Migration and Cutover Strategy for Central Label Management in Clinical Trial Supply Chain

The clinical trial supply chain relies on highly regulated data and processes. This thesis provides a methodology for quality control in complex, high-risk, zero-defects, and human-driven data migration projects. This work concerns the Good Manufacturing Practices (GMP) regulated data migration of Investigational Medicinal Product (IMP) labels from a legacy Enterprise Resource Planning (ERP) system to the new Central Label Management (CLM) software. The upcoming retirement of the old ERP system presented two threats: failing to migrate the label data and the potential for data integrity loss of the migrated data. The objective was to develop a robust migration and cutover strategy to ensure 100% data correctness and correspondence against the IMP data. Failure Modes and Effects Analysis (FMEA) was conducted to predict and mitigate threats to the migration process. Key performance indicators (KPIs) were defined to monitor process quality and ensure to meet the migration requirements (high quality levels, cost and time efficiency). This work’s analysis compares two quality control methodologies: statistical sampling (Acceptable Quality Level, AQL) and 100% data checking. The analysis concludes and proves that AQL is insufficient for this context as it is designed for stable manufacturing processes, not for a high complexity, with non-statistically random, human driven error patterns. The identified solution is a hybrid two-stage migration strategy. This methodology leverages the efficiency of AQL in establishing ‘pre-screen’ controls, batches that pass this initial check undergo a mandatory 100% data checking and reconciliation. This approach optimizes resources and provides immediate feedback to the migration team, while still maintaining a zero-defect standard. The KPI evaluation confirmed this strategy’s success. All non-conformities were captured internally, and no supply chain disruptions or patient impact occurred. This thesis concludes by proposing future optimizations, including a migration prioritization model to reduce the amount of data to migrate and a refinement of the label-IMP mapping algorithm to increase data reconciliation accuracy.