In this paper, we present two distinct neural network-based pose estimation approaches for mobile manipulation in factory environments. Synthetic datasets, unique to the factory setting, are created for neural network training in each approach. Approach I uses a CNN in conjunction with RBG and depth images. Approach II uses the DOPE network along with RGB images, CAD dimensions of the objects of interest, and the PnP algorithm. Each approach is evaluated and compared across pipeline complexity, dataset preparation resources, robustness, platform and run-time resources, and pose accuracy for manipulation planning. Finally, recommendations for when to use each method are provided.