考量具學名藥合規率之採購合約之供應商選擇問題

Abstract

在當今的藥品供應鏈管理中，選擇合適的供應商對於維持供應鏈的效率和降低成本至關重要。隨著藥品市場全球化的日益複雜化，供應商的選擇需充分考量各類採購合約對採購成本的影響。學名藥合規率(Generic Compliance Ratio, GCR)作為評估供應商折讓的關鍵指標，為供應商選擇提供了重要的量化依據，一般而言，GCR 在實務中主要有以下兩種計算方法: 1. 學名藥採購總成本(Cost of Goods, COGs)佔處方藥 COGs 的比例 2. 學名藥 COGs 佔專利藥 COGs 的比例 本研究採用第二種方法進行計算，並在後續章節進一步闡述學名藥與專利藥在藥品供應鏈中的差異。在實務應用中，無論是大型藥企還是小型藥商，GCR 都是採購中的重要考量指標，然而，過去的文獻中對於該指標的專門探討相對少見，甚至對於藥品庫存的優化方面也缺乏深入研究。因此，在基於 GCR 的合約下，通過控制 GCR 實現最低化 COGs 成為我們的研究重點。本研究的目的為： 1. 建立數學混和整數線性規劃模型(Mixed-Integer Linear Programming Model, MILP Model)找到 COGs 的最優解。 2. 提出一種基於學名藥價差比率分級與排序的啟發式採購演算法(Generic Drug Pricing Difference Ratio Heuristic Algorithm, GPDR_Alg)，以快速找到最優 GCR 以及學名藥價差比率(Generic Drug Pricing Difference Ratio, GPDR)來最小化 COGs，並與 MILP Model 比較。 3. 在考慮現有庫存數量以及藥品特性的基礎上建立存貨調用的啟發式演算法(Inventory Deployment Heuristic Algorithm, ID_Alg)優化採購需求量。 最後，為驗證方法的實用性，研究將結合真實採購、庫存與處方數據，利用上述三種演算法模型進行數值分析，探索基於 GCR 指標合約的 COGs 最小化方法。

In today's pharmaceutical supply chain management, selecting the right suppliers and accurately forecasting demand are critical for maintaining efficiency and reducing costs. The Generic Compliance Ratio (GCR) is a metric that ties to rebates. It has been utilized to evaluate a pharmacy’s procurement strategy. It helps pharmacies to qualify for rebates by maintaining the favorable GCR. Designing an achievable GCR helps pharmacies to qualify for rebates. Different variations are formulated in practice. Some commonly used variants include: 1. The proportion of the cost of goods(COGs) of Generic Drugs to the COGs of Prescription Drugs 2. The proportion of the COGs of Generic Drugs to the COGs of Branded Drugs This study adopts the second method for calculation and further elaborates on the differences between generic drugs and patented drugs in the pharmaceutical supply chain in subsequent sections. With a GCR based contract, managing GCR to minimizing the COGs makes room for secondary suppliers which leads to our research to 1. Establish a MILP model to find the optimal solution for minimizing COGs 2. Propose a heuristic algorithm(Generic Drug Pricing Difference Ratio Heuristic Algorithm, GPDR_Alg) and compare the result against the MILP model 3. Optimize the procurement demand considering inventory on hand quantities(Inventory Deployment Heuristic Algorithm, ID_Alg) Finally, the study will incorporate real-world inventory and prescription data to optimize procurement needs, and numerical analyses will be conducted using aforementioned three methods.