During the implementations of enterprise resource planning (ERP) systems, most companies have experienced some problems, one of which is how to determine the best ERP software satisfying their needs and expectations. Because improperly selected ERP software may have an impact on the time required, and the costs and market share of a company, selecting the best desirable ERP software has been the most critical problem for a long time. On the other hand, selecting ERP software is a multiple-criteria decision-making (MCDM) problem, and in the literature, many methods have been introduced to evaluate this kind of problem, one of which is the analytic hierarchy process (AHP), which has been widely used in MCDM selection problems. However, in this paper, we use a fuzzy extension of an analytic network process (ANP), a more general form of AHP, which uses uncertain human preferences as input information in the decision-making process, because the AHP cannot accommodate the variety of interactions, dependencies, and feedback between higher-and lower-level elements. Instead of using the classical eigenvector prioritization method in the AHP, only employed in the prioritization stage of ANP, a fuzzy-logic method providing more accuracy on judgements is applied. The resulting fuzzy ANP enhances the potential of the conventional ANP for dealing with imprecise and uncertain human comparison judgements. In short, in this paper, an intelligent approach to ERP software selection through a fuzzy ANP is proposed by taking into consideration quantitative and qualitative elements to evaluate ERP software alternatives.
In this study, we utilize analytic network process (ANP), a more general form of AHP, for justifying stand-alone machine tools out of available alternatives in market due to the fact that AHP cannot accommodate the variety of interactions, dependencies and feedback between higher and lower level elements. However, due to the vagueness and uncertainty on judgments of a decision-maker, the crisp pair wise comparison in the conventional ANP seems to be insufficient and imprecise to capture the right judgments of the decision-maker. That is why, also in this paper, fuzzy number logic is introduced in the pair wise comparison of ANP to make up for this deficiency in the ANP. In short, here, an intelligent approach to machine tool selection (MTS) problem through fuzzy ANP is proposed to improve the imprecise ranking of company's requirements which is based on the conventional ANP. In order to reach to final solution, a preference ratio (PR) analysis is done by using the results of the fuzzy ANP, and investment costs of alternatives. In addition, a numerical example is presented to illustrate the proposed approach
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