Impact of International Pandemics, Disasters, and Disruptions on Global Pharmaceutical Supply Chain Management
Open AccessIn the United States, the rate of drug shortages has significantly increased over the last 10–15 years. The Food and Drug Administration passed the Food and Drug Administration Safety and Innovation Act in 2012, which requires pharmaceutical manufacturers to notify the administration of any changes to drug product production with the hope to prevent or mitigate any potential drug shortages. Various factors as to why drug shortages occur have been studied, including manufacturing difficulties, shortages of raw materials, voluntary recalls, natural disasters, supply and demand issues, business and economic issues, and regulatory issues. This praxis focuses specifically on how global disasters effect raw material lead times and the associated delays, which lead to production disruptions and drug shortages. The data collected for this research praxis was gathered from the Food and Drug Administration’s Drug Shortage Database, EM-DAT, a global database of disasters, and private industry information. The data was combined into a single dataset that represented the numerous variables that contribute to lead time delay. Once compiled, CART Classification and Cluster Analysis were run on the data to determine the important variables that would serve as the final input variables. These input variables were then processed through Weka and various machine learning algorithms to build a predictive model to forecast whether raw material lead time delay would occur. The results from the best performing predictive model showed that the model could predict lead time delay with an accuracy of 79.36% and a “yes delay” true positive rate of 0.511, indicating a “good” quality model. These findings indicate that there is a relationship between global disasters and the occurrence of raw material lead time delay that can lead to potential drug shortages in the United States. Future research should be conducted to narrow down the output from a broad categorical response to a numerical response of a more exact timeframe.
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Banihani_gwu_0075A_16520.pdf | 2023-11-14 | Open Access |
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