FINDING BUSINESS FAILURE REASONS THROUGH A FUZZY MODEL OF DIAGNOSIS

Abstract

The aim of this paper is to find the causes of the failure and diagnose a group of small and medium-sized enterprises (SMEs) through a fuzzy logic model. The application to a specific sector involves the adaptation of the methodological hypothesis presented in the theoretical model and the definition of the variables that interact in the estimation (causes and symptoms). In this approach is proposed the simulation of the fuzzy model to forecast firms' health and find out the reasons that generate diseases. This supposes the use of the aggregate economic financial matrix to simulate the diseases of the set of SMEs in the construction sector and the estimation the causes of the diseases. In the research are presented the most common reasons of failure detected for the sector, the diseases relevant for each firm and a classification of business according to the impact of the causes between healthy and unhealthy firms.Keywords: economic-financial diagnosis, prediction, symptoms and causes, fuzzy relations(ProQuest: ... denotes formulae omitted.)1. INTRODUCTIONIn literature there are very few models which use fuzzy relations to diagnose problems in firms [1] to [5]. In this work is proposed to apply the Vigier and Terceno diagnosis model [1] to analyse the causes that generate problems or diseases in firms. This model based on fuzzy binary relations between causes and symptoms has advantages over other prediction models, understanding the process of business failure and has the capacity to diagnose problems and simulate (or complement) the analyst's task. One of the main results of this model is to forecast firms' health and find out the failure reasons. It provides a different point of view from the traditional models ([6], [7], [8], [9], [10], [11], etc.), incorporating elements of subjectivity and uncertainty formalized by fuzzy logic, and gives solutions (or introduces improvements) concerning most of the methodological problems discussed in the literature1. Due to characteristics of the proposed problem, which uses a large number of qualitative variables or expert analysts' opinions, it is very difficult to find a comprehensive solution using a classical method of resolution.The application of the model is based on the construction of incidence matrices of symptoms and causes and the estimation of an economic-financial knowledge matrix to detect the reasons of failure.2. THE DIAGNOSIS MODELThe model presented by [1] is based on the estimation of an economic-financial knowledge matrix (R) that starts in the estimation of the symptoms-causes matrices. The construction of the matrix R is determined by a group of symptoms S = {Si} , where i = 1,2,...,n, of causes C = {Cj} , where j = 1,2,...,n, p., and of firms E = {Eh} , where h=1,2,3,...,m, in which is possible to identify symptoms and causes.... (1)being,Q = [qhit] = [qiht] : transposed membership matrix of the firms' symptomsP = [phj] : membership matrix of the firms' causesa : fuzzy relations operatorThat is,... (2)where...The matrix R is used for predicting the incidence level of each one of the causes defined in the model2The model recommends that, to perform a study as homogeneous as possible, the set of selected companies (E) be from a region and a specific productive sector and also be composed of healthy and unhealthy companies to detect differences in indicators of both groups. Finally, the set of the years or periods T = {Tk} , where k=1,2,3,...,t, for which the estimate is made must be defined.As mentioned above, each matrix element rij is obtained through the operation between the transposed membership matrix of symptoms and the membership matrix of causes that satisfy the smaller ratio. As the model proposes the determination of possible diseases from the estimate of R, each rij shows the level of incidence between the symptom Si and the cause Cj . …