One of the most dynamically changing parts of the labor market relates to information technologies. Skillsets demanded by employers in this sphere vary across different industries, organizations and even certain vacancies. The educational system in the most cases lags behind such changes, so that obsolete skillsets are being taught. This article proposes an algorithm of skillsets identification that allows us to extract skills that are needed by companies from different occupational groups in the information technologies sector. Using the unstructured online-vacancies database for the Russian regional labor market, skills are extracted and unified with the use of TF-IDF and n-grams approaches. As a result, key specific skillsets for various occupations are found. The proposed algorithm allows us to identify and standardize key skills which might be applicable to create a system of Russian classification for occupations and skills. In addition, the algorithm allows us to provide lists of the key combinations of skills that are in high demand among companies inside each particular occupation.
This paper is devoted to comparison of the capabilities of various methods to predict the bankruptcy of construction industry companies on a one-year horizon. The authors considered the following algorithms: logit and probit models, classification trees, random forests, artificial neural networks. Special attention was paid to the peculiarities of the training machine learning models, the impact of data imbalance on the predictive ability of models, analysis of ways to deal with these imbalances and analysis of the influence of non-financial factors on the predictive ability of models. In their study, the authors used non-financial and financial indicators calculated on the basis of public financial statements of the construction companies for the period from 2011 to 2017. The authors concluded that the models considered show acceptable quality for use in forecasting bankruptcy problems. The Gini or AUC coefficient (area under the ROC curve) was used as the quality markers of the model. It was revealed that neural networks outperform other methods in predictive power, while logistic regression models in combination with discretization follow them closely. It was found that the effective way to deal with the imbalance data depends on the type of model used. However, no significant impact on the imbalance in the training set predictive ability of the model was identified. The significant impact of nonfinancial indicators on the likelihood of bankruptcy was not confirmed.
The absence of a common and universal approach to IT project management allows us to formulate a problem to analyze and study when choosing the most efficient project management methodology. The relatively small number of scientific works summarizing practical experience of a theoretical approach allowed us to formulate a generalized mathematical model for a common IT project lifecycle estimation in this work using waterfall, agile or hybrid approaches for the project management. Based on the advantages and disadvantages of existing methodologies that we revealed, it appears that use of agile approaches within stages of the cascade methodology approach improves the process of IT project management compared to a pure cascade implementation. Moreover, the recursive application of an iterative approach at certain stages of the project implementation worsens the characteristics of the project life cycle and can be used only to reduce a certain class of project risks. The results of our study allow us to propose a semi-empirical method for project planning estimation accuracy and attainability of the declared project implementation characteristics. All of this should have a positive impact on the effectiveness of the IT project management strategy choice.
The shift to digital technologies in various industries is one of the key goals in the digital agenda. Due to the essential role of interoperability of products and elements in complex systems, standardization stays in the forefront of government policy and business. In manufacturing systems, standards are of a prime importance, since they serve as a channel for modernization and innovation speedup. This paper makes a contributionto the currently rare literature on digital manufacturing standardization as a policy tool to promote digital technologies in business. By comparing five national cases of China, Germany, Japan, the Republic of Korea and the USA, we introduce national models of standardization in smart manufacturing according to the extent of state participation in standardization. In doing so, we examined initiatives in industry, digitalization, the development of a national system of standards, the reference architecture of digital production, as well as the countries’ cooperation in the field. Along with this, an overview of international initiatives in the field is presented, namely the ISO and the IEC. Taking into account the existing landscape, an assessment of the Russian case of digitalization in manufacturing and standardization is presented. Like China, Russia follows the third model of standardization. Given the results, we developed recommendations for Russia with the aim of intensifying efforts at standardization and the country’s presence in the international agenda, as well as to develop a Russian framework for digital transformation in sectors and achieve related economic effects.
Problem of multiple comparisons of several populations on small samples and specificity of the method of it solution are analyzed. It is proposed to extend a classical method for constructing statistical tests by the use of information preprocessing. Examples of the application of the proposed method are given.
Stress testing as an instrument of risk evaluating is actively used in many international organizations, as well as by Central banks in many countries. Some organizations (including the Bank of Russia) conducting stress testing do not public results of tests, which are interesting to the business society. They do so to avoid some panic moods on markets which could lead to massive outflow of deposits from banking sector as a whole or from some individual banks. As a rule, stress testing is conducted relying on huge number of unpublished reporting forms, but business society has no access to them. Only four reporting forms are presented in the Bank of Russia’s website.
In this paper we propose a simplified algorithm of credit risk stress testing of a banking cluster, based on the four officially published reporting forms. The algorithm provides modelling of median values of banking variables depending on macroeconomic indicators, and subsequent retranslation of the received values for assessing financial position of each bank included in the cluster. It is assumed that growth rates of banking indicators obtained from the econometrics models relying on median values are the same for each bank in the cluster.
As of 1 January 2018, credit risk stress testing was conducted for 26 banks, nine of which are system-significant credit institutions. Within the stress-testing eight econometric time series models were developed. As a result, it was discovered that 11 out of 26 banks in the cluster will face with certain difficulties regarding statutory requirements related with capital ratios or buffers.
The article considers the issues of technical product life cycle management in the field of spare parts delivery organization and management within the framework of after-sales service. It provides an examination of a Petri net model, describing the cause-effect relations between events that are linked to delivery planning and management, based on a probabilistic analytical model for after-sales service of technical products and a program-based risk analysis system based on technical and economic criteria. The result of a given model’s performance is planning of an acceptable balance between the cost and quality of products and their current maintenance, which includes detection and minimization of financial risks. An example that illustrates automated planning of spare parts delivery is given. Dynamics of operated technical products’ quantity variation is represented in the integrated graphic type, providing an opportunity to predict an average factor of technical product’s serviceability, determined both by a number of serviceable technical products in a warehouse of the customer and productivity of repair agencies. The earned value method application is proved to be an effective tool for risk analysis of schedule variance in the field of spare parts delivery. Monitoring of the earned value of finances permits to forecast not only the probability of successful completion of spare parts delivery, but also the risks of both cost and schedule variance. An example of automated risk analysis is provided. Estimated coincidence degree of actual cost and planned value is calculated by means of the effectiveness index, which is used to analyze the quality of customer’s subdivisions performance and to correct further functioning. For a selected year, the effectiveness index can be defined and optimized for the predetermined serviceability factor, assigned for every customer during the process of automated planning of spare parts delivery. The approach presented in the article can be considered quite universal, which predetermines an opportunity to apply it in order to provide solutions for product and service life cycle management problems in various organizational technical and economic systems.
In the paper an approach to modeling of requirements management process associated with IT projects is considered. The requirements management model includes three stages. The first stage is related with the choice of business requirements, which are described in the project solution and define the project scope. The second stage includes development of a model that is associated with accepting, rejecting, clarification or classification as ‘additional task’ for every of incoming user requirements. On the third stage for all the accepted user requirements priorities of system requirements are formulated; these priorities are subsequently used for project planning. The decision making models are based on the methods of analytic hierarchy process (AHP) and analytic network process (ANP), as well as on SuperDecisions decision support system.
The companies that are IT-industry leaders perform from several tens to several hundreds of projects simultaneously. The main problem is to decide whether the project is acceptable to the current strategic goals and resource limits of a company or not. This leads firms to an issue of a project portfolio formation; therefore, the challenge is to choose the subset of all projects which satisfy the strategic objectives of a company in the best way. In this present article we propose the multi-objective mathematical model of the project portfolio formation problem, defined on the fuzzy trapezoidal numbers. We provide an overview of methods for solving this problem, which are a branch and bound approach, an adaptive parameter variation scheme based on the epsilon-constraint method, ant colony optimization method and genetic algorithm. After analysis, we choose ant colony optimization method and SPEA II method, which is a modification of a genetic algorithm. We describe the implementation of these methods applied to the project portfolio formation problem. The ant colony optimization is based on the max min ant system with one pheromone structure and one ant colony. Three modification of our SPEA II implementation were considered. The first adaptation uses the binary tournament selection, while the second requires the rank selection method. The last one is based on another variant of generating initial population. The part of the population is generated by a non-random manner on the basis of solving a one-criterion optimization problem. This fact makes the population more strongly than an initial population, which is generated completely by random. Comparing of ant colony optimization algorithm and three modifications of a genetic algorithm was performed. We use the following parameters: speed of execution and the C-metric between each pair of algorithms. Genetic algorithm with non-random initial population show better results than other methods. Thus, we propose using this algorithm for solving project portfolio formation problem.
This article discusses the characteristic changes in management practices occurring in the context of the digital transformation of business. It shows the mutual interconnections of these changes, as well as the links to changes in the organizational culture of the organization. Among the new management practices reviewed are those both at the level of the enterprise as a whole (digital products, digital business models, digital management of value creation chains, digital business processes), as well as on the local level in adoption of management decisions - unlimited knowledge and management of the enterprise in real time (Real Time Enterprise). The article demonstrates the need for formation of certain cultural norms in the organization, including total knowledge management and an orientation to rapid changes. Review is made of the succession and qualitative distinctions of traditional automation from digitalization of enterprises. We discuss the possibility of using theories and methods connected with such concepts as complementary assets for research into new forms of organization for the digital enterprise. The article also presents a research program conducted in the framework of a program for digital transformation of activities of the OJSC Surgutneftegaz, Orbita 2.0. In the given research program the accent is placed on analysis of the problem of sustainability of the organization. In order for the organization to be flexible and changeable, it should periodically be in a condition of instability. In the contrary case, strong resistance to change will develop in it. The search for principles and forms of organization ensuring the controllability of sustainable organizations _ is an important area of this research.