Reliability-driven maintenance optimization and operational performance: Empirical evidence from the chemical industry

Reliability and maintenance optimization play a critical role in enhancing operational performance in capital-intensive process industries such as chemicals. This study empirically examines the relationship between equipment reliability, maintenance optimization, and operational outcomes using plant-level data from the chemical industry. Drawing on reliability engineering and operations management theory, the study integrates key reliability indicators, i.e. mean time between failures (MTBF), mean time to repair (MTTR), downtime, and availability—within an optimization-based maintenance framework. Using monthly operational data, the analysis employs paired statistical tests and multivariate regression with robust standard errors to evaluate the performance impact of optimized maintenance strategies relative to a baseline approach. The results indicate that optimized maintenance significantly reduces unplanned downtime by approximately 18% and maintenance costs by 10%, while improving system availability. Regression findings further reveal a statistically significant positive relationship between MTBF and production throughput, confirming that higher equipment reliability enhances operational performance. In contrast, MTTR and unplanned downtime exert significant negative effects on throughput, highlighting the importance of both failure prevention and rapid repair. Model diagnostics confirm strong explanatory power and statistical robustness. The study contributes to the practical perspective by suggesting that maintenance optimization should be viewed as a strategic lever rather than a purely cost-driven function.