Optimizing Inventory Level by Using the VBEOQ Model

In today’s increasingly competitive marketplace, efficient inventory management is crucial for businesses seeking to maximize profits and enhance customer satisfaction. Effective inventory management is crucial for organizations, particularly in the pharmaceutical industry, where balancing supply and demand is essential for operational efficiency. Traditional Economic Order Quantity (EOQ) models have been widely used for optimizing inventory levels; however, they often fail to incorporate financial factors that influence business performance. This study aimed to determine the optimal inventory level for the pharmaceutical supply organization ‘XXX’ using the Value-Based Economic Order Quantity (VBEOQ) model. The ABC classification of 1236 pharmaceutical products at the ‘XXX’ pharmaceutical supply organization revealed six A-stock products: Vemlidy, Lianhua Qingwen, Lutasan, Tabex, Ibuprofen-Denk, and Ketotifen Sopharma. These products collectively represented 80% of the organization’s total revenue, highlighting their critical importance to the company’s financial performance. Using the Economic Order Quantity (EOQ) and Value-Based Economic Order Quantity (VBEOQ) models, we analyzed order lead times and quantities for two critical pharmaceutical products, Vemlidi and Lianhua Qingwen. Our comparative analysis revealed significant differences between traditional and value-based inventory optimization approaches. According to the results of the EOQ model, the order quantity for Vemlidy was 28,556 units, with an order lead time of two orders per year. For Lianhua Qingwen, the order quantity was 43,603 units, and the order lead time was three-four orders per year. However, according to the results of the VBEOQ model, the order quantity for Vemlidy ranged from 1600 to 2000 units, while for Lianhua Qingwen, the order quantity ranged from 2300 to 2800 units per order. The findings indicate that the VBEOQ model provides a more dynamic and financially efficient method for determining optimal order quantities while minimizing inventory-related costs. This study contributes to the ongoing discussion on improving pharmaceutical inventory management through value-based approaches.