Risk Assessment for Complex Systems Based on Fuzzy Cognitive Maps: A Case of the Biopharmaceutical Industry

Analyzing the supply chain (SC) of biopharmaceutical drugs can be challenging due to their complexity, the existence of a wide variety of risks, and the dynamics of the system. This paper presents a framework for evaluating the SC risks of Iranian biopharmaceutical companies based on cause-and-effec...

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Main Authors: Hadi Abbasian, Reza Yousefi-Zenouz, Abdollah Amirkhani, Masoud Shirzadeh, Akbar Abdollahiasl, Shekoufeh Nikfar, Mohammadreza Siahi-Shadabad, Abbas Kebriaeezadeh
Format: Article
Language:English
Published: Wiley 2024-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/2024/4369401
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author Hadi Abbasian
Reza Yousefi-Zenouz
Abdollah Amirkhani
Masoud Shirzadeh
Akbar Abdollahiasl
Shekoufeh Nikfar
Mohammadreza Siahi-Shadabad
Abbas Kebriaeezadeh
author_facet Hadi Abbasian
Reza Yousefi-Zenouz
Abdollah Amirkhani
Masoud Shirzadeh
Akbar Abdollahiasl
Shekoufeh Nikfar
Mohammadreza Siahi-Shadabad
Abbas Kebriaeezadeh
author_sort Hadi Abbasian
collection DOAJ
description Analyzing the supply chain (SC) of biopharmaceutical drugs can be challenging due to their complexity, the existence of a wide variety of risks, and the dynamics of the system. This paper presents a framework for evaluating the SC risks of Iranian biopharmaceutical companies based on cause-and-effect relationships and fuzzy cognitive maps (FCMs). We first interviewed several biopharmaceutical supply experts to learn about potential SC risks, causal relationships among FCM concepts, FCM structure, and FCM activation cycle. The most critical and relevant risks and significant elements of the SCs, such as cost, time, and quality, were identified as relevant FCM concepts. Then, we used failure mode and effects analysis (FMEA) and the FCM of the SC risks to assess the impacts of the biopharmaceutical SC risks on each other and on the crucial elements of the SCs. The Hebbian learning algorithms were then applied to train the FCM models. We tested different scenarios to evaluate the impacts of FCM concepts on the SC risks. The proposed approach can prioritize risk factors and, more importantly, predict and analyze the effect of each risk factor/risk group on other risks or the outcome of a given risk. The proposed FCM features and the knowledge gained from evaluating them can provide practical and helpful information to pharmaceutical companies to deal with their supply risks more efficiently.
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spelling doaj-art-8ef54d10003741519b10ad59fbbf17892025-02-03T07:23:26ZengWileyComplexity1099-05262024-01-01202410.1155/2024/4369401Risk Assessment for Complex Systems Based on Fuzzy Cognitive Maps: A Case of the Biopharmaceutical IndustryHadi Abbasian0Reza Yousefi-Zenouz1Abdollah Amirkhani2Masoud Shirzadeh3Akbar Abdollahiasl4Shekoufeh Nikfar5Mohammadreza Siahi-Shadabad6Abbas Kebriaeezadeh7Department of Pharmacoeconomics and Pharmaceutical AdministrationLecturer in Business AnalyticsDepartment of Electrical EngineeringDepartment of Electrical EngineeringDepartment of Pharmacoeconomics and Pharmaceutical AdministrationDepartment of Pharmacoeconomics and Pharmaceutical AdministrationDepartment of Pharmaceutical and Food ControlDepartment of Pharmacoeconomics and Pharmaceutical AdministrationAnalyzing the supply chain (SC) of biopharmaceutical drugs can be challenging due to their complexity, the existence of a wide variety of risks, and the dynamics of the system. This paper presents a framework for evaluating the SC risks of Iranian biopharmaceutical companies based on cause-and-effect relationships and fuzzy cognitive maps (FCMs). We first interviewed several biopharmaceutical supply experts to learn about potential SC risks, causal relationships among FCM concepts, FCM structure, and FCM activation cycle. The most critical and relevant risks and significant elements of the SCs, such as cost, time, and quality, were identified as relevant FCM concepts. Then, we used failure mode and effects analysis (FMEA) and the FCM of the SC risks to assess the impacts of the biopharmaceutical SC risks on each other and on the crucial elements of the SCs. The Hebbian learning algorithms were then applied to train the FCM models. We tested different scenarios to evaluate the impacts of FCM concepts on the SC risks. The proposed approach can prioritize risk factors and, more importantly, predict and analyze the effect of each risk factor/risk group on other risks or the outcome of a given risk. The proposed FCM features and the knowledge gained from evaluating them can provide practical and helpful information to pharmaceutical companies to deal with their supply risks more efficiently.http://dx.doi.org/10.1155/2024/4369401
spellingShingle Hadi Abbasian
Reza Yousefi-Zenouz
Abdollah Amirkhani
Masoud Shirzadeh
Akbar Abdollahiasl
Shekoufeh Nikfar
Mohammadreza Siahi-Shadabad
Abbas Kebriaeezadeh
Risk Assessment for Complex Systems Based on Fuzzy Cognitive Maps: A Case of the Biopharmaceutical Industry
Complexity
title Risk Assessment for Complex Systems Based on Fuzzy Cognitive Maps: A Case of the Biopharmaceutical Industry
title_full Risk Assessment for Complex Systems Based on Fuzzy Cognitive Maps: A Case of the Biopharmaceutical Industry
title_fullStr Risk Assessment for Complex Systems Based on Fuzzy Cognitive Maps: A Case of the Biopharmaceutical Industry
title_full_unstemmed Risk Assessment for Complex Systems Based on Fuzzy Cognitive Maps: A Case of the Biopharmaceutical Industry
title_short Risk Assessment for Complex Systems Based on Fuzzy Cognitive Maps: A Case of the Biopharmaceutical Industry
title_sort risk assessment for complex systems based on fuzzy cognitive maps a case of the biopharmaceutical industry
url http://dx.doi.org/10.1155/2024/4369401
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