A Decision-making Tool for Demand Forecasting of Blood Components

Filho, Oscar Salviano Silva; Cezarino, Wagner; Salviano, Giselle Rebelo

doi:10.3182/20120523-3-ro-2023.00201

Cited by 14 publications

(6 citation statements)

References 9 publications

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“…Critchfeld et al [15] developed models for forecasting blood usage in a blood center using several time series methods, including moving average (MA), winter's approach, and ES. Filho et al [16] developed a Box-Jenkins seasonal autoregressive integrated moving average (BJ-SARIMA) model to forecast weekly demand for hospital blood components. Teir proposed method, SARIMA, is based on a Box-Jenkins approach that considers seasonal and nonseasonal characteristics of time series data.…”

Section: Blood Demand Forecastingmentioning

confidence: 99%

BUPNN: Manifold Learning Regularizer-Based Blood Usage Prediction Neural Network for Blood Centers

Pan

Zang

et al. 2023

Computational Intelligence and Neuroscience

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Blood centers are an essential component of the healthcare system, as timely blood collection, processing, and efficient blood dispatch are critical to the treatment of patients and the performance of the entire healthcare system. At the same time, an efficient blood dispatching system through the high-precision predictive capability of artificial intelligence is crucial for the efficiency improvement of the blood centers. However, the current artificial intelligence (AI) models for predicting blood usage do not meet the needs of blood centers. The challenges of AI models mainly include lower generalization ability in different hospitals, limited stability under missing values, and low interpretability. An artificial neural network-based model named the blood usage prediction neural network (BUPNN) has been developed to address these challenges. BUPNN includes a novel similarity-based manifold regularizer that aims to enhance network mapping consistency and, thus, overcome the domain bise of different hospitals. Moreover, BUPNN diminishes the performance degradation caused by missing values through data enhancement. Experimental results on a large amount of accurate data demonstrate that BUPNN outperforms the baseline method in classification and regression tasks and excels in generalization and consistency. Moreover, BUPNN has solid potential to be interpreted. Therefore, the decision-making process of BUPNN is explored to the extent that it acts as an aid to the experts in the blood center.

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Section: Blood Demand Forecastingmentioning

confidence: 99%

BUPNN: Manifold Learning Regularizer-Based Blood Usage Prediction Neural Network for Blood Centers

Pan

Zang

et al. 2023

Computational Intelligence and Neuroscience

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“…Author Shortages and outdated stock (Chapman et al, 2004), (Rytilä & Spens, 2006), (Kopach et al, 2008), (Delen et al, 2011), (Stanger et al, 2012), (Smid et al, 2013), (Duan & Liao, 2013), (Yuzgec et al, 2013), (Abbasi & Hosseinifard, 2014), (Hosseinifard & Abbasi, 2016) , (Dillon et al, 2017). Shortage (Reynolds et al, 2001), (Dijk et al, 2009, (Filho et al, 2012), (Fahimnia et al, 2015), (Zahraee, et al, 2015), (Bedi et al, 2016). Transportation Cost (Hemmelmayr et al, 2009), (Ghandforoush & Sen, 2010), (Delen et al, 2011).…”

Section: Indicatormentioning

confidence: 99%

Improving Blood Bank Performance in A Decentralised Blood Supply Chain Using Discrete Event Simulation

Mansur

Vanany

Arvitrida

2023

OSCM: An Int. Journal

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Indonesian Red Cross (IRC) is assigned to operate the blood bank in Indonesia's blood supply chain system. Due to the system decentralization, the regional blood banks are responsible for providing blood supply in their municipal area. Blood banks need to meet certain performance indicators to minimize shortages and outdated blood bags and generate sufficient income to cover operational costs and organizational development. This research aims to build a blood-bank management model by focusing on a multi-product arrangement and considering the unpredictability of supply and demand. Discrete event simulation was used to model the actual system and evaluate policy options to enhance blood bank performance. The simulation experimentation examines four control variables: supply arrangements based on Fixed Location Service (FLS), Mobile Unit Service (MUS), inventory target, and the setting of output product percentages. This study successfully provided solutions that can enhance the quality and efficiency of blood bank services. In this study, each scenario reveals the trade-off between the extent of the shortage and the level of obsolescence. The regulatory environment where donors are recruited greatly impacts shortages, obsolescence, and net income. Blood bank management must create an optimum targeted service level and revenue expectation to make strategic decisions.

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“…Berdasarkan semua data yang tersedia, pengelola pusat penyimpanan darah memiliki tugas penting, yakni memperkirakan kebutuhan komponen darah untuk meminimalisir penuhnya penyimpanan maupun kerugian karena darah yang terbuang [2]. Kurangnya pasokan dapat berdampak pada meningkatnya jumlah kematian pasien.…”

Section: Pendahuluanunclassified

Sistem Prediksi Jumlah Permintaan Produk Darah Menggunakan Metode Least Square Regression Line (Studi Kasus : Utd Pmi Kabupaten Jombang)

Nasyika¹

2019

INFORMAL

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Blood Transfusion Unit (UTD) as the provider of blood supply is required to fulfill blood demand. However in reality, the blood stock does not always meet the demand for blood. The amount of blood group stock in UTD depends on blood donors who voluntarily donate blood. Red blood cells only have a life span of 35 days since they were donated, if they have passed that time the blood cannot be used for transfusion. Based on these problems, the authors applied the Lesat Square Regression Line method to predict the number of requests for blood products in the coming month. The calculation results obtained prediction of demand with the smallest MAPE value that is blood product of PRC blood type A with a value of 14.40%. The biggest MAPE value obtained is 180.66% for WB blood type AB. Factors or parameters that affect the amount of blood product demand, that is the environment, disease outbreaks and differences in body resistance of each blood group. In addition, the types of blood products also have a number of different requests depending on the level of blood needs for health.

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A Decision-making Tool for Demand Forecasting of Blood Components

Cited by 14 publications

References 9 publications

BUPNN: Manifold Learning Regularizer-Based Blood Usage Prediction Neural Network for Blood Centers

BUPNN: Manifold Learning Regularizer-Based Blood Usage Prediction Neural Network for Blood Centers

Improving Blood Bank Performance in A Decentralised Blood Supply Chain Using Discrete Event Simulation

Sistem Prediksi Jumlah Permintaan Produk Darah Menggunakan Metode Least Square Regression Line (Studi Kasus : Utd Pmi Kabupaten Jombang)

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