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There are so many paper shredder products available in the market, and the decision to select a 'right shredder' usually involves a number of criteria. For organizations, complexity arises when the procurement function is to purchase a massive amount of shredders of the same type. This study initiated the idea to use the analytic hierarchy process with graph theory and matrix approach for solving the problem. The proposed model determines the best shredder from a pool of alternatives, given the buyer-side decision-maker's preference settings. With the expert questionnaire polled and the heterogeneous real data collected, the model is applied to a reduced data set. The size of the decision problem is defined as 8 alternatives are filtered among 26, while the 7 justification attributes considered are fully kept for not losing the experimental meaning. The result shows the effectiveness and applicability of the approach to manage the encountered decision scientifically. As this also implies that not only buyers but also the manufacturers can use this model to analyse 'something', it is confident to conduct more future studies. The R script which implements the dynamic programming concept to calculate the assessed index scores for graph theory and matrix approach is perhaps another contribution of this study.

This study determines the effectiveness of intuitionistic-fuzzy multi-attribute decision-making (IF-MADM) for making group decisions in practice. The effectiveness of the method is measured in terms of four dimensions: applicability, efficacy, efficiency and informativeness. To measure the efficacy, an IF-MADM model that has been recently proposed, AHP and the TOPSIS approach, which are compensatory models for group MADM, are used to model and solve the same collective decision. Using non-parametric statistical tests for data analytics, a ‘similarity confirmation method’ is proposed for a pair-wise test. This is to determine whether the score vectors are similar. Score vectors are used to determine the final ordinal ranks and whose scales differ greatly for different MADM methods. Since the latter two MADM models are both trustworthy with a known range of applications, any similarity in the results verifies the efficacy of IF-MADM. Using this process, the applicability of IF-MADM modelling is demonstrated. The efficiency and informativeness are also benchmarked and justified in terms of the model’s ability to produce a more informed decision. These results are of interest to practitioners for the selection and application of MADM models. Finally, the selection of a senior centre, which is a real group decision problem, is used to illustrate these. This extends the empirical application of IF-MADM, as relatively few studies practically compare issues for IF-MADM with those for other MADM models. The study also supports a rarely studied non-clinical healthcare decision that is relevant because there are many aging societies.

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The AHP–GTMA (analytic hierarchy process and graph theory and matrix approach) has been applied to select the best paper shredder before a company was making a bulk purchase order. However, there is a question as to whether one such relatively recent approach is effective to aid the selection decision problems in industrial/commercial practice. In this paper, a novel multi-measure, rank-based comparative research flow is proposed. The real decision problem case mentioned above is solved using the AHP–GTMA and the AHP–TOPSIS methods, respectively, with relevant datasets sourced. Several measures in the proposed flow, i.e., the arithmetical, geometrical, or even statistical ones, are multiplexed and used to validate the similarity between the rank order vectors (ROVs) (and thus between the final preferential orders determined over the alternatives) that are obtained using these two different methods. While AHP–TOPSIS is a confident multi-attribute decision-making (MADM) approach which has been successfully applied to many other fields, the similarity validated between these individual results using the proposed method is used to confirm the efficacy of the AHP–GTMA approach and to determine its applicability in practice. In addition, along with this study, some contributable points are also rendered for implementing the decision models, e.g., the optimized recursive implementation in R to compute the permanent value of a square ASAM (alternative selection attribute matrix, which is the computational basis required by AHP–GTMA) of any dimension. The proposed methodological flow to confirm the similarity based on the ordinal rank information is not only convenient in operational practice with ubiquitous tool supports (e.g., the vector-based R statistical platform), but also generalizable (to verify between another pair of results obtained using any other MADM methods). This gives options for future research.