Abstract
Purpose The uncertainty in supply and the short shelf life of blood products have led to a substantial outdating of the collected donor blood. On the other hand, hospitals and blood centers experience severe blood shortage due to the very limited donor population. Therefore, the necessity to forecast the blood supply to minimize outdating as well as shortage is obvious. This study aims to efficiently forecast the supply of blood components at blood centers. Methods Two different types of forecasting techniques, time series and machine learning algorithms, are developed and the best performing method for the given case study is determined. Under the time series, we consider the Autoregressive (AUTOREG), Autoregressive Moving Average (ARMA), Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA, Seasonal Exponential Smoothing Method (ESM), and Holt-Winters models. Artificial neural network (ANN) and multiple regression are considered under the machine learning algorithms. Results We leverage five years worth of historical blood supply data from the Taiwan Blood Services Foundation (TBSF) to conduct our study. On comparing the different techniques, we found that time series forecasting methods yield better results than machine learning algorithms. More specifically, the least value of the error measures is observed in seasonal ESM and ARIMA models. Conclusions The models developed can act as a decision support system to administrators and pathologists at blood banks, blood donation centers, and hospitals to determine their inventory policy based on the estimated future blood supply. The forecasting models developed in this study can help healthcare managers to manage blood inventory control more efficiently, thus reducing blood shortage and blood wastage.
Highlights
Blood performs several important functions in the human body such as transporting oxygen, carrying supplements to our cells, disposing ammonia, carbon dioxide, and other waste items
Due to the short shelf life of blood components, hospitals and blood centers are faced with the challenge of maintaining appropriate inventory levels to avoid outdating and shortage
Managing blood supply and demand is the core part of the healthcare supply chain system as blood plays a very crucial role in saving human lives
Summary
Blood performs several important functions in the human body such as transporting oxygen, carrying supplements to our cells, disposing ammonia, carbon dioxide, and other waste items. Four of the most critical elements are the red blood cells (RBC), white blood cells (WBC), plasma, and platelets [1]. Managing blood supply and demand is the core part of the healthcare supply chain system as blood plays a very crucial role in saving human lives. Blood supply forecasting is essential for making supply chain decisions, such as donor drive scheduling, vehicle routing policies, and inventory management, at blood centers and hospitals. Accurate forecasts of the timing and amount of future blood requests have been considered as the key inputs to donor recruitment decision making and inventory control. Lestari et al [7] indicated that the forecasting can predict the data trend observed and future demand for blood components
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