The Optimization of the Pay-per-use Model for the Healthcare Supply Chain

Abstract

Implementing the pay-per-use method can reduce hospital reagent inventory expenses, resulting in significant cost savings. However, this purchasing method, which lacks the capability to determine the order quantity, increases cost risks for reagent suppliers due to extended payment cycles between hospitals and suppliers. This paper aims to optimize the medical supply chain management under the pay-per-use model through process reengineering of the settlement mechanism and demand forecasting. It proposed the Supply, Processing, and Distribution (SPD) model for the healthcare supply chain and conducted an analysis of issues within the hospital settlement model. The paper then used the BP neural network to forecast hospital demand under the pay-per-use model, which was validated using data from Hospital A. The model fit was 0.97 for the training set and 0.87 for the test set, demonstrating the feasibility of reengineering the pay-per-use model and the effectiveness of the demand forecasting model. This study investigates how to optimize the pay-per-use model in the healthcare supply chain with the goal of reducing supplier inventory costs. It focusses on cash flow issues caused by suppliers' inability to accurately forecast the demand for hospital reagents. It provides practical as well as theoretical insight on how to improve the efficiency of healthcare supply chains.