Overstocking in Healthcare: Challenges and Strategic Solutions Leveraging Process and Technology Advancements

Abstract

Overstocking in healthcare refers to excessive inventory levels of medical supplies, equipment, and medication beyond operational requirements. This practice has adverse effects on cost efficiencies, waste reduction, and overall patient care. This paper explores the critical dimensions of overstocking, identifies its root causes, and provides practical solutions, including adopting value-based procurement strategies and leveraging digital technologies such as AI/ML for inventory management. The study elaborates on immediate interventions and long-term strategies to align inventory management practices with operational and care objectives, supported by data-driven methodologies. The work emphasizes a balanced approach, combining technology and organizational strategies to address the issue comprehensively.

Introduction

I. INTRODUCTION

Overstocking poses significant operational challenges across various industries, with amplified implications in healthcare. The accumulation of excessive inventory not only incurs direct costs but also impacts supply chain efficiencies, waste management, and patient care outcomes. Effective inventory management ensures that resources are allocated optimally, minimizing costs while maintaining high-quality care standards. This paper underscores the criticality of addressing overstocking in healthcare and presents a comprehensive framework for managing this challenge effectively. The scope includes identifying root causes, evaluating current practices, and proposing technological and organizational solutions to mitigate the issue.

The healthcare industry faces unique challenges compared to other sectors, given its critical role in saving lives and ensuring well-being. Inventory mismanagement can lead to either shortages or surpluses, both of which have dire consequences. Overstocking, in particular, is often driven by the desire to avoid shortages, yet it creates financial and operational bottlenecks. The issue necessitates a detailed exploration of its root causes and viable strategies to strike a balance between availability and efficiency.

II. PROBLEM STATEMENT: OVERSTOCKING CHALLENGES

A. Impact on Costs and Margins

Healthcare providers allocate substantial budgets to supplies and labor, with inventory forming a critical expense. Overstocking exacerbates financial strain by tying up capital in unused supplies and contributing to waste. The cost of unused drug waste in the healthcare industry costs $2.8 billion of medication per year. [2]. Organizations also face reduced profit margins due to high carrying costs, such as storage, insurance, and depreciation. These costs can account for a significant percentage of operational expenditures, particularly when inventory becomes obsolete or expires.

Approximately 30 percent of all hospital spending in the U.S. goes to supply chain overspend, which equals $25.4 billion each year. In hospitals, supply chain spending is second only to labor costs and is expected to rise. [1].

Figure 1: Financial Impact of Overstocking

B. Patient Care Implications

The unavailability of critical resources at the right time—caused by poor inventory turnover—disrupts patient care. Overstocking often masks underlying inefficiencies in the supply chain, leading to delayed treatment and potential safety concerns for patients. For instance, expired supplies due to overstocking can result in wastage and a reduced ability to respond to emergencies. Furthermore, overstocked facilities may face logistical challenges in accessing the right equipment during critical situations, adding stress to already strained systems.

C. Cross-functional Dependencies

Inventory management in healthcare is inherently cross-functional, encompassing procurement, supply chain logistics, clinical operations, and administrative oversight. Overstocking highlights gaps in communication and collaboration across these functions, necessitating a coordinated response. Misaligned goals between departments can lead to inefficiencies, where one department’s focus on avoiding shortages results in over-purchasing. Such silos impede effective decision-making and result in a lack of ownership over inventory optimization

III. FACTORS CONTRIBUTING TO OVERSTOCKING

A. Poor Demand Forecasting

The lack of robust predictive tools for demand forecasting leads organizations to adopt a conservative approach by over-ordering inventory. For example, a sudden surge in patient volumes due to a flu season might lead to panic buying, followed by excessive inventory post-peak. This cyclical pattern not only drains financial resources but also undermines efforts to establish stable inventory practices

B. Inadequate Data Integration

Fragmented and siloed systems hinder real-time visibility into inventory levels, procurement cycles, and consumption trends. Without integrated systems, healthcare providers struggle to align inventory levels with actual usage, leading to overstocking. Data silos result in duplicate orders, untracked consumption, and lack of accountability, all of which exacerbate the issue.

C. Complex Supply Chains

Healthcare supply chains involve multiple stakeholders, from manufacturers to distributors and end-users. Variabilities in demand, regulatory requirements, and logistics complexities further challenge inventory optimization. Additionally, global disruptions, such as pandemics, exacerbate these challenges by destabilizing supply predictability. A lack of streamlined processes for communication and coordination across the supply chain compounds these difficulties

Table 1: Key Supply Chain Variables Impacting Overstocking

IV. PROPOSED SOLUTIONS

A. Short-term Interventions

1. Data Consolidation: Establish centralized repositories integrating procurement, inventory, and usage data. This enables data-driven decision-making and real-time visibility.
2. Task Force Formation: Create cross-functional teams comprising procurement, logistics, and clinical staff to enhance communication and accountability.
3. Reporting Mechanisms: Implement dynamic reporting tools to monitor inventory levels, consumption patterns, and waste metrics.

B. Medium-term Strategies

1. AI/ML Integration: Leverage machine learning algorithms to predict demand accurately and optimize stocking levels. For example, predictive analytics can identify trends based on historical data and adjust procurement schedules accordingly. Advantages of leveraging AI based demand forecasting and planning tools are many. AI based forecasting models deliver enhanced accuracy over a period. With increased accuracy healthcare systems can significantly reduce operational cost by reducing overstocking. Applying AI-driven forecasting to supply chain management, for example, can reduce errors by between 20 and 50 percent [3]

Figure 2: Comparison of Forecasting Accuracy with and Without AI (illustrative purposes)

1. Value-based Procurement: Shift focus from volume-based purchasing to value-driven strategies, prioritizing cost-effectiveness and patient outcomes
2. ERP System Deployment: Implement integrated enterprise resource planning (ERP) systems to streamline processes and enhance transparency

Figure 3: Cross functionally and Clinically Integrated Resilient Supply Chain

Conclusion

Addressing overstocking in healthcare requires a blend of immediate actions and forward-looking strategies. By integrating advanced analytics, fostering cross-functional collaboration, and adopting value-based procurement, healthcare organizations can enhance operational efficiencies and improve patient care outcomes. A structured approach, supported by robust data systems, offers a sustainable solution to this persistent challenge.

References

[1] Moriarity, A. (2018). How to control costs by balancing the supply chain. Definitive Healthcare Blog. [2] Kavita Patel, Julie M. Donohue, and Edward H. Shortliffe, (2021), Discarded drugs: a wasteful and costly problem that requires whole-of-government approaches, statnews article [3] AI-driven operations forecasting in data-light environments, Mckinsey Study (2022)

Copyright

Copyright © 2025 Kunal Khashu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.