Identification and assessment of risk factors in the supply chain of the pharmaceutical industry using artificial intelligence.
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 intelligenc...
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| Format: | Article |
| Language: | fas |
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Tehran University of Medical Sciences
2021-12-01
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| Series: | بیمارستان |
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| Online Access: | http://jhosp.tums.ac.ir/article-1-6504-en.html |
| _version_ | 1818193890943959040 |
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| author | Rahele Panjekoobi Farzad Firouzi Jahantigh |
| author_facet | Rahele Panjekoobi Farzad Firouzi Jahantigh |
| author_sort | Rahele Panjekoobi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-12T00:53:36Z |
| format | Article |
| id | doaj.art-e6b90a27d7a54514884f7f627aac87fd |
| institution | Directory Open Access Journal |
| issn | 2008-1928 2228-7450 |
| language | fas |
| last_indexed | 2024-12-12T00:53:36Z |
| publishDate | 2021-12-01 |
| publisher | Tehran University of Medical Sciences |
| record_format | Article |
| series | بیمارستان |
| spelling | doaj.art-e6b90a27d7a54514884f7f627aac87fd2022-12-22T00:43:57ZfasTehran University of Medical Sciencesبیمارستان2008-19282228-74502021-12-012044250Identification and assessment of risk factors in the supply chain of the pharmaceutical industry using artificial intelligence.Rahele Panjekoobi0Farzad Firouzi Jahantigh1 MSc student, Department of Industrial Engineering, University of Sistan and Baluchestan, Zahedan, Iran Associate Professor, Department of Industrial Engineering, University of Sistan and Baluchestan, Zahedan, Iran(*Corresponding Author), Firouzi@eng.usb.ac.ir 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.http://jhosp.tums.ac.ir/article-1-6504-en.htmlrisksupply chain risk managementneural networkpharmaceutical supply chain management. |
| spellingShingle | Rahele Panjekoobi Farzad Firouzi Jahantigh Identification and assessment of risk factors in the supply chain of the pharmaceutical industry using artificial intelligence. بیمارستان risk supply chain risk management neural network pharmaceutical supply chain management. |
| title | Identification and assessment of risk factors in the supply chain of the pharmaceutical industry using artificial intelligence. |
| title_full | Identification and assessment of risk factors in the supply chain of the pharmaceutical industry using artificial intelligence. |
| title_fullStr | Identification and assessment of risk factors in the supply chain of the pharmaceutical industry using artificial intelligence. |
| title_full_unstemmed | Identification and assessment of risk factors in the supply chain of the pharmaceutical industry using artificial intelligence. |
| title_short | Identification and assessment of risk factors in the supply chain of the pharmaceutical industry using artificial intelligence. |
| title_sort | identification and assessment of risk factors in the supply chain of the pharmaceutical industry using artificial intelligence |
| topic | risk supply chain risk management neural network pharmaceutical supply chain management. |
| url | http://jhosp.tums.ac.ir/article-1-6504-en.html |
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