This paper illustrates two approaches for the mobile manipulation of factory robots using deep neural networks. The networks are trained using synthetic datasets unique to the factory environment. Approach I uses depth and red-green-blue (RGB) images of objects for its convolutional neural network (CNN) and Approach II uses computer-aided design models of the objects with RGB images for a deep object pose estimation (DOPE) network and perspective-n-point (PnP) algorithm. Both the approaches are compared based on their complexity, required resources for training, robustness, pose estimation accuracy, and run-time characteristics. Recommendations of which approach is suitable under what circumstances are provided. Finally, the most suitable approach is implemented on a real mobile factory robot in order to execute a series of manipulation tasks and validate the approach.