Paper Contents
Abstract
In the contemporary international trade environment, characterized by complex supply chains, inventory challenges, and unpredictable demand, data science emerges as a crucial foundation for decision-making in Supply Chain Management (SCM). This study explores the integration of data science into SCM, emphasizing the role of advanced technologies such as machine learning, predictive analytics, and big data in enhancing decision-making processes. Through a comprehensive literature review, this research aims to identify current trends and evaluate the impact of data science on SCM decisions. By employing a synthesis approach and conducting thematic analysis, the study will uncover key themes related to the challenges and benefits of leveraging data science in SCM. The findings will highlight how data science not only facilitates but also transforms decision-making, enabling more accurate forecasting, improved efficiency, and greater market readiness. Additionally, the paper investigates the contributions of the Internet of Things (IoT) and Industry 4.0 technologies to SCM, focusing on their roles in boosting operational efficiency and sustainability. This research underscores the transformative potential of data science in reshaping SCM practices and strategies in the face of evolving global trade dynamics.
Copyright
Copyright © 2024 N Thanuja. This is an open access article distributed under the Creative Commons Attribution License.
