Manufacturing industries contribute to 16.7\% of global CO2 emissions. Any waste generated during manufacturing processes such as defective or rejected products due to poor tolerances or quality issues, results in excessive energy consumption and unnecessary carbon emission. Most manufacturing waste and quality fluctuations arise from uncertainty and variation in raw materials and process conditions. Moreover, manufacturing with recycled materials introduces challenges related to quality control. For this purpose, uncertainty quantification via surrogate models and robust optimization are among the promising methods to reduce waste during manufacturing processes. Inverse robust optimization, also known as tailored scatter, tailored variation, or tailored uncertainty is a recently-introduced method that explores designing uncertainty for a given robust performance. These inverse problems face computational complexities due to process non-linearities, correlations and problem dimensionality. In this work, an efficient implementation of robust optimization using Robustimizer software is applied and its potential for tailored variation is presented. An additive manufacturing process, laser powder bed fusion, is used to demonstrate the potential of presented methods in increasing efficiency, and reducing environmental footprints in a more accurate and computationally efficient way. Robust process settings are achieved leading to minimal variation in melt pool size in the presence of uncertainties of material and process. In addition, the tailored scatter approach is implemented to provide a methodology for tighter control of noise variables in the laser powder bed fusion process considering uncertainties.