System-Level Test Case Design for Field Reliability Alignment in Complex Products

Achieving targeted reliability for complex products in real-world field environments remains a persistent challenge, even when laboratory validation suggests high performance. A significant reliability gap often emerges during the initial deployment phase, typically within the first one to five years where field failure rates can be up to twice those predicted in controlled settings. Compounding this issue is the limited correlation between failure modes observed in the field and those anticipated during lab testing, with studies indicating only 50–60% alignment. These discrepancies result in unforeseen operational costs, elevated warranty claims, and reduced customer satisfaction. This paper investigates the root causes of the disconnect between laboratory predictions and field performance, proposing a comprehensive framework to improve reliability demonstration and failure mode correlation. The framework introduces a closed-loop reliability correlation system that integrates diverse data sources and feedback mechanisms to achieve up to 95% alignment between lab and field failure modes. The proposed methodology builds upon traditional DFMEA practices by incorporating Function Block Diagrams (FBD), Interface Matrix (IM), Parameter (P-) Diagrams, and field failure trend analysis. It expands the scope of reliability assessment to include actual usage conditions, patterns, and stakeholder interactions shifting from an engineer-centric view to a holistic, user-centered approach. Internal component-level data remains consistent, but the enriched context enables deeper insights into real-world performance. By embedding these multidimensional analyses into system-level test case design, the framework ensures comprehensive coverage of critical variables, noise factors, and interaction effects. This results in more representative simulations, improved predictive accuracy, and early identification of latent failure modes. Ultimately, the proposed approach bridges the gap between laboratory and field environments, enhancing reliability metrics, and enabling proactive mitigation strategies that align with operational realities.