Iterative Methodology of Bankruptcy Forecast of Logistic Companies in Emerging Markets, Taking Into Account Global/Regional Crisis

Hrashchenko, Iryna; Krasnyuk, Maxim; Krasniuk, Svitlana

doi:10.32782/2520-2200/2020-1-43

Cited by 17 publications

(8 citation statements)

References 8 publications

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“…Achieving the possible level of selfsufficiency in fuel and energy resources is a fundamental task of national economies, without which the successful implementation of economic, scientific, technical and social programs aimed at ensuring state independence and stability in Europe is impossible.However, the onshore oil and gas industry of the countries of Eastern Europe with significant volumes of unexplored oil and gas resources, with the cost of oil and gas several times lower than world prices, the presence of a significant number of oil and gas industries, drilling and geophysical enterprises, oil refineries, and an extensive network of oil and gas pipelines, highly qualified production teams allows, with their effective use, not only to stabilize, but also to significantly increase the production of oil, gas and condensate in the future.An important reason for the drop in oil and gas production volumes is insufficient management efficiency of the cycle of parallel business processes of the oil and gas company: field exploration, their arrangement and development, production and sale of oil and gas. The solution is the application of effective economic-mathematical modeling at the strategic level of management and the use of knowledge-oriented decision-making support tools as an integral component of the complex information system of an oil and gas company [1].So, In modern economic conditions (globalization of the economy and simultaneous narrow specialization of economic sectors, the need to take into account when making a decision a huge amount of information from various sources, the need for a quick reaction, changes in the business policy of the corporation in response to rapid changes in the world situation [2]) it is necessary to use AI and a hybrid approach to building knowledge-based IDSS. Moreover, their use is particularly effective in the oil and gas industry of Ukraine, where management decision-making requires complex expert analysis, is associated with significant capital investments, there are many branches of scenarios and decision-making nodes, there are open situations of uncertainty, specific industry risk [3].…”

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confidence: 99%

“…So, In modern economic conditions (globalization of the economy and simultaneous narrow specialization of economic sectors, the need to take into account when making a decision a huge amount of information from various sources, the need for a quick reaction, changes in the business policy of the corporation in response to rapid changes in the world situation [2]) it is necessary to use AI and a hybrid approach to building knowledge-based IDSS. Moreover, their use is particularly effective in the oil and gas industry of Ukraine, where management decision-making requires complex expert analysis, is associated with significant capital investments, there are many branches of scenarios and decision-making nodes, there are open situations of uncertainty, specific industry risk [3].…”

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confidence: 99%

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INTELLIGENT TECHNOLOGIES IN HYBRID CORPORATE DSS (ON THE EXAMPLE OF UKRAINE OIL&GAS PRODUCTION COMPANY)

Krasnyuk¹,

Goncharenko²,

Krasniuk³

2022

Інноваційно-Інвестиційний Механізм Забезпечення Конкурентоспроможності Країни

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Oil and gas companies in order to maintain their efficiency in the conditions of: liberalization of markets, globalization, increased competition, reduction of consumer loyalty, constant variation in oil and gas prices, further development of Industry 4.0 and the Big Data factor, growing costs for drilling and completion – must have a flexible environment of information technologies that enables seamless and efficient sharing of knowledge throughout the company and along the entire value chain. One of the elements of this task is the effective use of hybrid knowledge-oriented decision support systems (DSS). This, in particular, determines the future stage of complex author's research – the development of a complex perforating knowledge management policy of an oil and gas company, the key tool for the implementation of which will be the hybrid, knowledge-oriented DSS considered in this publication.The form of knowledge representation has a significant impact on the characteristics and properties of a knowledge-oriented system. Therefore, based on the specifics of the exploration and development of oil and gas fields and the main advantages of the rule-oriented model of the DSS knowledge base, it is possible to conclude that it is necessary to use the KB rule-oriented basis for the DSS of an oil and gas production company. The rule-oriented subsystem is the main one in the knowledge-oriented DSS of an oil and gas production company, in fact, other subsystems provide it with analyses, assessments, and knowledge. Namely, the final product of the system: recommendations for making management decisions is carried out by a rule-oriented subsystem. In general, scientifically based conclusions were obtained regarding the knowledge-oriented architecture of the intellectual DSS of an oil and gas company, the basic model of knowledge presentation (production), the features of the mechanism of logical conclusion (direct logical conclusion), the conflict resolution procedure (a method of ordering products), etc. In the oil and gas industry, DSS built according to a hybrid approach have the greatest application potential – which are a powerful tool for solving complex specific problems of an oil and gas company. Therefore, in this work, the principles of hybrid application of intelligent technologies and the knowledge-oriented basis of DSS of an oil and gas company were further developed.

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confidence: 99%

mentioning

confidence: 99%

INTELLIGENT TECHNOLOGIES IN HYBRID CORPORATE DSS (ON THE EXAMPLE OF UKRAINE OIL&GAS PRODUCTION COMPANY)

Krasnyuk¹,

Goncharenko²,

Krasniuk³

2022

Інноваційно-Інвестиційний Механізм Забезпечення Конкурентоспроможності Країни

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“…Thus, a reliable forecast of the financial stability (risk of bankruptcy) of a company through the construction of adequate economic-mathematical-statistical models is possible if the following, rather complicated, for now, for the practical fulfillment of the conditions are fulfilled [3]:…”

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confidence: 99%

Results of Analysis of Machine Learning Practice for Training Effective Model of Bankruptcy Forecasting in Emerging Markets

Krasnyuk¹,

Tkalenko²,

Krasniuk³

2021

Advanced Discoveries of Modern Science: Experience, Approaches and Innovations Band1

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“…The indisputable advantage of machining is speed and the minimum probability of making mistakes. For example, in 2019, the average cost of an aircraft delay for a US passenger airline was $ 74.25 per minute.Machine learning is used in the field of airline revenue management: personalized discounts, optimal routes, dynamic formation of air ticket prices, and the like [5]. Correctly laid routes are a very important factor in the success of an airline.…”

mentioning

confidence: 99%

“…Machine learning is used in the field of airline revenue management: personalized discounts, optimal routes, dynamic formation of air ticket prices, and the like [5]. Correctly laid routes are a very important factor in the success of an airline.…”

mentioning

confidence: 99%

Modern Practice of Machine Learning in the Aviation Transport Industry

Krasnyuk¹,

Krasniuk²

2021

Theoretical and Practical Aspects of Modern Scientific Research 1권

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The fast-paced life of the present produces huge amounts of data every minute [1]. Timetables, scheduled aircraft maintenance, consumables, loyalty programs and personalized offers for passengers are data that can increase profits and reduce costs [2]. However, analysts are interested in information hidden in the data, which, without proper storage and processing, will remain just a lot of lines and will not be able to bring the expected benefits [3].Machine learning has made a big breakthrough in knowledge processing [4]. Most of the routine operations are algorithmized and transferred to machines; a person, as a rule, acts as a teacher and controller. The indisputable advantage of machining is speed and the minimum probability of making mistakes. For example, in 2019, the average cost of an aircraft delay for a US passenger airline was $ 74.25 per minute.Machine learning is used in the field of airline revenue management: personalized discounts, optimal routes, dynamic formation of air ticket prices, and the like [5]. Correctly laid routes are a very important factor in the success of an airline. A striking example is the presence of direct flights between Krakow (Poland) and Chicago (Illinois, USA), which, at first glance, does not make sense. However, it is enough to analyze the ethnic composition of Chicago to find that 1,500,000 Poles live in the diaspora. Another important factor is dynamic pricing for air tickets. This is not a trivial task, since the airline wants to sell at a higher price, and the passenger wants to buy at a lower price. After all, the price of an air ticket is influenced by many factors: the time of booking, the day of the month, the day of the week, the availability of events at the destination, the cost of fuel, etc. All this data is difficult to process manually, but a properly designed system can handle it easily. Therefore, each airline has its own closed dynamic pricing system, and scientists develop frameworks and train neural networks to accurately predict the cost of air tickets.The development of the aviation industry leads to an increase in the number of flights, which creates more carbon dioxide emissions. In 2018, all commercial air travel caused 2.4% of global CO2 emissions . Although this value may seem insignificant, in 2013-2018 the amount of emissions increased by 32%. Machine

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Iterative Methodology of Bankruptcy Forecast of Logistic Companies in Emerging Markets, Taking Into Account Global/Regional Crisis

Cited by 17 publications

References 8 publications

INTELLIGENT TECHNOLOGIES IN HYBRID CORPORATE DSS (ON THE EXAMPLE OF UKRAINE OIL&GAS PRODUCTION COMPANY)

INTELLIGENT TECHNOLOGIES IN HYBRID CORPORATE DSS (ON THE EXAMPLE OF UKRAINE OIL&GAS PRODUCTION COMPANY)

Results of Analysis of Machine Learning Practice for Training Effective Model of Bankruptcy Forecasting in Emerging Markets

Modern Practice of Machine Learning in the Aviation Transport Industry

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