Date of Award

2025

Degree Name

Healthcare Administration

College

College of Business

Type of Degree

M.S.

Document Type

Research Paper

First Advisor

Alberto Coustasse, Dr.PH. MD, MBA, MPH

Abstract

Introduction: As healthcare organizations increasingly have relied on digital transformation to meet growing demands for efficiency and cost reduction, big data analytics have emerged as a key tool for optimizing supply chain performance. Inventory management, which accounted for up to 50% of total hospital operating costs, remained one of the most critical and complex areas in the healthcare supply chain. Traditional inventory methods often led to stockouts, waste, and inefficiencies. The integration of big data tools such as predictive analytics and enterprise resource planning systems presented an opportunity to streamline operations and reduce costs.

Purpose of the Study: The purpose of this study was to evaluate the impact of implementing big data analytics compared to traditional inventory management methods on inventory efficiency in healthcare, focusing specifically on decreased stockout rates, reduced inventory waste, and lower supply chain costs.

Methodology: This study utilized a qualitative literature review combined with a semi-structured interview. Twenty-three peer-reviewed articles published between 2017 and 2024 were analyzed, and one expert interview with a healthcare supply chain manager was conducted to support the literature findings with real-world experience. Data was gathered using PRISMA guidelines and included U.S.-based sources from academic databases and professional associations.

Results: One study showed that the use of big data analytics significantly improved inventory efficiency. Key metrics included a reduction in stockouts by up to 20%, a 30% reduction in inventory levels through Vendor-Managed Inventory systems, and cost savings of up to 25% annually. The interview highlighted the advantages of ERP systems like Infor in enabling better access to data, improving staff efficiency through handheld use, and enhancing forecasting. However, challenges such as inconsistent lead times, labor force education, and underutilization of technology were also identified.

Discussion/Conclusion: Big data analytics improved inventory tracking, forecasting, and cost management in healthcare settings. However, implementation success was heavily dependent on capital investment, workforce training, and organizational readiness. The findings were largely positive but not universally conclusive due to ongoing operational barriers. Further research was recommended to evaluate the long-term impact of analytics integration and to address persistent challenges in healthcare supply chain management.

Subject(s)

Health services administration.

Health facilities -- Business management.

Business logistics -- Hospitals.

Big data -- Management.

Finance -- Hospitals.

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