What if passing the test is not enough? In reliability engineering, we often place a great deal of confidence in laboratory validation. We test, measure, analyze, and document. And when the product passes, we naturally assume it is ready for the field. But what happens when a product passes the lab test and still fails in the hands of the customer? Today, we are going to explore the gap between laboratory reliability testing and actual field performance. It is a gap that can be costly, frustrating, and sometimes difficult to explain. Products can perform well under controlled test conditions, yet still experience unexpected failures when exposed to real users, real environments, real service conditions, and real-world variability. Mike Konrad's guest is Dr. Nishith Kumar Reddy Gorla of CORE ITS LLC. Dr. Gorla’s work focuses on system-level reliability testing, field reliability alignment, reliability growth, and test-to-field correlation for complex products. His recent paper, “System-Level Test Case Design for Field Reliability Alignment in Complex Products,” looks at why traditional reliability test methods may miss important field failure modes. More importantly, it proposes a broader approach to test case design that considers not just the product design, but also actual use conditions, field failure data, end-user behavior, service procedures, manufacturing variation, consumables, and product interfaces. This is an important conversation because reliability is not simply about passing a test. It is about understanding how a product behaves in the real world, where variables interact, users behave unpredictably, environments change, and failure mechanisms may not appear until the system is challenged in realistic ways. If you design, build, test, specify, or depend on complex products, this discussion should be especially relevant. Today, we will talk about why products sometimes pass in the lab but fail in the field, how better system-level test cases can improve field correlation, and what reliability professionals can do to create test methods that more accurately reflect real-world performance.
