The role of calibration engineering in strengthening reliability of advanced manufacturing systems through artificial intelligence

Abstract

AI initiatives promise improved availability, yield, and effective capacity, their actual impact is often limited by weakly calibrated measurement systems and unmanaged uncertainty. In addition to outlining the organizational and data circumstances required to realize these benefits, the study seeks to quantify the relationship between AI-enabled calibration engineering and plant-level reliability. We use a quantitative cross-sectional case-based design using eight manufacturing companies in the United States and the cloud and on-premise operational data sources that are connected to them. Descriptive profiling, correlation matrices, multiple linear regressions with robust errors and site fixed effects, moderation tests, and sensitivity checks are all included in the study plan. Our main results show that dependability and AI-enabled calibration techniques are positively correlated; this link gets stronger with increasing data quality and training levels but gets weaker as equipment ages. For managers, they should institutionalize calibration metadata and uncertainty budgets as machine-readable context, enforce data ingestion gates for decision-grade information, and develop capabilities that integrate metrology governance with targeted training.