Supply Chain Management Intelligence: Leveraging AI and ML for Enhanced Marketing Strategies and Decision Making

Abstract

Supply chain management (SCM) has evolved into a complex and dynamic ecosystem, where efficient decision-making is paramount for success. This review paper explores the integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques in SCM to optimize marketing strategies and decision-making processes.

The paper begins by elucidating the fundamental concepts of SCM and the traditional challenges encountered in decision-making within this domain. It then delves into the transformative potential of AI and ML technologies in addressing these challenges, offering insights into how these technologies can enhance the efficiency, accuracy, and agility of SCM processes.

Furthermore, the paper  highlights various AI and ML applications across different stages of the supply chain, including demand forecasting, inventory management, logistics optimization, and customer relationship management. It examines the role of predictive analytics, prescriptive analytics, and cognitive computing in enabling data-driven decision-making and proactive strategy formulation.

Moreover, the paper discusses the implications of AI and ML adoption on organizational structures, workforce skills, and strategic partnerships within the SCM landscape. It emphasizes the need for a holistic approach that combines technological advancements with human expertise to maximize the benefits of intelligent SCM systems.

Lastly, the paper identifies current trends, challenges, and future research directions in the field of SCM intelligence, emphasizing the importance of continuous innovation and adaptation in the face of evolving market dynamics and technological advancements.

This paper underscores the transformative potential of AI and ML in revolutionizing SCM practices, enabling organizations to achieve greater efficiency, competitiveness, and sustainability in their marketing strategies and decision-making processes.