An efficient inventory model to reduce the wastage of blood in the national blood transfusion service

Kumari, D.M.S.; Wijayanayake, A.

doi:10.1109/mies.2016.7779989

Cited by 16 publications

(17 citation statements)

References 1 publication

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“…The system architecture comprises of the main architecture in which we have a user application to which other entities are sending and receiving data. It features a firebase cloud messaging, a firebase database, and ASP.net web services [21,22].…”

Section: Methodsmentioning

confidence: 99%

An ERP Based Blood Donation Management System for Hospital and Donor

Muhammad

Asif

Abbas

et al. 2020

SJET

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The objective of this paper is to develop a mobile blood donation management system application. This paper an android based application development technique by using an ERP model database management system. The techniques involve using mobile development IDEs and adequate APIs to have desired functionalities. There are two main mobile developing platforms present in the world iOS and Android. We have developed our application on Android OS. Different applications were surveyed and used to explore the presently available features to the end-user. After trials and research, the outline was made to what extent should go and developed. As the system is developed for hospitals and donors, the hospital puts a request for blood groups on the application. The system then finds nearby users online with the requested blood groups, if anybody required for his / her relative he or she can use this application for find donors who are available or offline with full information of donor if donor or user is interested in donation then he has done to the requester. Users' locations are tracked in real-time. If the users proceed to the hospital, the tracking shows if they are coming or not.

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Section: Methodsmentioning

confidence: 99%

An ERP Based Blood Donation Management System for Hospital and Donor

Muhammad

Asif

Abbas

et al. 2020

SJET

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“…Later, Silva Filho et al (2013) extend their model by developing an automatic procedure for demand forecasting while also changing the level of the model from hospital level to regional blood centre in order to help managers use the model directly. Kumari and Wijayanayake (2016) propose a blood inventory management model for the daily supply of platelets focusing on reducing platelet shortages. Three time series methods, namely MA, Weighted Moving Average (WMA) and ES are used to forecast the demand, and are evaluated based on shortages.…”

Section: Literature Reviewmentioning

confidence: 99%

Demand Forecasting for Platelet Usage: from Univariate Time Series to Multivariate Models

Motamedi¹,

Li²,

Down³

et al. 2021

Preprint

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Platelet products are both expensive and have very short shelf lives. As usage rates for platelets are highly variable, the effective management of platelet demand and supply is very important yet challenging. The primary goal of this paper is to present an efficient forecasting model for platelet demand at Canadian Blood Services (CBS). To accomplish this goal, four different demand forecasting methods, ARIMA (Auto Regressive Moving Average), Prophet, lasso regression (least absolute shrinkage and selection operator) and LSTM (Long Short-Term Memory) networks are utilized and evaluated. We use a large clinical dataset for a centralized blood distribution centre for four hospitals in Hamilton, Ontario, spanning from 2010 to 2018 and consisting of daily platelet transfusions along with information such as the product specifications, the recipients' characteristics, and the recipients' laboratory test results. This study is the first to utilize different methods from statistical time series models to data-driven regression and a machine learning technique for platelet transfusion using clinical predictors and with different amounts of data. We find that the multivariate approaches have the highest accuracy in general, however, if sufficient data are available, a simpler time series approach such as ARIMA appears to be sufficient. We also comment on the approach to choose clinical indicators (inputs) for the multivariate models.

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“…Several authors have investigated different univariate time series models to predict platelet demand, including moving averages, weighted moving averages, exponential smoothing, Winters models, and autoregressive moving averages (ARIMA) [ 10 , 13 - 15 ]. Fanoodi et al [ 14 ] reported improved prediction when using univariate time series modeling by means of an artificial neural network (ANN) compared with an ARIMA model.…”

Section: Introductionmentioning

confidence: 99%

Reduction of Platelet Outdating and Shortage by Forecasting Demand With Statistical Learning and Deep Neural Networks: Modeling Study

Schilling¹,

Rickmann²,

Hutschenreuter³

et al. 2022

JMIR Med Inform

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Background Platelets are a valuable and perishable blood product. Managing platelet inventory is a demanding task because of short shelf lives and high variation in daily platelet use patterns. Predicting platelet demand is a promising step toward avoiding obsolescence and shortages and ensuring optimal care. Objective The aim of this study is to forecast platelet demand for a given hospital using both a statistical model and a deep neural network. In addition, we aim to calculate the possible reduction in waste and shortage of platelets using said predictions in a retrospective simulation of the platelet inventory. Methods Predictions of daily platelet demand were made by a least absolute shrinkage and selection operator (LASSO) model and a recurrent neural network (RNN) with long short-term memory (LSTM). Both models used the same set of 81 clinical features. Predictions were passed to a simulation of the blood inventory to calculate the possible reduction in waste and shortage as compared with historical data. Results From January 1, 2008, to December 31, 2018, the waste and shortage rates for platelets were 10.1% and 6.5%, respectively. In simulations of platelet inventory, waste could be lowered to 4.9% with the LASSO and 5% with the RNN, whereas shortages were 2.1% and 1.7% with the LASSO and RNN, respectively. Daily predictions of platelet demand for the next 2 days had mean absolute percent errors of 25.5% (95% CI 24.6%-26.6%) with the LASSO and 26.3% (95% CI 25.3%-27.4%) with the LSTM (P=.01). Predictions for the next 4 days had mean absolute percent errors of 18.1% (95% CI 17.6%-18.6%) with the LASSO and 19.2% (95% CI 18.6%-19.8%) with the LSTM (P<.001). Conclusions Both models allow for predictions of platelet demand with similar and sufficient accuracy to significantly reduce waste and shortage in a retrospective simulation study. The possible improvements in platelet inventory management are roughly equivalent to US $250,000 per year.

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An efficient inventory model to reduce the wastage of blood in the national blood transfusion service

Cited by 16 publications

References 1 publication

An ERP Based Blood Donation Management System for Hospital and Donor

An ERP Based Blood Donation Management System for Hospital and Donor

Demand Forecasting for Platelet Usage: from Univariate Time Series to Multivariate Models

Reduction of Platelet Outdating and Shortage by Forecasting Demand With Statistical Learning and Deep Neural Networks: Modeling Study

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