The growing interest and expectations from the blockchain applica-tions attract many analysts to this issue. In what spheres of logistics and supply chain management blockchain is appropriate? What blockchain software solutions are available to companies now? This paper investigates the basic function-ality of the existing software solutions on the market, the comparative analysis of blockchain platforms used for developing the solutions for logistics is also carried out. The main trends of blockchain applications are identified, based on the analysis of the project experience on the use of blockchain, in logistics and supply chain management, in different countries. The problems, limitations and conditions of blockchain implementation are also determined.
As an exogenous antecedent of national innovation performance, culture has been receiving significant attention in cross-cultural research. However, relying primarily on Hofstede’s framework of national culture, this research has so far been predominantly inclined to treating culture as a collection of independent dimensions, thereby ignoring the complex notion of culture profiles that refer to distinctive patterns of interrelated dimensions, which cannot be considered in isolation, but only in combination. Employing the lens of neo-configurational theory and with the support of the fuzzy-set Qualitative Comparative Analysis (fsQCA), the present study aims to fill this gap by exploring how multiple Hofstede’s dimensions interact and combine to influence national innovation performance. In this way, this study goes beyond the existing theory and empirical evidence about the relationship between distinctive culture profiles and innovation performance at national level, while broadening our understanding more generally about how to conceptualize and operationalize culture in business research.
Highly complex, ambiguous and turbulent business environment forces the leading multinational companies to search new strategic capabilities, and management innovations (MI) are considered as an imperative for this development. However, among the local companies operating in the Russian market, management innovations do not have sufficient focus from the company top-management. Major objective of this paper is to study the process of management innovations and the key areas of its implementations within domestic and MNCs operating in Russia. The empirical study involved 1,025 employees from 791 companies operating in Moscow and the Moscow Region. The analysis of the collected data shows that the companies operating in the Russian market, primarily focus on employee motivation (20%) and building an effective communication process (18%) as the priority implementation areas of management innovations. Influence of the different type of economic activity, as well as the business size, and the degree of the company internationalization have been studied in the framework of the research, which revealed the number of peculiarities of the implementation areas of management innovations for Russian and multinational companies, operating in the Russian market.
Innovations in management is one of the most relevant research topics within the global scientific community, as well as one of the leading drivers for the development of multinational companies. The purpose of this article is to investigate the key sources of managerial innovations within Russian and multinational companies operating in the Russian market. Over one thousand employees from nearly 800 companies operating in Moscow and the Moscow Region participated in the empirical study. Research results show the importance of “the internal know-how” and “access to consulting services” as the key sources of managerial innovations for companies operating in the Russian market. “Collaboration with other Russian players” is limited mainly to Russian companies. The “collaboration with multinational players” is an underdeveloped source of managerial innovations among Russian companies, resulting in the slowdown of Russian managerial practices’ development. Additionally, the analysis of the significance of managerial innovations’ sources depending on the economic activity, size of the business and the degree of internationalization revealed a number of specific features that elaborate the general understanding of the research topic and its conclusions.
Abstracts presented at 18th World Organization of Systems and Cybernetics Congress
Artificial intelligence and machine learning helps to improve the quality of customer service and change the methods of companies’ activities. For this reason, enterprises should consider integrating these technologies into digital transformation plans to remain competitive. Low-code machine learning platforms allow companies and business professionals with minimal coding experience to create applications and fill in the gaps of the personnel in their organization. Automated machine leaning (AutoML) technology represents the next step in the evolution of machine learning, providing non-technical companies with the ability to create machine learning applications quickly and cheaply
Abstracts for 32nd International Congress on Project Management
The present work is devoted to the problems of Digital Twin development of industrial enterprises in the field of mining. The main goal of this article is to formulate the principles of designing platform solutions for the integration of the most important functional elements that ensure the implementation of technological processes of a full production cycle. Various classification schemes for heterogeneous, poorly structured information spaces that form the distributed digital environment of a mining enterprise are proposed. Based on the results of structural and functional modeling, a number of principles and requirements are formulated for the implementation of the Digital Twin technology of the transport and technological process in quarry. A conceptual diagram of the functional structure of the Digital Twin platform is proposed, taking into account the need to include Industry 4.0 technologies such as Industrial Internet of Things, Big Data (including Predictive Analytics and Machine Learning), Autonomous Haulage Systems and Dynamics 3D Optimization Modeling. Some aspects of the implementation and functioning of the prototype version of the Digital Twin platform are considered in terms of the use of instrumental solutions based on the Unity visual modeling environment.
This concise book provides a survival toolkit for efficient, large-scale software development. Discussing a multi-contextual research framework that aims to harness human-related factors in order to improve flexibility, it includes a carefully selected blend of models, methods, practices, and case studies. To investigate mission-critical communication aspects in system engineering, it also examines diverse, i.e. cross-cultural and multinational, environments.
This book helps students better organize their knowledge bases, and presents conceptual frameworks, handy practices and case-based examples of agile development in diverse environments. Together with the authors’ previous books, "Crisis Management for Software Development and Knowledge Transfer" (2016) and "Managing Software Crisis: A Smart Way to Enterprise Agility" (2018), it constitutes a comprehensive reference resource that adds value to this book.
The intellectual structure of scientific discipline consists of a set of interacting topics. The evolution of these topics is the subject of special attention because it reflects the actual interest of researchers and stakeholders. This paper analyzes issues of High-Performance Computing (HPC) on the base of the formal topic modeling technique. Analyzing the abstracts of 7661 publications referenced in Web of Science in 2005–2019, we identified seven topics that concern different aspects of HPC science. The central theme is the Large Scale Applicationsfocused on practical and scientific problems solved using HPC. It is closely linked with Parallel Algorithms that should effectively exploit the thousands of processing cores, Parallel Softwarefor heterogeneous distributed systems, and Interconnected systems that study the integration of HPC facilities in systems of larger size. These topics are relatively stable both in terms of popularity (number of publications) and impact (number of citations). The single topic, which popularity and impact continuously grow in the last 15 years, is Energy efficiency since power consumption is a critical issue of exascale systems. We also found that the topic of Heterogeneous systems dedicated mainly to GPU usage declines after the peak of interest in 2010–2015. The results obtained shed light on the structure of HPC science and supplement the known publications that declare research direction towards exascale performance.
В статье представлены результаты исследования нескольких эффективных моделей прогнозирования динамики котировок акций. Данное исследование основано на анализе прогнозирующей способности пяти методов прогнозирования с использованием выборки, включающей 30 компаний из двух сегментов рынка: автомобильного и информационного.
The blockchain technology is currently penetrating different areas of the modern Information and Communications Technology community. Most of the devices involved in blockchain-related processes are specially designed targeting only the mining aspect, i.e., solving the computational puzzle task. At the same time, the use of wearable and mobile devices may also become a part of eCommerce blockchain operation, especially during the on-charge time. The paper considers the possibility of using a large number of constrained devices to support the operation of the blockchain with a low impact on battery consumption. The utilization of such devices is expected to improve the system efficiency as well as to attract a more substantial number of users. This paper contributes to the body of knowledge with a survey of the main applications of blockchain for smartphones along with existing mobile blockchain projects. It also proposes a novel consensus protocol based on a combination of Proof-of-Work (PoW), Proof-of-Activity (PoA), and Proof-of-Stake (PoS) algorithms for efficient and on-the-fly utilization on resource-constrained devices. The system was deployed in a worldwide testnet with more than two thousand smartphones and compared with other projects from the user-experienced metrics perspective. The results prove that the utilization of PoA systems on a smartphone does not significantly affect the lifetime of the smartphone battery while existing methods based on PoW have a tremendous negative impact. Finally, the main open challenges and future investigation directions are outlined.
This article describes the issues of analysis and assessment of the human factor for predicting the violation committed by the locomotive driver when driving the electric rolling stock. An intelligent system overview for assessing the likelihood of a violation by a locomotive driver is given. Such a system can generate recommendations depending on previously committed violations. One of the tasks is to reduce the risk of locomotive safety devices malfunctions, which are part of the locomotive electrical equipment. The solution to the problem of predicting the occurrence of possible violations is solved using tools and machine learning algorithms. A model has been built that generates recommendations for the driver based on information about previously committed violations and several static characteristics of the locomotive driver.
We considered basic mechanisms of atmospheric particle acceleration and estimated the escape rates of ionospheric ions (H+ and O+) during the geomagnetic field reversal. It is assumed that during the reversal the Earth's magnetic field deviates from the current dipole configuration, and the quadrupole component dominates. The standoff distance of the quadrupole magnetosphere is about of 3 Earth's radii and therefore a magnetic shielding protects the atmosphere from sputtering and ion pickup but not from the polar and auroral winds.
Currently, there is an extensive set of bankruptcy prediction models, but almost all of them are classification based, i.e., they allow to estimate the posterior probability that a particular firm will fail, given its financial characteristics. The expected time to failure is not considered explicitly. On the other hand, there is a survival analysis that deals with the time of the occurrence of the event of interest (while this event may not occur during observation). However, despite its popularity in the medical and technical sciences, survival analysis is relatively rarely used in predicting financial failure. Even when it is applied, most authors use the simplest form of a model. The goal of our work is to evaluate the applicability of survival analysis to bankruptcy prediction. We compare a few state-of-art statistical and machine learning models using a real dataset. Our findings confirm that survival analysis allows (1) to extract from given data valuable information regarding the dynamics of risks and (2) to estimate the impact of features.
Auditors use behavioral red flags (BRFs) to examine which individuals are more prone to unwarranted behavior like corruption and asset misappropriation. Using a rich data set from the Association of Certified Fraud Examiners (ACFE), we analyze the impact of BRFs on loss sizes from asset misappropriation. We control for anti-fraud mechanisms established at the company level and other factors both at the individual and the firm level. Performing an exploratory factor analysis yields six factors for BRFs which capture the principal perpetrator’s situation at the private level and the workplace. A general wheeler-dealer attitude and financial distress significantly increase loss sizes. By contrast, we find no evidence that non-monetary private problems lead to higher losses.
The goal of this paper is to perform benchmarking of Russian power companies from a strategic asset management point of view. The Russian power industry is undergoing through a transformation that implies introduction of competition and increasing deployment of new technologies which leads to the shrinkage of market opportunities and incumbents have to re- think their current practices. We argue that strategic asset management is a source of sustainable competitive advantages and it lies at the core of the innovative development both for generating and distribution companies. We employ a linear programming technique in order to classify and compare companies (within two segments) in terms of revenue generating efficiency versus cost efficiency. The resulting scores are plotted on two-dimensional matrices. We provide additional analysis of secondary data in order to infer on best practice within the industry. The results of the present research provide useful insights both for market incumbents and possible new entrants concerning the competitive dimension with a focus on the role of strategic asset management. The theoretical contribution lies in the study of asset management strategies, competition and competitive advantages within the industry.
This paper summarizes practices of customer- driven services applied in the leading Russian bank to avoid the impact of financial sanctions (2014–2019). We show how economic sanctions and strict national policies triggered this bank to increase flexibility in customer care to attract more capital from their existing clients. The project comprised three stages: (1) to analyse requirements and to develop ‘‘as-is’’ state of processes; (2) to analyse best practices and to improve processes under the scope of flexibility and customer orientation; (3) to implement the new vision in ‘‘to-be’’ state and final verification. At the third research stage to assess the results of processes improvement in the bank within a year we have applied a set of methods based on data envelopment analysis which provides a multidimensional understanding of processes and new scopes of customer’s value profiles. We have found that process reengineering result could give the contribution already at the first month of implementation and argue the findings could be used to introduce flexible data-driven customer care and improve customer-related processes in organisations worldwide.