An application of OWA operators in fuzzy business diagnosis

Display Omitted Ordered weighted average (OWA) operator is introduced to enrich the results of diagnostic fuzzy models of business failure.The approach allows synthesizing firms diseases through the reduction of the general map of causes into key areas.This application can encourage the development of computer systems for monitoring firms problems and alert failures.An empirical estimation applied to a set of small and medium-sized enterprises (SMEs) with good results is presented. 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 Terceos 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 models 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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