Industrial System Evaluation Model for Process Efficiency, Reliability, and Output Quality Measurement

Abstract

Industrial systems require integrated performance evaluation because production outcomes are shaped by the interaction between process efficiency, equipment reliability, and output quality. Conventional performance measurement often isolates productivity, downtime, or defect indicators, which limits the ability to diagnose system-level operational readiness. This study designed and tested an Industrial System Evaluation Model to measure process efficiency, production reliability, and output quality using a panel data approach. A balanced simulated panel dataset was developed from 10 production lines observed over 12 monthly periods, producing 120 line-month observations. The model integrated three dimensions: Process Efficiency Score, Production Reliability Score, and Output Quality Score. Fixed-effects and random-effects panel regressions were estimated to evaluate the relationship between operational indicators and the composite Industrial System Evaluation Index. The Hausman test, variance inflation diagnostics, robustness checks, and sensitivity analysis were used for validation. Results: The optimized production-line group achieved the highest mean evaluation score of 82.46, compared with 69.38 for the baseline group and 58.72 for the constrained group. Fixed-effects estimation showed that throughput achievement, cycle-time adherence, machine availability, mean time between failure, first-pass yield, and defect reduction significantly improved the composite index. Mean time to repair and unplanned downtime had significant negative effects. The proposed model provides a replicable panel-data-based framework for evaluating industrial systems as integrated operational systems rather than as separate efficiency, reliability, or quality domains.