Designing an optimal inventory management model for the blood supply chain

Abstract

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.

Applications of deep learning into supply chain management: a systematic literature review and a framework for future research

“…Inventory management is a part of supply chain management that coordinates the efficient forward and reverse movement of products, services, and associated information between the point of origin and the point of consumption to fulfill customers' needs (Singh and Verma 2018). In the category of inventory management, Ahmadimanesh et al (2020) used DL to design an inventory management model for a blood transfusion network. The solutions of the model facilitate the prediction of the amount of hospital blood demand, the amount of safety stock, the optimal number of orders, and the optimal amount of delivery.…”

“…Cai et al (2018) presented a DNN to classify crop types. Ahmadimanesh et al (2020) used a feedforward neural network to design an inventory management model. Chuaysi and Kiattisin (2020) combined KNN classifier with MLP on statistical and trajectory features of fishing vessels to enable their traceability at sea.…”

Designing an Optimal Model of Blood Logistics Management with the Possibility of Return in the Three-Level Blood Transfusion Network

“…They predicted the monthly demand for three components of blood (red blood cells, plasma, and platelets) in an RBB. [21] et al (2020) used neural network and reusable simulation to design an optimal inventory management model in an area without separation of blood units. The results showed a significant reduction in loses and deficiency of blood units.…”

A robust autonomous method for blood demand forecasting

“…According to a recent survey, 1 most research on supply chain planning assumes demand as stochastic, deterministic, or otherwise intractably uncertain, using constant estimates or distribution sampling. However, demand forecasting has been deemed necessary and superior to expert planning in several studies, [1][2][3][4][5][6][7][8][9][10][11] suggesting that supply chain management approaches can be improved by a significant margin by adopting methods for demand forecasting. Almost all reviewed research on demand forecasting attempts to determine the single best method for reducing shortages or costs, revealing that the best method varies between blood banks and blood products.…”