Volume 20, Issue 4 (12-2021)
jhosp 2021, 20(4): 42-50 |
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Panjekoobi R, Firouzi Jahantigh F. Identification and assessment of risk factors in the supply chain of the pharmaceutical industry using artificial intelligence.. jhosp 2021; 20 (4) :42-50
URL: http://jhosp.tums.ac.ir/article-1-6504-en.html
URL: http://jhosp.tums.ac.ir/article-1-6504-en.html
1- MSc student, Department of Industrial Engineering, University of Sistan and Baluchestan, Zahedan, Iran
2- Associate Professor, Department of Industrial Engineering, University of Sistan and Baluchestan, Zahedan, Iran(*Corresponding Author), Firouzi@eng.usb.ac.ir
2- Associate Professor, Department of Industrial Engineering, University of Sistan and Baluchestan, Zahedan, Iran(*Corresponding Author), Firouzi@eng.usb.ac.ir
Abstract: (1724 Views)
Background and Aim: As difficulties increase, the level of uncertainty and risk in the supply chain increases. Medicine is a strategic product and is directly related to community health. The aim of this study is to evaluate the risk factors of pharmaceutical supply chain with artificial intelligence methods.
Materials and Methods: By reviewing the texts and interviewed 6 adept experts who had a Master’s degree and Ph.D. and had experience between 7 and 15 years in the field of risk and pharmaceutical supply chain, risk factors were identified. Finally, using multilayered perceptron neural networks and support vector machines with polynomial linear kernel functions and radial base in two low-risk and high-risk classes were classified in Python software.
Results: 22 factors were identified and classified using neural networks in 5 categories: assets, network and transportation, government and market, strategy and supplier. Shift in interest and inflation, Changes in exchange rates, Inflexibility in production and disruption of customer service are the most important risks in the pharmaceutical supply chain, respectively. The results of evaluation criteria showed that the multilayer perceptron model had better performance than the support vector machines with linear, polynomial and radial basis functions.
Conclusion: The results showed that artificial neural networks are able to classify pharmaceutical supply chain risk factors with acceptable accuracy. As a result, classification of risk factors with an accuracy of 97/07% indicates the high ability of multilayer perceptron network in risk assessment of pharmaceutical supply chain.
Materials and Methods: By reviewing the texts and interviewed 6 adept experts who had a Master’s degree and Ph.D. and had experience between 7 and 15 years in the field of risk and pharmaceutical supply chain, risk factors were identified. Finally, using multilayered perceptron neural networks and support vector machines with polynomial linear kernel functions and radial base in two low-risk and high-risk classes were classified in Python software.
Results: 22 factors were identified and classified using neural networks in 5 categories: assets, network and transportation, government and market, strategy and supplier. Shift in interest and inflation, Changes in exchange rates, Inflexibility in production and disruption of customer service are the most important risks in the pharmaceutical supply chain, respectively. The results of evaluation criteria showed that the multilayer perceptron model had better performance than the support vector machines with linear, polynomial and radial basis functions.
Conclusion: The results showed that artificial neural networks are able to classify pharmaceutical supply chain risk factors with acceptable accuracy. As a result, classification of risk factors with an accuracy of 97/07% indicates the high ability of multilayer perceptron network in risk assessment of pharmaceutical supply chain.
Keywords: risk, supply chain risk management, neural network, pharmaceutical supply chain management.
Type of Study: Original Article |
Subject:
سایر
Received: 2022/03/30 | Accepted: 2022/05/24 | Published: 2022/06/21
Received: 2022/03/30 | Accepted: 2022/05/24 | Published: 2022/06/21
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