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This research views data science as the basis of the decision-making process at SCM. Tough international trade environment characterized by complex supply chain and inventory issues as well as unpredictable demand for goods necessitates powerful analytics tools. Using the latest technologies - machine learning, predictive analytics, and big data - data science generates data-driven decisions for more accurate, efficient, and prompt SCM decision-making. The study intends to study the current trends and evaluate the influence of data science in SCM decision-making processes. It also delves into the difficulties and advantages with the utilization of data science during these procedures. This study uses a synthesis approach by systematically going through a literature review to gather data from different academic journals and industry publications. According to the results of the thematic analysis, the themes will emerge, so the whole complexity and depth of data science applications in SCM will be properly revealed. Data science changes the business decision-making in a way that was impossible before with the advent of new information from the huge and complex data sources. Data analytics not only smoothens but also upgrades long-term trend forecasting and market readiness in SCM. Furthermore, the paper emphasizes the influence of the Internet of Things (IoT) and industry 4.0 technologies of SCM with an accent on how they are associated to increase efficiency and sustainability in the operations.
Zean Gong (Wed,) studied this question.