Pub Date : 2021-12-31DOI: 10.17323/2587-814x.2021.4.50.60
A. Sigal
This article deals with probabilistic and statistical modeling of managerial decision-making in the economy based on sample data for the previous periods of time. For better definition, the study is limited to Markowitz’s models in the problem of finding an effective portfolio of the field in the third information situation. The third information situation is a widespread decision-making situation and is characterized by the fact that the decision-maker sets, according to his opinion, are a linear order relation on the components of an unknown probabilistic distribution of the states of the economic environment. Often, from the point of view of the decision-maker, the components of an unknown probability distribution of the states of the economic environment must satisfy a partially reinforced linear order relation. As a result, the use of traditional statistical estimates turns out to be impossible, while the following question arises, which is practically not studied in the scientific literature. In this case, what formulas should be used to find statistical estimates and, above all, estimates of unknown probabilities of the state of the economic environment? As an estimate of an unknown probability distribution, we proposed to use the Fishburne sequence that satisfies all available constraints, while corresponding to the opinion of the decision maker and the linear order relation given by him. Fishburne sequences are a generalization of the well-known Fishburne formulas. It is fundamentally important that any Fishburne sequence satisfies a simple linear order relation, and under certain conditions, a partially strengthened linear order relation. Particular attention is paid to the entropic properties of generalized Fishburne progressions, which represent the most important class of Fishburne sequences, as well as the use of generalized Fishburne progressions to take into account the opinion of the decision maker. Such a scheme for estimating an unknown probability distribution has been developed, which makes it possible to achieve the correctness of probabilistic and statistical modeling, as well as appropriate consideration of the opinion of the decision-maker, uncertainty and risk.
{"title":"Using Fishburne’s sequences in suitable modeling used for sample data","authors":"A. Sigal","doi":"10.17323/2587-814x.2021.4.50.60","DOIUrl":"https://doi.org/10.17323/2587-814x.2021.4.50.60","url":null,"abstract":"This article deals with probabilistic and statistical modeling of managerial decision-making in the economy based on sample data for the previous periods of time. For better definition, the study is limited to Markowitz’s models in the problem of finding an effective portfolio of the field in the third information situation. The third information situation is a widespread decision-making situation and is characterized by the fact that the decision-maker sets, according to his opinion, are a linear order relation on the components of an unknown probabilistic distribution of the states of the economic environment. Often, from the point of view of the decision-maker, the components of an unknown probability distribution of the states of the economic environment must satisfy a partially reinforced linear order relation. As a result, the use of traditional statistical estimates turns out to be impossible, while the following question arises, which is practically not studied in the scientific literature. In this case, what formulas should be used to find statistical estimates and, above all, estimates of unknown probabilities of the state of the economic environment? As an estimate of an unknown probability distribution, we proposed to use the Fishburne sequence that satisfies all available constraints, while corresponding to the opinion of the decision maker and the linear order relation given by him. Fishburne sequences are a generalization of the well-known Fishburne formulas. It is fundamentally important that any Fishburne sequence satisfies a simple linear order relation, and under certain conditions, a partially strengthened linear order relation. Particular attention is paid to the entropic properties of generalized Fishburne progressions, which represent the most important class of Fishburne sequences, as well as the use of generalized Fishburne progressions to take into account the opinion of the decision maker. Such a scheme for estimating an unknown probability distribution has been developed, which makes it possible to achieve the correctness of probabilistic and statistical modeling, as well as appropriate consideration of the opinion of the decision-maker, uncertainty and risk.","PeriodicalId":41920,"journal":{"name":"Biznes Informatika-Business Informatics","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2021-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43191873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-30DOI: 10.17323/2587-814x.2021.3.7.23
A. Demidovskij, E. Babkin
The current problem of developing new kinds of decision support systems for different categories of management personnel is addressed in this study. A critical feature of such systems is their distributed and decentralized nature, which enables the construction of next-generation information systems in the form of Multi-Agent Systems, Internet of Things, or Fog Computing Architectures. Parallel models of the dynamics of artificial neural networks are produced under such realistic circumstances, demonstrating their potential for addressing a variety of issues. The purpose of this study is to conduct a critical analysis of the problem of integrating Artificial Neural Networks with decision support systems using a corpus of relevant scholarly literature. To tackle this question, the Design Science Research methodology was considered. According to this methodology, a literary search strategy was established, scientific literature was collected and analyzed, and key comparisons between different solutions were emphasized. The study resulted in the presentation of the most important findings, outstanding issues, and potential areas of fundamental and applied solutions. A consistent trend toward the development of decision support systems based on integrated neural-network methods has been observed, which is efficient and cost-effective since it enables the creation of distributed and trainable decision support systems.
{"title":"Integrated neurosymbolic decision support systems: problems and opportunities","authors":"A. Demidovskij, E. Babkin","doi":"10.17323/2587-814x.2021.3.7.23","DOIUrl":"https://doi.org/10.17323/2587-814x.2021.3.7.23","url":null,"abstract":"The current problem of developing new kinds of decision support systems for different categories of management personnel is addressed in this study. A critical feature of such systems is their distributed and decentralized nature, which enables the construction of next-generation information systems in the form of Multi-Agent Systems, Internet of Things, or Fog Computing Architectures. Parallel models of the dynamics of artificial neural networks are produced under such realistic circumstances, demonstrating their potential for addressing a variety of issues. The purpose of this study is to conduct a critical analysis of the problem of integrating Artificial Neural Networks with decision support systems using a corpus of relevant scholarly literature. To tackle this question, the Design Science Research methodology was considered. According to this methodology, a literary search strategy was established, scientific literature was collected and analyzed, and key comparisons between different solutions were emphasized. The study resulted in the presentation of the most important findings, outstanding issues, and potential areas of fundamental and applied solutions. A consistent trend toward the development of decision support systems based on integrated neural-network methods has been observed, which is efficient and cost-effective since it enables the creation of distributed and trainable decision support systems.","PeriodicalId":41920,"journal":{"name":"Biznes Informatika-Business Informatics","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45893444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-30DOI: 10.17323/2587-814x.2021.3.48.59
A. Vladova, Elena Shek
Significant transformation of the operational activity of product and service distributors is driven by changes in data-receiving and processing technology. At present, the work of these companies’ representatives is digitized to a large extent: for example, the road time, the number and places of meetings with customers are automatically recorded. At the same time, the productivity of managers who do not make direct sales is usually evaluated with the help of surveys, experts and costly double visits, although the existence of large data samples makes possible the use of statistical analysis to identify both insufficient and inflated values of performance indicators. Source data: a relational database that accumulates information about 28 categorical, quantitative, geolocation and temporal parameters of sale representatives’ activities for the last year. Based on available data, we created synthetic features (the latitude and longitude features produced the index, region, street, and house features; based upon identifiers we calculated the sum of activities of sales representatives; according to temporary features we defined the season of the year, the day of the week and the period of day features). The methodology for statistical analysis consists of three main stages: collection and processing of primary data; summary and grouping processed information; setting statistical hypotheses and interpreting the results. A probabilistic approach was used to model the level of distortion of sale representatives’ activities. As a result, with the built tag cloud we highlighted: the most popular season for advertising campaigns; the most productive departments and sale representatives; days of the week with the largest number of contacts to customers. We established a significant number of records about meetings with clients at the weekends. As a result of the data mining, we made a statistical hypothesis about the possibility of identifying the sale representatives who distort the number and parameters of meetings. A set of synthetic integer, real and categorical features was created to identify hidden relationships. Doubtful data (such as working at weekends or at night) were revealed. The resulting aggregated dataset is grouped by a sale representative’s activity ID and the distribution of this feature is plotted. For each sale representative, integer and real features are summarized and outliers that characterize inefficient performance or distortion of data have been detected. Thus, the presence of a large sample of data on the history of movements and activities allowed us to evaluate the productivity of the distribution company’s sales representatives based upon indirect features.
{"title":"Data preprocessing for machine analysis of sales representatives’ key performance indicators","authors":"A. Vladova, Elena Shek","doi":"10.17323/2587-814x.2021.3.48.59","DOIUrl":"https://doi.org/10.17323/2587-814x.2021.3.48.59","url":null,"abstract":"Significant transformation of the operational activity of product and service distributors is driven by changes in data-receiving and processing technology. At present, the work of these companies’ representatives is digitized to a large extent: for example, the road time, the number and places of meetings with customers are automatically recorded. At the same time, the productivity of managers who do not make direct sales is usually evaluated with the help of surveys, experts and costly double visits, although the existence of large data samples makes possible the use of statistical analysis to identify both insufficient and inflated values of performance indicators. Source data: a relational database that accumulates information about 28 categorical, quantitative, geolocation and temporal parameters of sale representatives’ activities for the last year. Based on available data, we created synthetic features (the latitude and longitude features produced the index, region, street, and house features; based upon identifiers we calculated the sum of activities of sales representatives; according to temporary features we defined the season of the year, the day of the week and the period of day features). The methodology for statistical analysis consists of three main stages: collection and processing of primary data; summary and grouping processed information; setting statistical hypotheses and interpreting the results. A probabilistic approach was used to model the level of distortion of sale representatives’ activities. As a result, with the built tag cloud we highlighted: the most popular season for advertising campaigns; the most productive departments and sale representatives; days of the week with the largest number of contacts to customers. We established a significant number of records about meetings with clients at the weekends. As a result of the data mining, we made a statistical hypothesis about the possibility of identifying the sale representatives who distort the number and parameters of meetings. A set of synthetic integer, real and categorical features was created to identify hidden relationships. Doubtful data (such as working at weekends or at night) were revealed. The resulting aggregated dataset is grouped by a sale representative’s activity ID and the distribution of this feature is plotted. For each sale representative, integer and real features are summarized and outliers that characterize inefficient performance or distortion of data have been detected. Thus, the presence of a large sample of data on the history of movements and activities allowed us to evaluate the productivity of the distribution company’s sales representatives based upon indirect features.","PeriodicalId":41920,"journal":{"name":"Biznes Informatika-Business Informatics","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44565883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-30DOI: 10.17323/2587-814x.2021.3.60.77
R. Rogulin
The formation of supply chains for raw materials is closely related to production problems involving the determination of prices for sold goods. The question often arises about the need to study the sources of raw materials and the methodology for pricing the goods produced, taking into account a large number of external aspects of the market. Often, only particular approaches to solving production problems are considered in the literature, and methods for solving the complex problem of forming supply chains for raw materials and pricing are poorly developed. This paper presents a mathematical model that makes it possible to assess the feasibility of interaction between a timber industry enterprise and a commodity exchange, with the daily formation of a price vector over the entire planning horizon. A two-stage algorithm for finding a suboptimal solution is considered, which at the first stage is based on linear optimization, and at the second, on gradient descent with the use of penalty functions. The model was tested on the data of the commodity and raw materials exchange of Russia and one of the enterprises of the Primorsky Territory. The result of testing was the volume of production of each type of product over the entire planning horizon, the volume of delivery of raw materials from regions to enterprises, as well as the methods of delivery of goods to the consumer and the policy of pricing. It is shown that almost all goods should increase in price due to a reduction in the excess volume of applications (demand) over the entire planning horizon, with the exception of two types of products. It is noted that the exchange can provide the necessary volume of raw materials for high-capacity production, which demonstrates the possibility, if necessary, to increase the volume of raw materials purchases. It is shown which goods will be included in the release plan more often than others when optimizing the price vector. The ways of delivery of final types of products are analyzed. The disadvantages and advantages of the mathematical model and algorithm are presented.
{"title":"A mathematical model for the formation of the pricing policy and the plan of the production and transport system in a timber-processing enterprise","authors":"R. Rogulin","doi":"10.17323/2587-814x.2021.3.60.77","DOIUrl":"https://doi.org/10.17323/2587-814x.2021.3.60.77","url":null,"abstract":"The formation of supply chains for raw materials is closely related to production problems involving the determination of prices for sold goods. The question often arises about the need to study the sources of raw materials and the methodology for pricing the goods produced, taking into account a large number of external aspects of the market. Often, only particular approaches to solving production problems are considered in the literature, and methods for solving the complex problem of forming supply chains for raw materials and pricing are poorly developed. This paper presents a mathematical model that makes it possible to assess the feasibility of interaction between a timber industry enterprise and a commodity exchange, with the daily formation of a price vector over the entire planning horizon. A two-stage algorithm for finding a suboptimal solution is considered, which at the first stage is based on linear optimization, and at the second, on gradient descent with the use of penalty functions. The model was tested on the data of the commodity and raw materials exchange of Russia and one of the enterprises of the Primorsky Territory. The result of testing was the volume of production of each type of product over the entire planning horizon, the volume of delivery of raw materials from regions to enterprises, as well as the methods of delivery of goods to the consumer and the policy of pricing. It is shown that almost all goods should increase in price due to a reduction in the excess volume of applications (demand) over the entire planning horizon, with the exception of two types of products. It is noted that the exchange can provide the necessary volume of raw materials for high-capacity production, which demonstrates the possibility, if necessary, to increase the volume of raw materials purchases. It is shown which goods will be included in the release plan more often than others when optimizing the price vector. The ways of delivery of final types of products are analyzed. The disadvantages and advantages of the mathematical model and algorithm are presented.","PeriodicalId":41920,"journal":{"name":"Biznes Informatika-Business Informatics","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42567834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-30DOI: 10.17323/2587-814x.2021.3.24.34
Diana Petrova, P. Trunin
Press releases on monetary policy play an important role in the communication policy of the central bank. These press releases explain key rate decisions and provide signals about the future direction of the central bank’s monetary policy. Information signals can influence the expectations of financial market participants and increase the predictability and effectiveness of monetary policy. There are not enough research papers dedicated to the text analysis of the Bank of Russia press releases and the assessment of information signals. Hence, this article examines the impact of information signals about monetary policy on the money market rate, term and credit spreads. First, we estimate latent Dirichlet allocation to determine the topics of information signals. Second, we use sentiment analysis to construct signals about easing or tightening of the monetary policy. Third, the impact of signals about the future monetary policy on the money market indicators is assessed using the exponential GARCH model. Empirical research has shown that signals of future monetary policy easing are associated with lower money market rates and term spreads, and an increase in the credit spread. The result proved to be resistant to various ways of vectorizing the text of press releases. The article was prepared as a part of the state assignment research of Russian Presidential Academy of National Economy and Public Administration.
{"title":"Analysis of the impact of central bank communications on money market indicators","authors":"Diana Petrova, P. Trunin","doi":"10.17323/2587-814x.2021.3.24.34","DOIUrl":"https://doi.org/10.17323/2587-814x.2021.3.24.34","url":null,"abstract":"Press releases on monetary policy play an important role in the communication policy of the central bank. These press releases explain key rate decisions and provide signals about the future direction of the central bank’s monetary policy. Information signals can influence the expectations of financial market participants and increase the predictability and effectiveness of monetary policy. There are not enough research papers dedicated to the text analysis of the Bank of Russia press releases and the assessment of information signals. Hence, this article examines the impact of information signals about monetary policy on the money market rate, term and credit spreads. First, we estimate latent Dirichlet allocation to determine the topics of information signals. Second, we use sentiment analysis to construct signals about easing or tightening of the monetary policy. Third, the impact of signals about the future monetary policy on the money market indicators is assessed using the exponential GARCH model. Empirical research has shown that signals of future monetary policy easing are associated with lower money market rates and term spreads, and an increase in the credit spread. The result proved to be resistant to various ways of vectorizing the text of press releases. The article was prepared as a part of the state assignment research of Russian Presidential Academy of National Economy and Public Administration.","PeriodicalId":41920,"journal":{"name":"Biznes Informatika-Business Informatics","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46375720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-30DOI: 10.17323/2587-814x.2021.3.35.47
V. Kuzmin, A. Menisov
Together with ubiquitous, global digitalization, cybercrime is growing and developing rapidly. The state considers the creation of an environment conducive to information security to be a strategic goal for the development of the information society in Russia. However, the question of how the “state of protection of the individual, society and the state from internal and external information threats” should be achieved in accordance with the “Information Security” and the “Digital Economy of Russia 2024” programs remains open. The aim of this study is to increase the efficiency whereby automated control systems identify confidential data from html-pages to reduce the risk of using this data in the preparatory and initial stages of attacks on the infrastructure of government organizations. The article describes an approach that has been developed to identify confidential data based on the combination of several neural network technologies: a universal sentence encoder and a neural network recurrent architecture of bidirectional long-term short-term memory. The results of an assessment in comparison with modern means of natural language text processing (SpaCy) showed the merits and prospects of the practical application of the methodological approach.
{"title":"An approach to identifying threats of extracting confidential data from automated control systems based on internet technologies","authors":"V. Kuzmin, A. Menisov","doi":"10.17323/2587-814x.2021.3.35.47","DOIUrl":"https://doi.org/10.17323/2587-814x.2021.3.35.47","url":null,"abstract":"Together with ubiquitous, global digitalization, cybercrime is growing and developing rapidly. The state considers the creation of an environment conducive to information security to be a strategic goal for the development of the information society in Russia. However, the question of how the “state of protection of the individual, society and the state from internal and external information threats” should be achieved in accordance with the “Information Security” and the “Digital Economy of Russia 2024” programs remains open. The aim of this study is to increase the efficiency whereby automated control systems identify confidential data from html-pages to reduce the risk of using this data in the preparatory and initial stages of attacks on the infrastructure of government organizations. The article describes an approach that has been developed to identify confidential data based on the combination of several neural network technologies: a universal sentence encoder and a neural network recurrent architecture of bidirectional long-term short-term memory. The results of an assessment in comparison with modern means of natural language text processing (SpaCy) showed the merits and prospects of the practical application of the methodological approach.","PeriodicalId":41920,"journal":{"name":"Biznes Informatika-Business Informatics","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46035007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-30DOI: 10.17323/2587-814x.2021.3.78.96
Mehdi Shoja, F. Lotfi, A. Abri, A. R. Komijan
A new approach that has dominated the production operations management field in recent years is supply chain management. A supply chain includes all the facilities, tasks and activities involved in manufacturing a product from suppliers to customers. Its various elements are planning, supply and demand management, procurement of raw materials, production scheduling, distribution and delivery of products to the customer. Special structures in the supply chain have been less studied in previous research. In this paper, the supply chain and its performance evaluation are examined in the presence of non-discretionary, undesirable and negative data. For this purpose, another model of the network DEA is presented which evaluates performance of the chain in the presence of non-discretionary inputs and outputs, undesirable outputs and negative outputs even in its internal structure. The efficiency of the chain stages is also calculated using a dual model. Subsequently, 42 cement companies listed on the Tehran stock exchange were evaluated, each of which has a chain of four stages including suppliers, manufacturers, distributors and customers. Based on the implementation of the model, six companies were found to be efficient and the rest were introduced as inefficient. Moreover, 25 cement companies in the Supplier sector, 18 companies in the manufacturing sector, seven companies in the distribution sector and finally 17 companies in the customer service sector were found to be efficient.
{"title":"Efficiency of Green Supply Chain in the presence of non-discretionary and undesirable factors, using Data Envelopment Analysis","authors":"Mehdi Shoja, F. Lotfi, A. Abri, A. R. Komijan","doi":"10.17323/2587-814x.2021.3.78.96","DOIUrl":"https://doi.org/10.17323/2587-814x.2021.3.78.96","url":null,"abstract":"A new approach that has dominated the production operations management field in recent years is supply chain management. A supply chain includes all the facilities, tasks and activities involved in manufacturing a product from suppliers to customers. Its various elements are planning, supply and demand management, procurement of raw materials, production scheduling, distribution and delivery of products to the customer. Special structures in the supply chain have been less studied in previous research. In this paper, the supply chain and its performance evaluation are examined in the presence of non-discretionary, undesirable and negative data. For this purpose, another model of the network DEA is presented which evaluates performance of the chain in the presence of non-discretionary inputs and outputs, undesirable outputs and negative outputs even in its internal structure. The efficiency of the chain stages is also calculated using a dual model. Subsequently, 42 cement companies listed on the Tehran stock exchange were evaluated, each of which has a chain of four stages including suppliers, manufacturers, distributors and customers. Based on the implementation of the model, six companies were found to be efficient and the rest were introduced as inefficient. Moreover, 25 cement companies in the Supplier sector, 18 companies in the manufacturing sector, seven companies in the distribution sector and finally 17 companies in the customer service sector were found to be efficient.","PeriodicalId":41920,"journal":{"name":"Biznes Informatika-Business Informatics","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43079752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-30DOI: 10.17323/2587-814x.2021.2.47.59
V. Komarov, A. Sarafanov
In the context of digitalization of the most knowledge-intensive sectors of the domestic economy, the development of an industrial training system in the field of electronic instrumentation is of great importance. The key areas of its development with the use of information and communication technologies include the development and improvement of the technological basis for training and retraining of personnel in engineering educational programs. One of the elements of this basis is the service of multi-user remote access via the internet to a high-tech experimental equipment laboratory as a service based on internet of things (IoT) systems. Within the framework of this service, an urgent problem is to increase the functional saturation of automated stands/installations, which is currently characterized by a paucity of scientific research. The purpose of the research is to expand the areas of experimental research carried out in the mode of multiuser remote access based on specialized IoT systems. As a result, a method of multidisciplinary application of specialized IoT systems was developed. This consists of the technical implementation of possibilities for additional research: research into technologies underlying both multi-user distributed measuring and control systems and IoT systems in general; research into technologies used in their end-to-end computer-aided design; research into joint interaction of several geographically distributed automated stands/installations, implemented on the basis of a four level IoT reference architecture. A methodology for the design of multi-user distributed measuring and control systems as specialized IoT systems has also been developed, focused on solving multidisciplinary research problems in an interactive dialogue mode based on single sample of experimental equipment. The methodology mobilizes organizational, technical and methodological support for the process of creating such systems with specified target characteristics. In general, the method and methodology developed open up opportunities for systematically implementing the basic principles of the “Education 4.0” concept in the preparation and retraining of engineering personnel in the field of electronic instrumentation.
{"title":"IoT systems in the process of multidisciplinary training of personnel for the digital economy and their design","authors":"V. Komarov, A. Sarafanov","doi":"10.17323/2587-814x.2021.2.47.59","DOIUrl":"https://doi.org/10.17323/2587-814x.2021.2.47.59","url":null,"abstract":"In the context of digitalization of the most knowledge-intensive sectors of the domestic economy, the development of an industrial training system in the field of electronic instrumentation is of great importance. The key areas of its development with the use of information and communication technologies include the development and improvement of the technological basis for training and retraining of personnel in engineering educational programs. One of the elements of this basis is the service of multi-user remote access via the internet to a high-tech experimental equipment laboratory as a service based on internet of things (IoT) systems. Within the framework of this service, an urgent problem is to increase the functional saturation of automated stands/installations, which is currently characterized by a paucity of scientific research. The purpose of the research is to expand the areas of experimental research carried out in the mode of multiuser remote access based on specialized IoT systems. As a result, a method of multidisciplinary application of specialized IoT systems was developed. This consists of the technical implementation of possibilities for additional research: research into technologies underlying both multi-user distributed measuring and control systems and IoT systems in general; research into technologies used in their end-to-end computer-aided design; research into joint interaction of several geographically distributed automated stands/installations, implemented on the basis of a four level IoT reference architecture. A methodology for the design of multi-user distributed measuring and control systems as specialized IoT systems has also been developed, focused on solving multidisciplinary research problems in an interactive dialogue mode based on single sample of experimental equipment. The methodology mobilizes organizational, technical and methodological support for the process of creating such systems with specified target characteristics. In general, the method and methodology developed open up opportunities for systematically implementing the basic principles of the “Education 4.0” concept in the preparation and retraining of engineering personnel in the field of electronic instrumentation.","PeriodicalId":41920,"journal":{"name":"Biznes Informatika-Business Informatics","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67930029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-30DOI: 10.17323/2587-814x.2021.2.34.46
O. Dolganova
The intensive development and application of artificial intelligence technologies in organizing interaction with clients is accompanied by such difficulties as: the client’s unwillingness to communicate with the robot, distrust, fear, negative experience of the clients. Such problems can be solved by adhering to ethical principles of using artificial intelligence. In scientific and practical research on this topic, there are many general recommendations that are difficult to apply in practice, or, on the contrary, that describe the methods for solving a highly specialized technical or management problem. The purpose of this article is to determine the ethical principles and methods, the observance and implementation of which would increase confidence in artificial intelligence systems among client of a particular organization. As a result of the analysis and synthesis of the scientific and practical investigations, as well as the empirical experience of Russian and foreign companies, the main areas of application of artificial intelligence technologies affecting the customer experience were identified. The ethical principles recommended to be followed by business have been formulated and systematized. The main methods have been also identified to enable implementation of these principles in practice, and so to reduce the negative effects of customer interaction with artificial intelligence and increase their confidence in the company.
{"title":"Improving customer experience with artificial intelligence by adhering to ethical principles","authors":"O. Dolganova","doi":"10.17323/2587-814x.2021.2.34.46","DOIUrl":"https://doi.org/10.17323/2587-814x.2021.2.34.46","url":null,"abstract":"The intensive development and application of artificial intelligence technologies in organizing interaction with clients is accompanied by such difficulties as: the client’s unwillingness to communicate with the robot, distrust, fear, negative experience of the clients. Such problems can be solved by adhering to ethical principles of using artificial intelligence. In scientific and practical research on this topic, there are many general recommendations that are difficult to apply in practice, or, on the contrary, that describe the methods for solving a highly specialized technical or management problem. The purpose of this article is to determine the ethical principles and methods, the observance and implementation of which would increase confidence in artificial intelligence systems among client of a particular organization. As a result of the analysis and synthesis of the scientific and practical investigations, as well as the empirical experience of Russian and foreign companies, the main areas of application of artificial intelligence technologies affecting the customer experience were identified. The ethical principles recommended to be followed by business have been formulated and systematized. The main methods have been also identified to enable implementation of these principles in practice, and so to reduce the negative effects of customer interaction with artificial intelligence and increase their confidence in the company.","PeriodicalId":41920,"journal":{"name":"Biznes Informatika-Business Informatics","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49324575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-30DOI: 10.17323/2587-814x.2021.2.75.90
Naief Azyabi
All organizations have been affected by the coronavirus pandemic in different ways. Small and medium-sized enterprises (SMEs) are more vulnerable to changes due to their limited resources. However, the capabilities of information technologies and processes of knowledge management can assist these enterprises to survive and respond appropriately to changes. Thus, this study aims to assess the extent to which information technology capabilities influence the responsiveness of SMEs to challenges that have emerged during the coronavirus crisis. It also investigates the degree to which knowledge management affects such a relationship in the context of Saudi Arabia. The study includes developing a survey as a data collection method. The responses from 136 SMEs were used to make an analysis and, consequently, draw a conclusion. It has been found that IT capabilities positively influence SMEs’ responsiveness to changes brought by coronavirus, through supporting work flexibility and providing a wide range of options in the supply chain, processes, and sales. It further found that knowledge management mediates the relationship between IT capabilities and SMEs’ responsiveness.
{"title":"How do information technology and knowledge management affect SMEs’ responsiveness to the coronavirus crisis?","authors":"Naief Azyabi","doi":"10.17323/2587-814x.2021.2.75.90","DOIUrl":"https://doi.org/10.17323/2587-814x.2021.2.75.90","url":null,"abstract":"All organizations have been affected by the coronavirus pandemic in different ways. Small and medium-sized enterprises (SMEs) are more vulnerable to changes due to their limited resources. However, the capabilities of information technologies and processes of knowledge management can assist these enterprises to survive and respond appropriately to changes. Thus, this study aims to assess the extent to which information technology capabilities influence the responsiveness of SMEs to challenges that have emerged during the coronavirus crisis. It also investigates the degree to which knowledge management affects such a relationship in the context of Saudi Arabia. The study includes developing a survey as a data collection method. The responses from 136 SMEs were used to make an analysis and, consequently, draw a conclusion. It has been found that IT capabilities positively influence SMEs’ responsiveness to changes brought by coronavirus, through supporting work flexibility and providing a wide range of options in the supply chain, processes, and sales. It further found that knowledge management mediates the relationship between IT capabilities and SMEs’ responsiveness.","PeriodicalId":41920,"journal":{"name":"Biznes Informatika-Business Informatics","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46143897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：