О ДВУХ ПОДХОДАХ К КЛАСТЕРИЗАЦИИ ПРОМЫШЛЕННЫХ ПРЕДПРИЯТИЙ С ИСПОЛЬЗОВАНИЕМ МЕТОДА ОБОЛОЧЕЧНОГО АНАЛИЗА ДАННЫХ
Initially proposed by Charnes, Cooper and Rhodes as a method for comparative efficiency assessment, Data Envelopment Analysis (DEA) eventually got an alternative use. Researchers suggested ways to use it to group (cluster) objects not by the level of their efficiency, but by other parameters, which, from the computational point of view, were secondary results of applying DEA determining the mode used by the object to gain efficiency. The need for such an approach is dictated by two research objectives in strategic management, requiring clustering companies as objects of analysis. First, as companies follow different lines of behavior, finding stable patterns of their actions, and explaining and predicting their behavior is possible only when companies are broken into homogeneous groups. Second, comparative assessment of companies’ success is also possible only within homogeneous groups, because changes in such indicators as unit costs, market share, sales per employee and other similar measures may be assessed quite differently depending on whether the company in question is aspiring to gain the wide market through cost leadership or is following an alternative pathway. Authors undertake a comparative analysis of the two approaches to clustering production facilities based on DEA results. Po, Guh and Yang suggested combining in the same cluster objects with the same production function, when isoquants are determined by the production probability area. Alternative methods based on application of standard clustering procedures to DEA results have been proposed by Kao and Hung, and later by Volkova, Filinov, Titova, Kuskova, Gorny and Nikolaeva. Theoretical analysis and computational experiments show that both approaches (based on finding the edges of the production probability area and based on application of standard clustering procedures to DEA results) yield similar results under certain circumstances but differ in the opportunities offered to the researcher in substantive interpreting of the groups created and performing alternative calculations with the changing number of clusters (groups).
One of the goals of the first edition of this book back in 2005 was to present a coherent theory for K-Means partitioning and Ward hierarchical clustering. This theory leads to effective data pre-processing options, clustering algorithms and interpretation aids, as well as to firm relations to other areas of data analysis. The goal of this second edition is to consolidate, strengthen and extend this island of understanding in the light of recent developments. Moreover, the material on validation and interpretation of clusters is updated with a system better reflecting the current state of the art and with our recent ``lifting in taxonomies'' approach. The structure of the book has been streamlined by adding two Chapters: ``Similarity Clustering'' and ``Validation and Interpretation'', while removing two chapters: ``Different Clustering Approaches'' and ``General Issues.'' The Chapter on Mathematics of the data recovery approach, in a much extended version, almost doubled in size, now concludes the book. Parts of the removed chapters are integrated within the new structure. The change has added a hundred pages and a couple of dozen examples to the text and, in fact, transformed it into a different species of a book. In the first edition, the book had a Russian doll structure, with a core and a couple of nested shells around. Now it is a linear structure presentation of the data recovery clustering.
We present a complex analysis of business models for large, medium and small Russian commercial banks from 2006 to 2009. The Russian banks are grouped based on homogeneity criteria of their financial and operational outcomes. The banks’ structure of assets and liabilities, profitability and liquidity ratio are taken into account. The results show how the banks are adjusted their business models before and after the financial turmoil taken place in 2008. In addition, the prevailing banking business models observed for the leading banks in Russia are defined. The banks often changing their business models are found and analyzed.
Increased attention and focus has been laid on the strategic importance of intellectual capital for modern management. However, intangible resources appear difficult to measure. Today, there are several methods, both financial and nonfinancial ones that allow managing them, to provide benchmarking and analyze its value added function (Sveiby, 2007). The rare investigations of intellectual capital in Russian enterprises show that “Almost in all industries it is still more profitable to invest in tangible assets rather than in intangible ones” (Volkov, Garanina, 2007). Still, some investigations on the micro level show that there are enterprises with high level of technological capital and innovative activity. The researchers called them “innovative leaders” and empirically proved that they have high labour productivity and are awarded by market through extra profit (Gonchar et al., 2010). Using the research sample and Pulic’s Value Added Intellectual Coefficient (VAIC™) the authors investigate empirically the dynamics and structure of VAIC, and study the relation between the intellectual capital and indicators of organizational performance, such as labour productivity, sales growth and profitability. Additionally, the VAIC™ model allows analysing the role of human, structural and physical capital. This paper outlines the study based on 350 Russian industrial enterprises’ annual statistical and account reports from 2005 through 2007. Besides, the authors adopt the VAIC calculation according to the Russian accounting system’s specifications and limitations. The findings support the hypothesis that a company’s intellectual capital influences favourably the organizational performance, and may indicate future competitiveness. A proof showing that the explanatory power of models is higher when considering the additional variables such as investment in fixed capital, R&D expenditures and a company’s size is represented. The results extend the understanding of the intellectual capital role in creation of sustainable advantages for companies in developing economies where different technological advancements may bring different implications for organizational value creation efficiency.
Data Correcting Algorithms in Combinatorial Optimization focuses on algorithmic applications of the well known polynomially solvable special cases of computationally intractable problems. The purpose of this text is to design practically efficient algorithms for solving wide classes of combinatorial optimization problems. Researches, students and engineers will benefit from new bounds and branching rules in development efficient branch-and-bound type computational algorithms. This book examines applications for solving the Traveling Salesman Problem and its variations, Maximum Weight Independent Set Problem, Different Classes of Allocation and Cluster Analysis as well as some classes of Scheduling Problems. Data Correcting Algorithms in Combinatorial Optimization introduces the data correcting approach to algorithms which provide an answer to the following questions: how to construct a bound to the original intractable problem and find which element of the corrected instance one should branch such that the total size of search tree will be minimized. The PC time needed for solving intractable problems will be adjusted with the requirements for solving real world problems.
For the development of technological innovations it is essential to ensure competent and modern commercialization within the framework of balanced business models. Multifactor cluster analysis of business models of contemporary high-technology companies and industries shows that the most effective commercialization emanate in the framework of four basic models. Company's profitability does not depend directly on the level of its technologies, but is determined by the quality of these business models. Besides trends in high-technology industries demonstrate raising segmentation and differentiation of markets and more frequent utilization of value network models.
The analysis of region differentiation of microentrepreneurship development and indexes of judicial statistics based on the current data of statistical recording are given in the article. The capabilities of cluster analysis for revelation of typological groups of the Russian region depending on the level of entrepreneurial activities and the results of law enforcement practice are represented.
This paper presents a pattern behavioral analysis of 100 largest Russian commercial banks by total assets during an eight- year period: from the first quarter of 1999 to the second quarter of 2007. Bank performance indicators are analyzed. Structural similarities in the development of the banks are examined. A cluster analysis is applied to determine banks with a similar structure of operations. This analysis allows to estimate how the structure of the Russian banking system has been changing over time. In particular, it allows to identify prevailing patterns in the behavior of Russian commercial banks and to analyze the stability of their position in a particular pattern.
How seriously does the degree of trust in basic social and political institutions for people from different countries depend on their individual characteristics? To answer this question, three types of models have been estimated using the data of the fifth wave of the World Value Survey: the first one based on the assumption about a generalized relationship for all countries, the second one taking into account heterogeneity of countries (using introduction of the country-level variables), the third type applying a preliminary subdivision of countries into five clusters. The obtained results have been used for suggestion of possible actions to increase public confidence in the basic institutions.
The paper examines the structure, governance, and balance sheets of state-controlled banks in Russia, which accounted for over 55 percent of the total assets in the country's banking system in early 2012. The author offers a credible estimate of the size of the country's state banking sector by including banks that are indirectly owned by public organizations. Contrary to some predictions based on the theoretical literature on economic transition, he explains the relatively high profitability and efficiency of Russian state-controlled banks by pointing to their competitive position in such functions as acquisition and disposal of assets on behalf of the government. Also suggested in the paper is a different way of looking at market concentration in Russia (by consolidating the market shares of core state-controlled banks), which produces a picture of a more concentrated market than officially reported. Lastly, one of the author's interesting conclusions is that China provides a better benchmark than the formerly centrally planned economies of Central and Eastern Europe by which to assess the viability of state ownership of banks in Russia and to evaluate the country's banking sector.
The paper examines the principles for the supervision of financial conglomerates proposed by BCBS in the consultative document published in December 2011. Moreover, the article proposes a number of suggestions worked out by the authors within the HSE research team.
портовый менеджмент, показатели деятельности, анализ эффективности, система учета, распределение издержек, методы анализа деятельности портовой системы
At present many industries reveal tendency for setting up of vertically integrated companies (VIC) the structure of which unites all technological processes. This tendency proved its efficiency in oil industry where coordination of all successive stages of technological process, namely, oil prospecting and production -oil transportation - oil processing - oil chemistry - oil products and oil chemicals marketing, is necessary. The article considers specific features of introduction of "personnel management" module at enterprises of oil and gas industry.
vertically integrated companies; personnel management