Health supply chain forecasting: a comparison of ARIMA and LSTM time series models for demand prediction of medicines

Abstract

The ever-accelerating revolution along with digitalization of the healthcare industry has revealed the power of machine learning and deep learning prediction models in addressing health supply chain logistic issues. The purpose of this study was to predict the demand for medicines using autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) time series models while comparatively analysing their performance for medicine demand prediction to optimize the flow of supplies in the health system. Using data generated in Rwanda public health supply chain, in our study focused on predicting the demand of the top five medicines, identified as highly supplied (amoxicillin, penicillin v, ibuprofen, paracetamol, and metronidazole). We evaluated the models’ outputs by root mean square error (RMSE) and the coefficient of determination, R-squared (R2). In comparison to ARIMA, the deep learning LSTM model revealed superior performance with better accuracy and lower error rates in predicting the demand for medicines. Our results revealed that the LSTM model has an RMSE value of 2.0 for the training set and 2.043 for the test set, with R2 values of 0.952 and 0.912, respectively. ARIMA has an RMSE value of 9.35 for the training set and 8.926 for the test set as well as R2 value of 0.24 and 0.16 for the training and test sets, respectively. Based on these findings, we recommend that the LSTM time series model should be used for demand prediction in the management of medicines and their flow within health supply chain due to its remarkable performance for prediction task when applied to the dataset of our study.