M.S. Applied Data Science - Capstone Chronicles 2025

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model can be developed that improves the accuracy and efficiency of recall classification. This model is also expected to offer early indicators of potential safety issues. The project has the potential to reduce health impacts, lower manufacturer costs, improve regulatory efficiency, and enhance transparency in public risk awareness. These goals support public health and safety management efforts across industries and institutions. 2 Background Product recalls are a critical component of consumer safety, necessitating prompt action from manufacturers and regulatory agencies to identify and mitigate potential hazards. The U.S. FDA oversees recalls across various sectors, including biologics, medical devices, drugs, food and cosmetics, tobacco, and veterinary products. The FDA classifies recalls based on the following severity of health risks: ●​ Class I (most severe, life-threatening risks)​ ●​ Class II (Moderate risks)​ ●​ Class III (least severe, unlikely to cause harm) The recall process typically begins when a firm identifies a defect and notifies the FDA, providing relevant information about the product and the nature of the problem. The FDA then assesses the health hazard to determine the appropriate recall classification. This process involves evaluating the level of risk, determining the scope of the recall, notifying the public, and monitoring the effectiveness of the recall (FDA, 2024). In the current recall process, manufacturers voluntarily provide the necessary information to the FDA, which can delay public awareness of

the product’s risk severity. In addition, each industry has independent supply chain structures and recall management protocols, though they could share the same distribution networks. FDA recall records contain cross-category distribution data, offering an opportunity for researchers to develop predictive classification models that are generalizable across industries. 2.1 Problem Identification and Motivation Recall classification accuracy and timeliness are major concerns, with studies suggesting that external factors, such as lobbying activities, may influence recall classifications. This raises questions about the objectivity and consistency of the recall process. Additionally, the prolonged duration between recall initiation and termination, as highlighted by recent research, indicates a need for more efficient identification and management of recalls, especially for high-severity cases. The lack of proactive risk identification and inconsistent communication across different product types further complicate the recall process. Current systems often react to safety issues after they occur, rather than proactively identifying potential risks. The absence of standardized language and risk assessments can lead to confusion and poor communication with consumers and stakeholders. Additionally, the breakdown of recall management protocols across industries, despite shared distribution networks, can lead to prolonged circulation of hazardous products. Quality assurance professionals face significant challenges in mitigating defects that lead to costly and potentially hazardous recalls. This research is driven by the need to enhance public health protection, streamline resource allocation,

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