<p>UniLasso and the smoothly clipped absolute deviation (SCAD) penalty method are compared for identifying active factors in two-level supersaturated designs. A two-stage UniLasso procedure is proposed in which an initial UniLasso fit serves as a screening step and the selected factors are subsequently refined by stepwise forward selection to improve model parsimony. Using benchmark simulation settings previously employed in the literature, the methods are evaluated under varying levels of sparsity and heterogeneous effect sizes. Across a wide range of scenarios, UniLasso exhibits stronger screening performance and frequently achieves higher true-model recovery rates than SCAD, particularly in more challenging settings involving larger numbers of active factors and smaller effects. Although the initial UniLasso stage tends to select larger models, the proposed refinement substantially reduces model size while retaining most of the identification advantage. Analyses of several real-data examples show that the two-stage procedure generally yields more stable and parsimonious models than SCAD, while also illustrating the trade-off between recovering active effects and maintaining model parsimony. Overall, the results suggest that second-stage refinement substantially improves the practical utility of UniLasso.</p>