Volume 14, Issue 4 (Winter 2017)                   Sci J Iran Blood Transfus Organ 2017, 14(4): 335-345 | Back to browse issues page

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Firouzi jahantigh F, Fanoodi B, Khosravi S. A Demand Forcasting Model for the Blood Platelet Supply Chain with Artificial Neural Network Approach and Arima Models . Sci J Iran Blood Transfus Organ 2017; 14 (4) :335-345
URL: http://bloodjournal.ir/article-1-1133-en.html
A Demand Forcasting Model for the Blood Platelet Supply Chain with Artificial Neural Network Approach and Arima Models
F. Firouzi jahantigh * , B. Fanoodi , S. Khosravi
Keywords: Key words: Blood Platelets, Arima, Blood Transfusion
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Sci J Iran Blood Transfus Organ 2018; 14(4): 335-345
Original Article
 

 

A Demand Forcasting Model for the Blood Platelet
Supply Chain with Artificial Neural Network Approach
 and Arima Models
 
Firouzi Jahantigh F.1, Fanoodi B.1, Khosravi S.2,3
 
 
1Shahid Nikbakht Engineering College, University of Sistan and Baluchestan, Zahedan, Iran
2Blood Transfusion Research Center, High Institute for Research and Education in Transfusion Medicine, Tehran, Iran
3Zahedan Educational Regional Blood Transfusion Center, Zahedan, Iran
 
 
Abstract
Background and Objectives
One of the major issues in global healthcare systems is the issue of improving supply chain performance and uncertainties in demand. The aim of this study is to forecast blood platelet demand with artificial neural network and Arima Models in the blood transfusion supply chain in Sistan and Baluchistan province.
 
Materials and Methods
In this applied study, the data on demand for 8 types of blood platelets were collected from the Zahedan Blood Center between 2011 and 2015. Then, using artificial neural network models and ARIMA models, daily demand forecasts were made. Then, according to MSE performance evaluation criteria, the results of the above-mentioned methods were compared. The data were analyzed by MetlabR2016b and Eviews 6 softwares.
 
Results
The results of this study indicate the high accuracy of neural network models followed by Arima compared to that calculated in the current profile of IBTO. The average accuracy according to MSE of the two models for platelet types are: O+ (0.0132±0.0048), O- (0.0115 ± 0.0041), A+ (0.0205 ± 0.0043), A- (0.0108 ± 0.0033), B+ (0.0221 ± 0.0086), B- (0.0045 ± 0.0009), AB+ (0.0136 ± 0.0031), AB- (0.0034 ± 0.0005) which represent the mean and standard deviation of the error, respectively.
 
Conclusions 
The results of this study indicate the high accuracy of artificial neural network models followd by Arima in predicting blood platelet demand. Therefore, using artificial neural network models for prediction of demand is recommended instead of common statistical prediction methods in blood centers.
 
Key words: Blood Platelets, Arima, Blood Transfusion
 
 
 
Received:  4 Jul  2017
Accepted: 9 Oct 2017
 
 

Correspondence: Firouzi Jahantigh F., PhD in Industrial Engineering. Assistan Professor of Shahid Nikbakht Engineering College, University of Sistan and Baluchestan.
Postal Code: 1593915111, Zahedan, Iran. Tel: (+98854) 22344518; Fax: (+98854) 88905011
E-mail:
firouzi@eng.usb.ac.ir
Type of Study: Research | Subject: General
Published: 2017/12/31
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