This research introduces an integrated decision-support framework for make-or-buy decisions by combining intuitionistic fuzzy sets with grey relational analysis. Since make-or-buy selection involves multiple evaluation criteria and substantial uncertainty in expert judgments, a single decision technique may not adequately capture all aspects of the problem. Therefore, intuitionistic fuzzy sets are employed to represent uncertainty in the evaluation process, whereas grey relational analysis is utilized to rank the available alternatives. To further demonstrate the robustness of the proposed framework, its performance is compared with the results obtained from well-established MCDM techniques. The proposed model was further validated by comparing its outcomes with those generated using the TOPSIS and WASPAS methods, both of which are extensively employed in MCDM studies.
Make-or-buy decisions are strategically important in manufacturing industries due to their direct impact on cost control, resource allocation, and competitiveness. The aim of this study is to add a new one to the multi-criteria decision-making methods by using more than one type of uncertainty and to apply this method in an area that has never been used before in the literature. This method is applied to a real-world make-or-buy decision problem in the metal manufacturing sector, where selecting the optimal sourcing strategy has a direct impact on operational efficiency and cost control. In this study, an extension of the technique developed for ordering preferences by similarity to the ideal solution.
The results show that this integrated approach is effective in handling uncertainty and can be a valuable decision-support tool for practitioners dealing with complex procurement or sourcing decisions in real-world industrial settings. The proposed method ranked “full out-sourcing” as the most suitable option, with a grey relational grade of 0.798, indicating strong alignment with the selected criteria under uncertainty. Among the three evaluated alternatives, full outsourcing achieved the highest grey relational grade (0.798), while hybrid and in-house production scored 0.691 and 0.598, respectively. These findings suggest that the proposed integrated approach can be used effectively for complex sourcing decisions in uncertain environments across different industries.
The novelty of this study is that instead of dealing with the whole decision-making process with the same uncertainty theory, this work employs the most appropriate uncertainty methods at different decision stages. In addition, this study is the first of its kind in which the grey theory and fuzzy set theory are used together in make-or-buy decision as far as our literature review demonstrates. So, the contributions of the study to the literature is using the different uncertainty kinds in multicriteria decision making problems.