Journal Article

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

Hemmak, Allaoua, Senouci, Benaoumeur, Ben Elmir, Walid

2023

Overview

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.

• Publisher: MDPI AG
• Subject: Apheresis / Artificial intelligence / Blood & organ donations / blood bank management / Blood banks / Blood groups / Blood platelets / Blood products / blood supply chain / Blood transfusion / Blood volume / Case studies / Decision support systems / Demand / Forecasting / Hospitals / Inventory / Inventory management / Literature reviews / Machine learning / machine learning algorithms / Modules / Neural networks / Plasma / Regression analysis / Robustness (mathematics) / Scheduling / Shortages / Supply & demand / Supply chains / Time series / time series forecasting models / Uncertainty