Publication Date
4-2025
Presentation Length
Poster/Gallery presentation
College
College of Sciences & Mathematics
Department
Math and Computer Science, Department of
Student Level
Undergraduate
SPARK Category
Research
Faculty Advisor
Dr. Christina Davis
WELL Core Type
Occupational Wellness
SPARK Session
Data Science 11:45-12:45, 10:15-11:45 Poster/Gallery Session Ayers 2150
Presentation Type
Poster
Summary
The timely and cost-effective distribution of antiretroviral (ARV) and HIV laboratory commodities, particularly in over-exploited and low-resource settings, is imperative through effective Supply Chain Management.This project uses a comprehensive supply chain dataset to analyze pricing trends, predict lead times, forecast demand, and optimize costs for ARV and HIV lab shipments across multiple countries. Through data science, we can predict future demand, detect unusual pricing patterns, assess risks of supply chain disruptions, examine how commodity prices fluctuate over time and across regions, identify key factors influencing shipment lead times, and develop predictive models for future demand. Through anomaly detection techniques, we can uncover irregularities in pricing or shipments that may indicate inefficiencies, compliance risks, or potential fraud. Additionally, we can evaluate the effectiveness of predictive analytics to anticipate supply chain disruptions based on historical trends. The findings can provide actionable insights for those who contribute to the optimization of global health supply chains (policymakers, logistics managers, and healthcare organizations, etc) with the goal of ensuring equitable access to essential HIV-related commodities.
Recommended Citation
Ahrndt, Kayla, "Forecasting Supply Chain Disruptions and Reducing Costs Through Data Science" (2025). SPARK Symposium Presentations. 73.
https://repository.belmont.edu/spark_presentations/73
