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Measurement of operator quality performance has been challenging in theconstruction fabrication industry. Among various causes, the learning effect isa significant factor, which needs to be incorporated in achieving a reliableoperator quality performance analysis. This research aims to enhance apreviously developed operator quality performance measurement approach byincorporating the learning effect (i.e., work experience). To achieve thisgoal, the Plateau learning model is selected to quantitatively represent therelationship between quality performance and work experience through abeta-binomial regression approach. Based on this relationship, an informativeprior determination approach, which incorporates operator work experienceinformation, is developed to enhance the previous Bayesian-based operatorquality performance measurement. Academically, this research provides asystematic approach to derive Bayesian informative priors through integratingmulti-source information. Practically, the proposed approach reliably measuresoperator quality performance in fabrication quality control processes.

Enhanced Welding Operator Quality Performance Measurement: Work Experience-Integrated Bayesian Prior Determination