In contemporary drug supply chains, the ability to assess patron profitability over time has become a strategic necessity for ensuring operational efficiency and financial sustainability. The increasing complexity of pharmaceutical logistics, coupled with the integration of digital technologies, demands robust analytical frameworks that can capture customer behavior and translate it into actionable insights. This study investigates the application of the Recency-Frequency-Monetary (RFM) approach as a structured methodology for evaluating long-term patron profitability within drug distribution networks.

The research adopts a conceptual and analytical approach, integrating RFM segmentation with supply chain management principles and digital healthcare innovations. It explores how transactional data, combined with inventory management systems, mobile health technologies, and decision-support frameworks, can enhance the predictive accuracy of profitability assessments. The study also considers the role of logistics efficiency, network transparency, and technological adoption in influencing customer value.

Findings indicate that RFM-based segmentation provides a reliable mechanism for distinguishing high-value patrons from low-value segments, particularly when integrated with advanced supply chain analytics and digital platforms. The study reveals that inventory optimization, reverse logistics strategies, and real-time data systems significantly impact customer retention and profitability patterns. Furthermore, the integration of mobile health applications and RFID-enabled logistics enhances data visibility, thereby improving decision-making processes.

The research contributes to the existing literature by bridging customer analytics with pharmaceutical supply chain management, offering a multidisciplinary framework for evaluating patron profitability. It highlights the importance of aligning analytical models with operational and technological capabilities. Limitations include the absence of empirical validation and the reliance on conceptual synthesis, suggesting future research directions involving data-driven implementation and machine learning integration.

Overall, the study provides a comprehensive framework for drug supply chain organizations to optimize customer value assessment, improve resource allocation, and achieve sustainable competitive advantage in a rapidly evolving healthcare environment.

Customer Profitability, RFM Model, Drug Supply Chain, Inventory Management, Predictive Analytics, Healthcare Logistics, Reverse Logistics, Digital Health, Supply Chain Optimization

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