An Integrated Approach of SAW, TOPSIS, and RAM for Ranking Alternatives: A Case Study in the Food Industry

Multi-Criteria Decision Making (MCDM) stands as a widely employed technique for ranking alternatives and identifying the most suitable option across diverse domains. However, the inherent algorithmic variations among different MCDM methods can lead to discrepancies in the ranking outcomes when applied to the same problem. Consequently, to enhance the reliability of alternative rankings, it is crucial to address the problem using multiple distinct MCDM approaches. This study integrates three prominent methods: Simple Additive Weighting (SAW), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Root Assessment Method (RAM) to concurrently rank alternatives within a representative food-related example, specifically the nutritional value assessment of various nut types. The SAW, TOPSIS, and RAM methodologies were applied to rank eight types of nuts: almond, Brazil nut, cashew, hazelnut, macadamia, pecan, chestnut, and walnut, each characterized by nineteen distinct nutritional attributes. The results demonstrate a consistent identification of the top-ranked nut across all methods. Furthermore, the ranking order of the remaining alternatives exhibited minimal variation among the three approaches. Spearman's rank correlation coefficients were 0.905 between SAW and TOPSIS, 0.929 between SAW and RAM, and 0.976 between TOPSIS and RAM. These findings not only offer valuable guidance for consumers in selecting the optimal nut product, but also provide a clear direction for practitioners to consider the combined application of these three MCDM methods for ranking alternatives in other fields.