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Frequently, management decision making problems involve multiple criteria/objectives/attributes.  Over the years, many quantitative methods have been developed to facilitate making rational decisions involving multiple criteria. The Analytic Hierarchy Process (AHP) is, in general, regarded as one of the most successful techniques to solve decision making problems involving multiple criteria. In AHP, the decision maker starts by constructing the overall hierarchy of the decision problem.  The hierarchy consists of criteria, subcriteria and alternatives of the decision making problem. A number of pairwise comparison matrices are formed in order to derive weights of the criteria and the local weights of the alternatives. Subsequently, the principle of hierarchical composition is used to determine the global weights of the alternatives. The alternative with the highest global weight is selected as the best alternative. The drawback of the traditional AHP is that it requires a large number of pairwaise comparisons, especially in the presence of a large number of criteria. The present empirical study attempts to investigate the possibility of eliminating insignificant criteria in order to reduce AHP computational time. Using the Expert Choice software, findings confirm that criteria that carry comparatively lesser weights can be excluded from the hierarchy and thereby the total time required for making the pairwise comparisons can be reduced drastically.  To solve large-scale enterprise multi-criteria decision making problems (that involve large number of criteria) by AHP, it is proposed that at the very outset, decision makers can apply nominal group technique to identify the insignificant criteria. These criteria can be dropped from subsequent analysis and this exclusion will not affect the final decision significantly. This proposed methodology is expected to enhance the applicability of AHP in solving various kinds of larger sized multi-criteria decision making problems in any enterprise.

Management decision-making by the analytic hierarchy process: a proposed modification for large-scale problems