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An application of OWA operators in fuzzy business diagnosis

Profile image of Valeria SchergerValeria Scherger

2016, Applied Soft Computing

https://doi.org/10.1016/J.ASOC.2016.06.026
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Abstract

The paper aims to develop an adjustment index based on OWA operators to enrich the results of diagnostic fuzzy models of business failure. A proposal to verify the diseases prediction accuracy of the models is also added. This allows a reduction of the map of causes or diseases detected in strategic defined areas. At the same time, these key areas can be disaggregated when an alert indicator is identified, and shows which of the causes need special attention. This application of OWA can encourage the development of suitable computer systems for monitoring companies' problems, warn of failures and facilitate decision-making. In addition, taking Vigier and Terceño's 2008 model as a benchmark, causes aggregation operators are introduced to evaluate alternative groupings, and the adjustment measure using approximate solutions is proposed to test the model's prediction. The empirical estimation and the verification of the improvement proposals in a set of small and medium-sized enterprises (SMEs) in the construction industry are also presented. The functionality and the prediction capacity are thus measured and detected by monitoring key areas that warn about insolvency situations in the firm.

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