study investigates the challenges and obstacles to effective GenAI adoption, focusing on technical limitations, data quality concerns, workforce skill deficiencies, and ethical issues. This research followed the systematic review methodology, reviewing and synthesizing information from 24 peer-reviewed articles published between 2023 to 2024. The findings reveal substantial potential for GenAI in SCM areas like demand forecasting, inventory optimization, and risk management while emphasizing significant data quality, integration, and organizational preparedness challenges. The study offers two practical implications: a) the findings provide actionable insights for supply chain professionals, highlighting how GenAI can be integrated into SCM; b) the research underscores the necessity for strong data governance, ethical AI usage policies, and adherence to evolving regulations.

Objective: This study aims to understand GenAI’s role in SCM, identify key opportunities for implementing GenAI in supply chain processes, analyze the tangible benefits reported in existing implementations, and examine the challenges and barriers to successful GenAI adoption.

Method: This review adhered to PRISMA guidelines, applying rigorous inclusion and exclusion criteria across multiple databases to yield 24 peer-reviewed articles from 2023–2024. Data extraction, quality assessment, and synthesis were conducted systematically to ensure unbiased insights into GenAI’s impact on supply chain management.

Results and Discussion: The results reveal that GenAI significantly enhances SCM processes such as demand forecasting, inventory optimization, and risk management, while also exposing challenges in data quality, system integration, and organizational readiness. The discussion contextualizes these findings within the theoretical framework, emphasizing practical implications and addressing study discrepancies and limitations.

Research Implications: This research expands the exploration of GenAI in SCM and encourages further investigation into emerging technologies to optimize supply chains. For the SCM industry, it highlights a strategic approach to GenAI adoption, emphasizing technology investments, workforce training, data integrity, and ethical practices to boost operational efficiency and resilience.

Originality/Value: This study contributes to the literature by examining the practical integration of GenAI into supply chain management, addressing underexplored technical, organizational, and ethical challenges. The relevance and value of this research are evidenced by its actionable insights, which can guide practitioners and policymakers in enhancing operational efficiency, innovation, and resilience in supply chains.