Machine Learning in Forecasting Time Series to Control the Stock of High-Cost Drugs used in Cancer Treatment
Paulo César Ossani, Gislaine Camila Lapasini Leal, Lorena Mazia Enami · International Journal of Services and Operations Management · 2024
An incorrect forecast can have tragic consequences for patient care. From a practical point of view, a financial compromise can be made as long as service levels are maintained at a maximum. Aiming at a compromise monetary reduction while still meeting demand, this study addresses a cancer drug inventory control in a small hospital. While there is extensive use of parametric time series models, this paper proposes the use of machine learning techniques, such as random forests, support vector regression, K-nearest neighbours, and extreme learning machines, which can produce better results in predicting drug consumption - monthly data between 2015 and 2019. Indicators such as inventory turns and service levels were used to validate the models, which increased inventory turns by more than 45%, which represents a large impact. Thus, better demand accuracy is achieved without compromising the service levels, proving the feasibility of the techniques.