Smart Platform for Data Blood Bank Management: Forecasting Demand in Blood Supply Chain Using Machine Learning

Abstract

Despite the efforts of the World Health Organization, blood transfusions and delivery are still the crucial challenges in blood supply chain management, especially when there is a high demand and not enough blood inventory. Consequently, reducing uncertainty in blood demand, waste, and shortages has become a primary goal. In this paper, we propose a smart platform-oriented approach that will create a robust blood demand and supply chain able to achieve the goals of reducing uncertainty in blood demand by forecasting blood collection/demand, and reducing blood wastage and shortage by balancing blood collection and distribution based on an effective blood inventory management. We use machine learning and time series forecasting models to develop an AI/ML decision support system. It is an effective tool with three main modules that directly and indirectly impact all phases of the blood supply chain: (i) the blood demand forecasting module is designed to forecast blood demand; (ii) blood donor classification helps predict daily unbooked donors thereby enhancing the ability to control the volume of blood collected based on the results of blood demand forecasting; and (iii) scheduling blood donation appointments according to the expected number and type of blood donations, thus improving the quantity of blood by reducing the number of canceled appointments, and indirectly improving the quality and quantity of blood supply by decreasing the number of unqualified donors, thereby reducing the amount of invalid blood after and before preparation. As a result of the system’s improvements, blood shortages and waste can be reduced. The proposed solution provides robust and accurate predictions and identifies important clinical predictors for blood demand forecasting. Compared with the past year’s historical data, our integrated proposed system increased collected blood volume by 11%, decreased inventory wastage by 20%, and had a low incidence of shortages.

The Impact of Age Group in Hierarchical Forecasting of Monthly Blood Donations in Zimbabwe

“… 36 , 37 Similarly, in a study of Chilean blood donors, it was concluded that blood donors who had previously donated blood were more likely to dominate in future donations. 38 …”

Forecasting demand for blood products: Towards inventory management of a perishable product

“…The medical community is focusing on strengthening supply networks to reduce risks and waste while maintaining customer service standards [ 1 , 2 ]. The main challenges in the health supply chain include demand uncertainty, inventory management, expiration, and a lack of resources [ 2 , 3 , 4 ]. Planning blood collection and distribution is crucial for hospitals and healthcare facilities, especially in the production and distribution of various blood components.…”

“…Planning blood collection and distribution is crucial for hospitals and healthcare facilities, especially in the production and distribution of various blood components. Forecasting blood consumption ensures balance between demand and supply, preventing inventory shortages or oversupply [ 4 ]. This allows for rational resource allocation and clinical need coordination.…”

“…Accurate projections of blood demand are essential for making wise choices and managing blood supplies. Gathering data over years helps determine demand characteristics and forecast future demand [ 4 , 6 ].…”

“…However, these models haven't been able to correctly forecast how clinical transfusion procedures would alter [ 7 - 9 ]. Use of a time-series methods have shown potential for high accuracy in forecasting demand for RBC transfusions [ 4 , 8 ]. Time-series models have been used in various domains such as public health and biological data aspects [ 10 ], brain studies [ 11 ], drug usage [ 12 ], gene networks [ 13 ], traffic safety [ 14 ], prediction of COVID19 outbreak [ 15 ], prediction of RBC demand [ 8 , 16 ] and so on.…”