Beyond Point Prediction: A Machine-Learning Framework for Engineer-to-Order Quotation Review in Industrial Valve Manufacturing

Engineer-to-order (ETO) quotation is commonly treated as point regression, although historical final quotations are enterprise-specific commercial outcomes formed under partial observability. This study develops a machine-learning framework centered on quotation review that separates central quotation benchmarking, configuration-level generalization, conditional quotation dispersion, and order-level diagnostic evidence. Using 15,000 industrial-valve quotations represented by six engineering variables, we identified 1859 unique configurations; 96.53% of orders in a conventional random test split had an exact configuration already present in training. XGBoost achieved 3.96% MAPE on the random hold-out and 4.13% ± 0.08% under configuration-grouped five-fold validation. Controlled historical-retrieval baselines achieved 16.11% ± 1.20% MAPE for HAQ and 10.14% ± 1.42% for OAQ under configuration-grouped validation, while XGBoost remained at 4.13% ± 0.08%. After non-crossing re-sorting of the predicted quantiles, configuration-grouped QR-XGBoost retained a Q0.50 MAPE of 4.17% ± 0.11% and nominal 80% coverage of 73.18% ± 1.40%. SHAP analysis identified material, nominal diameter, and pressure rating as dominant model-attributed factors. The framework therefore provides inspectable prediction, conditional-range, and attribution outputs that may support quotation review, without claiming autonomous pricing, causal cost attribution, or demonstrated human-review benefit.