Towards Developing a Model-Based Decision Support Method for Enterprise Restructuring
In modern world enterprises need to be agile in their operation and structure to react to changes quickly. One of the open questions here is how to develop the enterprise, or, to be more precise, if enterprise needs to be developed, and if yes, in which way. In this research we are focusing on the case when enterprise stakeholders understand the need of enterprise development, have ideas for that, and they need decision support method to understand if enterprise restructuring is likely to be successful and cost effective. Another covered topic is how to choose the best option for restructuring from variety provided. In this paper we describe the developed decision support method which combines DEMO methodology and transaction costs theory for quantitative costs estimation. To make this method applicable and reproducible we proposed few enhancements to DEMO notation.
Decision support in equipment condition monitoring systems with image processing is analyzed. Long-run accumulation of information about earlier made decisions is used to realize the adaptiveness of the proposed approach. It is shown that unlike conventional classification problems, the recognition of abnormalities uses training samples supplemented with reward estimates of earlier decisions and can be tackled using reinforcement learning algorithms. We consider the basic stages of contextual multi-armed bandit algorithms during which the probabilistic distributions of each state are evaluated to evaluate the current knowledge of the states, and the decision space is explored to increase the decision-making efficiency. We propose a new decision-making method, which uses the probabilistic neural network to classify abnormal situation and the softmax rule to explore the decision space. A modelling experiment in image processing was carried out to show that our approach allows a higher accuracy of abnormality detection than other known methods, especially for small-size initial training samples.
This article presents an engineering approach to estimating market resiliency based on analysis of the dynamics of a liquidity index. The method provides formal criteria for defining a “liquidity shock” on the market and can be used to obtain resiliency-related statistics for further research and estimation of this liquidity aspect. The developed algorithm uses the results of a spline approximation for observational data and allows a theoretical interpretation of the results. The method was applied to real data resulting in estimation of market resiliency for the given period.
The paper aims to investigate the process of establishing distribution network. The paper takes network paradigm as a main basis of investigation looking at the development of distribution networks in Russian chemical industry.
An outline of a few methods in an emerging field of data analysis, “data interpretation”, is given as pertaining to medical informatics and being parts of a general interpretation issue. Specifically, the following subjects are covered: measuring correlation between categories, conceptual clustering, and generalization and interpretation of empirically derived concepts in taxonomies. It will be shown that all of these can be put as parts of the same inquiry.
Inconsistency of business processes can affect company profits and lead to the loss of regular customers and reputation in the market. Well managed business process has one key distinctive feature – a consistency. Checking the consistency of business process helps to reveal hidden bugs in the process model, but requires considerable labor costs and analytics. We compared two approaches to verifying consistency. The first approach is based on generating object life cycles for each object type used in process and supported by special tool as an extension for IBM WebSphere Business Modeler. Another one is a proposition to use DEMO methodology for verifying consistency. The results of research show that DEMO methodology enables significantly reduce labor costs and improve quality of analyze
The article considers various approaches to the theory of human and social capital, analyzes the problem of measurement of human resources and application of "Human Development Index" in the Russian Federation. Under market economy conditions the human capital is considered as an asset bringing regular income.
In this paper we investigate how asymmetric information and informed trading influences liquidity and how liquidity influences asset pricing on the Russian stock market in 1998-2011. We use a battery of existing liquidity proxies as well as our own modification of Lesmond et al. (1999) measure and capture informed trading through positive daily return autocorrelation. We find that asymmetric information worsens liquidity, whereas no supportive evidence of adverse impact of informed trading can be discovered, which could be partly due to a weak proxy. Furthermore, liquidity, along with market risk, seems to be the major driver of asset pricing on the Russian stock market. This result, however, is not robust to specifying liquidity as characteristic rather than factor.
In the paper an approach to identification of characteristics for assessment of IT strategic decisions is proposed. The main feature of the approach is associated with integration of Balanced Scorecard methodology for IT service (IT Balanced Scorecard) and COBIT standard. Such integration allows to describe a hierarchical structure of characteristics (metrics) for assessment of decisions efficiency in yje field of information technologies.