Several artificial intelligence (AI) technologies have been applied to assist in the selection of suitable three-dimensional (3D) printing facilities in ubiquitous manufacturing (UM). However, AI applications in this field may not be easily understood or communicated with, especially for decision-makers without relevant background knowledge, hindering the widespread acceptance of such applications. Explainable AI (XAI) has been proposed to address this problem. This study first reviews existing XAI techniques to explain AI applications in selecting suitable 3D printing facilities in UM. This study addresses the deficiencies of existing XAI applications by proposing four new XAI techniques: (1) a gradient bar chart with baseline, (2) a group gradient bar chart, (3) a manually adjustable gradient bar chart, and (4) a bidirectional scatterplot. The proposed methodology was applied to a case in the literature to demonstrate its effectiveness. The bidirectional scatterplot results from the experiment demonstrated the suitability of the 3D printing facilities in terms of their proximity. Furthermore, manually adjustable gradient bars increased the effectiveness of the AI application by decision-makers subjectively adjusting the derived weights. Furthermore, only the proposed methodology fulfilled most requirements for an effective XAI tool in this AI application.