Advancing Supply Chain Management through Artificial Intelligence: Conceptual studies

Abstract

Abstract

This article investigates how artificial intelligence (AI) is reshaping supply chain management (SCM) through concrete and operational applications. AI enhances the efficiency of data collection and analysis while significantly strengthening decision-making processes and advanced modeling capabilities (Ivanov & Dolgui, 2020). Techniques such as machine learning and Big Data analytics provide robust and scalable solutions to complex supply chain challenges, including demand forecasting, inventory management, and logistics flow optimization (Chopra & Meindl, 2020; Kamble, Gunasekaran & Gawankar, 2020). The integration of these technologies also contributes to improving supply chain resilience in the face of increasing disruptions and uncertainty (Christopher & Peck, 2004). Moreover, AI enhances end-to-end supply chain visibility through the use of natural language processing (NLP) techniques and intelligent agents, enabling more proactive and informed managerial decisions (Mikalef et al., 2020). Nevertheless, several challenges persist, particularly concerning algorithmic transparency, data governance frameworks, and ethical implications related to AI deployment (LeCun, Bengio & Hinton, 2015). The article concludes by proposing strategic perspectives for the effective adoption of AI in SCM, while emphasizing the critical challenges associated with its operational implementation.

Keywords: Artificial intelligence (AI); Supply chain management (SCM); Big Data analytics; Machine learning; Supply chain optimization; Supply chain resilience.