Аңдатпа
A BSTRACT Background: Pharmaceutical supply-flow management requires reliable mechanisms for monitoring product availability, quality assurance, logistics performance, regulatory compliance, and supply-chain vulnerability. Digital technologies and open data provide opportunities to strengthen pharmaceutical supply monitoring; however, open data sources are heterogeneous and do not fully represent internal pharmaceutical supply-chain operations. Objective: This study aimed to develop and demonstrate a reproducible, GxP-informed open-data analytical framework for pharmaceutical supply-flow monitoring and to clarify how external regulatory, trade, logistics, product-qualification, procurement, and clinical-innovation signals can be converted into decision-support indicators. Materials and Methods: A secondary open-data analytical design was applied. Publicly accessible datasets were extracted and processed using Python. The framework integrated open regulatory, product qualification, clinical innovation, procurement, utilization, trade-flow, country-level digital infrastructure, and logistics performance data. Exact data-source categories, API endpoints or access routes, query parameters, access dates, preprocessing rules, and record counts were documented in a reproducibility log. A composite digital-logistics readiness score was calculated using normalized electricity access, internet use, and Logistics Performance Index indicators; the score was evaluated through descriptive statistics, Spearman correlation, sensitivity checks, multiple linear regression with heteroscedasticity-robust standard errors, principal component analysis, internal consistency assessment, clustering, and concentration-risk analysis. Results: The framework produced a modular monitoring model linking external open-data signals to supply-flow risk domains. Country-level analysis identified substantial variation in digital logistics readiness; Singapore, Denmark, Finland, Switzerland, the Netherlands, and the United Arab Emirates showed the highest scores. Spearman correlation analysis showed positive associations among electricity access, internet use, logistics performance, and the composite readiness score. The exploratory regression model explained approximately 41% of the variation in logistics performance, while sensitivity analyses showed high rank stability for alternative weighting schemes. Concentration-risk analysis identified higher concentration in selected procurement and product-qualification domains. World Health Organization Prequalification data showed that prequalified pharmaceutical products were concentrated mainly in human immunodeficiency virus/acquired immunodeficiency syndrome, tuberculosis, malaria, and reproductive health. Conclusion: Open data can support a reproducible and transparent external monitoring layer for pharmaceutical supply-flow management. The proposed framework is not a substitute for validated enterprise resource planning, warehouse management system, quality management system, serialization, pharmacovigilance, or formal GxP compliance systems; rather, it is a GxP-informed decision-support layer that can guide early-stage risk mapping, supplier and product qualification review, procurement vulnerability assessment, digital-readiness planning, and quality-risk triage.