Volume 14, Issue 3 (2024)
ORMR 2024, 14(3): 45-72 |
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Daneshgar F, Rajabzadeh A, Afsharkazemi M A. A framework for predicting the delivery status of health commodities and data-driven healthcare supply chain management based on support vector machine technique and bayesian optimization: Case of the global healthcare supply chain of the United States Agency for International Development. ORMR 2024; 14 (3) :45-72
URL: http://ormr.modares.ac.ir/article-28-72933-en.html
URL: http://ormr.modares.ac.ir/article-28-72933-en.html
1- PhD candidate in Information Technology Management, Department of Information Technology, Faculty of Management and Economics, Science and Research Unit, Islamic Azad University, Tehran, Iran.
2- Professor,Department of Industrial Management, Faculty of Management, Tarbiat Modares University, Tehran, Iran. ,alirajabzadeh@modares.ac.ir
3- Associate Professor, Department of Industrial Management, Faculty of Management and Economics, Science and Research Unit, Islamic Azad University, Tehran, Iran.
2- Professor,Department of Industrial Management, Faculty of Management, Tarbiat Modares University, Tehran, Iran. ,
3- Associate Professor, Department of Industrial Management, Faculty of Management and Economics, Science and Research Unit, Islamic Azad University, Tehran, Iran.
Abstract: (113 Views)
Objective: Timely delivery of medications, medical equipment, and other essential supplies is critical to patient care and can often be life-saving. Delivery delays in the healthcare supply chain can lead to increased costs and operational challenges for healthcare organizations and affect patient care and financial stability. Efficient and reliable supply chain management is critical to reduce these risks and ensure integrated performance in the health industry. This research addresses the delay in the delivery of health commodities in the global health supply chain of the United States Agency for International Development. It presents a framework based on the support vector machine technique and Bayesian optimization to predict the delivery status of health commodities. It also determines the features that have had the greatest impact in predicting the status of commodities delivery for data-driven health supply chain management.
Method: The study's research method is design science, which presents a framework based on the support vector machine technique and Bayesian optimization to predict the delivery status of health commodities. It also compares the performance of different classification algorithms to predict the transportation status.
Findings: The results indicate that the presented framework based on the support vector machine technique and Bayesian optimization leads to a classification accuracy of 95%, outperforming other techniques to predict delivery delay. The results showed that the features of the destination country, shipping method, supplier, and production location are the most influential features in predicting the delivery status.
Article Type: Original Research |
Subject:
Strategy and Management
Received: 2023/12/13 | Accepted: 2024/08/14 | Published: 2024/11/30
Received: 2023/12/13 | Accepted: 2024/08/14 | Published: 2024/11/30
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