This study focuses on developing and demonstrating a straightforward workflow for identifying pathways to increase green part density in binder jetting additive manufacturing (BJAM) using statistically driven process maps. The workflow was applied to investigate the effects of process parameters toward improving green part density, with a direct application in manufacturing of Fe-Si components. Specifically, a half-factorial experimental design was used to study the effects of four key parameters—layer thickness, powder spreading speed, roller rotational speed, and binder saturation—on Fe-Si spherical powder with D50 of 32.40 µm. Relative bulk density was estimated via three methods: geometrical and mass measurements, the Archimedes test, and CT imaging. The study discusses relative bulk density as well as localized density variation in the printed parts, which is attributed to both parameter selection and inherent process variability. A regression analysis was used to reveal the significance of main effects and second-order interactions. The regression model (R2 = 0.915) was used to derive an expression for green density as a function of the parameters and had a prediction error of 0.96%. Based on the regression model, an optimized set of parameters was obtained that would maximize green density up to 57.96% for the machine and material system.