<p>A novel data-driven decision-making technique is proposed in this research, the Grey Integrated Decision Analytics (GIDA) and two practical problems in manufacturing and supply chain environments were solved using the proposed methodology. The technique uses two input matrices, the pairwise comparison among attributes, and the situational effects of alternatives, considering different objectives and can be implemented for any multi-criteria decision-making problem with lesser computational complexity. Two practical problems, one in the supply chain environment (the problem of resilient supplier selection) and the other in the manufacturing environment (the problem of tool material selection) were solved to illustrate the implementation of the GIDA methodology, and the alternatives were prioritized. Researchers and practitioners can use the proposed methodology for solving different multi-criteria decision-making problems, particularly in manufacturing and supply chain-related studies. The method encourages the need for a composite score constructed based on the proposed bulls-eye distances and the decreasing order of scores shows the increasing order of selection preferences, where practitioners can easily make decisions. The proposed GIDA model is able to incorporate a large number of input responses and uses the distance concepts from ideal conditions, to obtain effect vectors for effectually solving data-driven decision-making environments.</p>