Abstract. The purpose of the article is to develop a fuzzy model of assessment of risks of activities of oil and gas production enterprises. Methodology. Due to a large number of factors, influencing the probability of risk occurrence, and in order to obtain a comprehensive indicator during the research, we have applied a fuzzy cascade model of the Mamdani type. Research results. In the conditions of instability and constant uncertainty of oil and gas production processes, identification and forecasting of the occurrence of risks of operations of oil and gas production enterprises by traditional mathematical methods of modelling provide no required reliability and accuracy of forecasting. In this regard, we propose an integral assessment and application of the fuzzy logic methodology for obtaining the required results for the adoption of effective managerial decisions. Despite the complexity of the mathematical apparatus, risk assessment on the basis of the theory of fuzzy sets makes it possible to create a sufficiently flexible model, which will operate with a large number of input arguments and give as a resultant variable a value, which can be considered to be objective with some degree of approximation. Practical importance. The step-by-step addition of each group of risk factors to the model allows obtaining reliable results of the probability of occurrence of risk events on a real-time basis, which significantly reduces the company's losses. Value/originality. According to the results of the research, the Mamdani-type fuzzy cascade model of the assessment of risks of the activities of the oil and gas production enterprises is developed for the first time.
A pricing model for eco-innovative products has been developed based on its technological readiness for commercialization in the context of sustainable economic development. It is proved that the pricing model for eco-innovative products can be based on an integral indicator that includes a set of factors influencing the level of its technological readiness – from the point of view of the developer (manufacturer, seller) and market perception (buyer, consumer, etc.). The formalization of such an indicator is carried out on the basis of algorithms of fuzzy set theory. The developed method is tested on a number of types of eco-innovative products developed by Ukrainian business entities. The obtained price integral indicators are determined by a fuzzy number with a certain range, which makes it possible to take into account the features characteristic of a particular market situation due to the level of technological readiness of eco-innovative products. This method allows you to achieve a higher level of price accuracy.
Фурсова В.А. кандидат экономических наук, доцент, доцент кафедры финансов, учета и налогообложения Национального аэрокосмического университета имени Н.Е. Жуковского «Харьковский авиационный институт» Фадеева И.Г. доктор экономических наук, профессор, заведующая кафедрой финансов Ивано-Франковского национального технического университета нефти и газа Боровик Л.В. доктор экономических наук, доцент, доцент кафедры экономики и финансов ГВУЗ «Херсонский государственный аграрный университет»
Фадєєва І. Г., Гринюк О. І. Нечітка логіка як інструмент ризик-контролінгу в контексті проактивного управління нафтогазовидобувними підприємствами Метою статті є пошук шляхів підвищення ефективності функціонування нафтогазовидобувних підприємств (НГВП) в умовах невизначеності бізнес-середовища. Обґрунтовано актуальність застосування методів нечіткої логіки як інструменту ризик-контролінгу в рамках забезпечення проактивного управління нафтогазовидобувними підприємствами. Удосконалено модель системи ризик-контролінгу НГВП, яка, на відміну від існуючих, доповнена каскадною нечіткою моделлю типу Мамдані -оцінювання та прогнозування ймовірності настання ризиків, що створює передумови для проактивного управління суб'єктом господарювання. Сформовано модель імплементації системи ризик-контролінгу в систему проактивного управління НГВП, яка базується на інтегруванні систем управління ризиками та ризик-контролінгу.
The article is devoted to the development and improvement of methodology and implementation of riskcontrolling subsystem in the management system of oil and gas companies in under uncertainty conditions. It is proved, that the mathematical tool of Fuzzy Logic allows to reveal and take into account complicated nonlinear dependences between quantitative and qualitative indicators of risk-event probability estimation, and also mutual effect of risk forming factors. The six-level system of evaluation of risks of operating activities of oil and gas companies, based on fuzzy logic has been established. It allows to take into account the non-linear nature of the relationship between the risk forming factors and the resulting indicator. The logical inference model has been designed. It shows the dependence of the risk level on the meaning of linguistic rules about groups of risk forming factors in different risk groups and which is the basis for the risk assessment model. The conceptual model of management of oil and gas companies with the risk-controlling subsystem as a part of it, the main element of which is the fuzzy model of risk assessment and forecasting, has been proposed. The research concluded, that the implementation of the proposed model of management of oil and gas production enterprises will significantly improve the efficiency of the upstream segment enterprises and significantly reduce losses, caused by the occurrence of risk-events.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.