ABSTRACT
BACKGROUND: Inventory managers in the blood supply chain always endeavor to provide their clients with prompt and appropriate responses. On the other hand, timely and regular blood deliveries to consumers are essential since ineffective delivery and transportation practices promote shortages, returns, blood loss. The paper attempted to develop an extensive and integrated optimal model of blood transfusion network logistics management by blood type to reduce the cost of losses, returns, and blood shortages given the relevance of this for the blood transfusion network. METHODS: The regional blood transfusion network in Khorasan Razavi, which contains one main base, six central bases, and 54 hospitals, should be constructed using the optimal model for blood inventory management and distribution. A reusable simulation process was used to identify the optimal behavior for the inventory of all participants in the region (central bases as suppliers and hospitals as consumers), and the demand of hospitals as consumers has been calculated using artificial neural networks. This will lead to a significant reduction of returned blood units by consumers, optimal management of suppliers' and consumers' inventory to prevent waste and shortages. The routing method was used to proceed with the designed model and look into the optimal strategy to distribute blood requested by the consumers. with the aim of reducing the cost and increasing the speed of transportation. RESULTS: The model's solution allowed for the estimation of the amount of consumers' demand, the optimal amount of target stock, the central bases and hospitals' reorder points, as well as the method of distributing blood from the supplier to its consumers. Implementing the model leads to outcomes such as reducing the time of blood transfer from the central bases to their consumers, increasing the speed of blood delivery to the consumers, increasing the average stock of blood in the central bases, reducing the accumulation of distribution machines at the location of the central bases, the amount of stock, the method for requesting, consuming, and storing blood, and the performance of the central bases' consumers all affect how much control they have over them.
Subject(s)
Blood Transfusion , Neural Networks, Computer , Humans , Computer SimulationABSTRACT
Blood supply managers in the blood supply chain have always sought to create enough reserves to increase access to different blood products and reduce the mortality rate resulting from expired blood. Managers' adequate and timely response to their customers is considered vital due to blood perishability, uncertainty of blood demand, and the direct relationship between the availability/lack of blood supply and human life. Further to this, hospitals' awareness of the optimal amount of requests from suppliers is vital to reducing blood return and blood loss, since the loss of blood products surely leads to high expenses. This paper aims to design an optimal management model of blood transfusion network by a synthesis of reusable simulation technique (applicable to all bases) and deep neural network (the latest neural network technique) with multiple recursive layers in the blood supply chain so that the costs of blood waste, return, and shortage can be reduced. The model was implemented on and developed for the blood transfusion network of Khorasan Razavi, which has 6 main bases active from October 2015 to October 2017. In order to validate the data, the data results of the variables examined with the real data were compared with those of the simulation, and the insignificant difference between them was investigated by t test. The solution of the model facilitated a better prediction of the amount of hospital demand, the optimal amount of safety reserves in the bases, the optimal number of hospital orders, and the optimal amount of hospital delivery. This prediction helps significantly reduce the return of blood units to bases, increase availability of inventories, and reduce costs.
