<![CDATA[ The efficient management of the demand and supply of blood is a challenging problem to solve, mainly because of the nature of the blood supply chain, as the blood is a perishable item with strict storage conditions, while the demand is highly uncertain. Thus, an inefficient prediction of the demand may result in either a scarcity of blood, compromising the safety of the patients, or a surplus of blood, thereby increasing the waste. This review discusses the application of predictive analytics to solve the blood supply chain problem by combining the recent advancements of statistical prediction, optimization, machine learning, and deep learning. It discusses the various theoretical foundations of the problem, such as the basics of the blood supply chain, the concept of uncertainty, the theory of inventory management, and the prediction methodologies, while focusing on the significance of the application of predictive analytics to solve the problem by improving the accuracy of the prediction, the efficiency of the inventory management, and the quality of the decisions made. A comparative study of the various prediction methodologies reveals the evolution of the prediction from the traditional statistical prediction to machine learning and deep learning, often combined with optimization to solve the resource allocation problem. Although the prediction problem is solved to a large extent, there are still many challenges to be overcome with respect to the heterogeneity of the data, the interpretability of the results, the privacy of the data, and the infrastructure required to implement the prediction system. ]]>
