Reducing demand uncertainty in the platelet supply chain through artificial neural networks and ARIMA models

One of the significant issues in global healthcare systems is improving the supply chain performance and addressing the uncertainties in demand. Blood products, especially platelets, have the most challenging supply chains in the health system given their short shelf life and limited human resources. Therefore, proper management of blood products is critical, and in turn, could reduce loss and health costs, and help preserve these valuable resources. This study aims to predict blood platelet demands based on artificial neural networks (ANNs) and auto-regressive integrated moving average (ARIMA) models in order to reduce the uncertainty in the supply chain. To this end, daily demands for eight types of blood platelets from 2013 to 2018 were used in the current study. Data were collected from treatment centers and hospitals located in Zahedan, Iran. The results of this study indicated that ANNs and ARIMA models were more accurate in predicting the uncertainties in demand than the baseline model used in Zahedan Blood Transfusion Center. The highest and lowest prediction improvements based on ANNs and ARIMA models were associated with type O+ and A+ platelets, respectively. Given that the ANN models can significantly improve the prediction of uncertainties in demand, we highly recommend that the conventional statistical prediction methods in blood transfusion centers be replaced with these models.