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Evaluation of Criteria for the Classification of Enterprises

机译：企业分类的标准评价

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Successful development and growth of enterprises is of great importance for the economic growth of the country, social stability and creation of new work places. However, the enterprise's activity is directly or indirectly influenced by internal and external factors. An effective strategy of companies might be one of the most important factors. Loss-making enterprises can improve their results following the strategy of profitable enterprises, however the question is, which enterprises could be attached to the domain of profitable ones and what classification criteria should be applied. The analysis of special literature has shown that it is not enough to analyze separate activities of enterprises, but the whole system of variables should be analyzed. Cluster analysis is a statistical method, which allows classifying the selected objects into the classes according to their similarity within the class and significant difference between the classes. This classification is based on the analysis of all parameters of the system, so it could be effectively used for the grouping of enterprises into the classes. The main purpose of this investigation was to determine the classification criteria for enterprises according to their Net Profit. Profit (loss) and balance sheet data of 50 profitable and 50 loss-making enterprises during the year 2002-2006 were taken for the investigation from the Lithuanian Department of Statistics. After the first term analysis the financial data which were not characteristic for the majority of the analyzed enterprises were excluded from the investigation as well as the data with outliers. 30 parameters corresponding to the different profit (loss) and balance sheet lines were selected for the further analysis. Collinearity diagnostics of data applying three different methods was performed in the second step. Not correlated parameters were included into regression model. Three profit describing regression equations were composed, variables of which were used for the classification of the enterprises. Hierarchical clusterization methods were used for the classification of enterprises into profitable, loss-making and mixed (all others). Performing cluster analysis the selection of the linkage distance and the classification method was performed first of all. The best linkage distance and the best classification method is that one, according to which most of the profitable and loss-making enterprises match their classes. The investigation has shown that the Ward's method and Euclidian distance were most suitable for the classification. Due to this reason, the enterprises were classified using different variables, which were included into the regression equations. Only independent variables of the regression analysis equation were used in the first case; the independent variables and net profit (dependent variable) were used in the second case and weighted variables of the regression analysis equation were used in the last case. The best result was achieved using independent variables selected by correlation analysis and included into regression analysis equation composed for the evaluation of net profit. In this case 17 profitable and 21 loss-making enterprises matched the classification criteria. Mahalanobis-Taguchi system was used for the evaluation of the differences between profitable and lossmaking enterprises. Present paper discusses the necessity to establish standard set of profitable enterprises and provides a validation of this standard. Performed investigation has shown that the classification of enterprises according to selected variables credibly evaluates performance of the enterprises and makes it possible to classify them into the classes of profitable and loss-making companies.
机译：成功的发展和企业增长对于国家的经济增长，社会稳定和新工作场所的创造具有重要意义。但是，企业的活动直接或间接地受到内部和外部因素的影响。有效的公司可能是最重要的因素之一。亏损企业可以通过盈利企业战略提高其结果，但问题是，哪些企业可以附在有利可图的企业以及应申请的分类标准。对特殊文献的分析表明，分析企业的单独活动是不够的，但应分析整个变量系统。群集分析是一种统计方法，它允许根据其类内的相似性以及类之间的显着差异将所选对象分类为类。该分类基于对系统所有参数的分析，因此可以有效地用于企业分组到课堂上。本调查的主要目的是根据净利润确定企业的分类标准。 2002 - 2006年度在2002 - 2006年期间50次有利可图和50家亏损企业的利润（亏损）和资产负债表数据被采取了立立立人统计部的调查。在第一次术语分析之后，从调查中排除了大多数分析企业的财务数据，以及具有异常值的数据。对应于不同利润（损耗）和平衡板线的30个参数进行进一步分析。在第二步中执行应用三种不同方法的数据的共同性诊断。回归模型中包含不相关的参数。三个利润描述了回归方程，其变量用于企业的分类。分层集群化方法用于企业分类为有利可图，损失和混合（所有其他人）。执行群集分析首先执行连接距离和分类方法的选择。最好的联系距离和最佳分类方法是，根据哪种盈利和损失企业匹配他们的课程。调查表明，病房的方法和欧几里德距离最适合分类。由于这个原因，企业使用不同的变量进行分类，这些变量包含在回归方程中。在第一种情况下仅使用回归分析方程的独立变量;在最后一个情况下使用了独立变量和净利润（依赖变量）和回归分析方程的加权变量。使用通过相关分析选择的独立变量来实现最佳结果，并将其纳入回归分析方程，用于评估净利润。在本案中，17有利可图的和21家损失企业符合分类标准。 Mahalanobis-Taguchi系统用于评估有利可图和失去的企业之间的差异。本文讨论了建立标准盈利企业的必要性，并提供了对本标准的验证。表演调查表明，根据所选变量的企业分类可信地评估企业的表现，并可以将他们分类为盈利和损失公司的课程。

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