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

Walid Ben Elmir · Hemmak Allaoua · Benaoumeur Senouci

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Blood donation and transfusion practices Supply Chain and Inventory Management Artificial Intelligence in Healthcare

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.

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References

1979Distribution of the Estimators for Autoregressive Time Series with a Unit Root 1977Standards for blood banks and transfusion services.2013Pharmaceutical supply chain and inventory management strategies: Optimization for a pharmaceutical company and a hospital 2015A structured review of quantitative models in the blood supply chain: a taxonomic framework for decision-making 2005Supply Chain Management of Blood Banks 2014The top ten global health supply chain issues: Perspectives from the field 2019Inventory management of platelets along blood supply chain to minimize wastage and shortage 2019Reducing demand uncertainty in the platelet supply chain through artificial neural networks and ARIMA models

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2024Machine Learning and Deep Learning Models for Demand Forecasting in Supply Chain Management: A Critical Review 2024Big Data—Supply Chain Management Framework for Forecasting: Data Preprocessing and Machine Learning Techniques 2023AI in Inventory Management: Applications, Challenges, and Opportunities 2023Blood Demand Forecasting and Supply Management: An Analytical Assessment of Key Studies Utilizing Novel Computational Techniques

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