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In the article there are considered the peculiarities of transport support in the Arctic region, highlighted current problems and reasons restricting the active functioning of different modes of transport in the region, conducted analysis of Arctic ports. The authors’ interpretation of modern tendencies on the field of cargo delivery along the Northern Sea route allows assessing high importance of the issue of transport development in Arctic for Russian economy.
*Реализация соц. сети Instagram запрещена на территории России по основаниям осуществления экстремистской деятельности
The purpose of this article is to assess how museums have changed their presence on Instagram, how subscribers have responded to these changes, and what opportunities are opening up for museums in this regard. The study reveals an increase in the educational focus of museum Instagram during and after the lockdown, as well as an increase in subscriber activity, and offers recommendations on how to use these changes for the future development of museums. Statistical tests, namely the one-sample Kolmogorov-Smirnov test, the t-test, and the Wilcoxon signed rank test for linked samples, are used to test hypotheses. The research sample includes 7,776 Instagram posts from 74 Russian museums and covers three time periods: before, during and after the lockdown.
In the digital economy, information systems have a significant impact
on supply chain management. However, there is a need for further development of
theoretical knowledge andmathematical models, including methods formanaging
risk in complex supply networks to best serve customer orders. In the supply chain
operations reference (SCOR) model, reliability is assessed by calculating perfect
order parameters. The component/process reliability is calculated as the product of
the weighted averages of the perfect order parameters, and possible combinations
of failure features are not taken into account. This paper presents an approach to
probabilistic estimation of perfect order parameters based on the general theorem
on the repetition of experiments, and proposes to use a binomial distribution
to approximate the values obtained. The obtained results make it possible to assess
the efficiency of possible measures (increasing the insurance stock, replacing the
carrier, etc.) to improve the reliability of perfect order fulfilment.
The research into various sources showed that, despite the results achieved, a low-demand environment and short time series are currently often neglected. To improve the reliability and validity of forecast estimates based on a short time series with low demand, it is necessary to create calculation models using all available quantitative and qualitative information. In this paper, we propose an algorithm that includes the systematisation of statistical data in the form of a time series, statistical and analytical models, expert evaluation of forecast consistency, analysis of the results in order to form versions of a combined model for assessing the predicted parameters of stock consumption, making decisions on choosing one of the inventory management strategies for a short time series with low demand, and the proposed approach is tested. Further research of low demand should include a number of directions, in particular, the development of combined forecasting methods, which include, in addition to quantitative and qualitative methods, the application of decision-making methods.
At present, the use of Artificial Intelligence (AI) methods and tools is an essential component of management information systems for a company to succeed in a rapidly changing environment. Agent-based modelling systems as systems of distributed AI must be considered nowadays as an obligatory stage of decision-making in Russian oil and gas companies, which use modern information technologies actively. The paper is focused on the description and comparative analysis of system dynamics and agent-based modelling, used for intelligent decision support systems development in transport logistics. The main goal of this research is evaluation of the multi-agent system's role for decision-making processes and management information systems development and creating the model of logistics processes (the processes of oil and oil products transportation, loading, and unloading). The work is based on a generalisation of theoretical researches in this area along with international practices and domestic experience.
The issues of organization and operational planning in the field of logistics process management in procurement, production and distribution, as well as issues of organization and management of warehousing, inventory management of material resources are considered.
The conceptual apparatus of the considered logistics systems and technologies is analyzed. The issues of designing a warehousing system, building a distribution system, optimizing the processes of inventory management of material resources are described in detail.
The advanced and foreign experience in the field of operational logistics is considered. Corresponds to the latest generation of the Federal State Educational Standard.
For students studying under the advanced training program for mid-level specialists of specialty 38.02.03 "Operational activities in logistics", teachers of economic specialization and specialists working in the field of operational logistics, adapted to the requirements of the "digital economy".
At present, the use of modern modeling methods and tools is an essential component of management information systems for a company to succeed in a rapidly changing environment. It is important that simulation is considered today as an obligatory stage of decision-making in oil companies, which use modern information technologies actively. The paper is focused on the description and comparative analysis of system dynamics and agent-based modeling, used for intelligent decision support systems development in transport logistics. The main goal of this research is evaluation of the multi-agent system's role for decision-making processes and management information systems development and creating the model of logistics processes (the process of oil products loading and unloading). It also considers the main determinations and notions of the intellectual agent modelling methodology, gives the types of modeling categorization. The work is based on a generalization of theoretical researches in this area along with international practices and domestic experience.
Around 30% to 70% of products in retail and services experience low demand, including spare parts and components for nearly all types of machinery and equipment industries. A detailed analysis of stock forecasting methods for the low demand represents a research gap in inventory management. The existing clustering methods, that is, ABC analysis and XYZ analysis (based on coefficient of variation), do not allow identification of the consumption process dynamics and, therefore, cannot be used for the classification and improvement of forecasting models for stock consumption. This paper surveys special cases of inventory management with low demand. The results of one- and two-dimensional stock classifications are presented. The limitations of the economic order quantity (EOQ) model for inventory management strategies are determined. Methods of inventory parameter calculations for products with low demand are suggested. Integrated time series forecasting models, along with algorithms to estimate the inven- tory forecasting parameters, are proposed with regard to products with low demand. The basis for the suggested models is the following concept: all the available sources of quantitative and qualitative information should be used for managerial decision-making under uncertainty and risk. Calculations for time series with low demand are conducted for testing purposes. The obtained results confirm the adequateness of the suggested concept, aimed at solving the problem of cost reduction in supply chains.
It has been proved by the latest research on key performance indicators
(KPIs) of transportation services that their successful implementation into
practice is possible only if there is a thorough database of indicators and the
methodology of their calculation. To reach these goals, it is necessary to classify
the indicators within the framework of the system which includes the two levels:
the basic (the first) and the specific (the second) KPI. This division allows to
form the complex of models to calculate the basic indicators, which characterize
performance (e.g. performance per hour), time parameters, expenses, reliability,
etc. The article provides the analysis of papers on the methods of transportation
efficiency rating in supply chains and the ways of their development to increase
the efficiency of transportation; the new approach to obtain analytic dependencies
to calculate KPI of transportation on the basis of the integral (factorial)
method of economic analysis; the examples of calculations of some KPIs of
transportation. The suggested KPI models can be used to create programs aimed
at the digitalization of transportation operations in supply chains.
Over the past thirty years, optimization modeling techniques have begun to be actively used in supply chain planning and management. Given the specifics of planning tasks in supply chains, linear programming and its methods such as dynamic programming, stochastic programming and scenario planning have become the most popular. These methods make it possible to optimize the supply chain across numerous databases, each of which corresponds to a scenario describing different options for development in an uncertain future. Despite quite intensive research in this area, dynamic and stochastic programming is still underused by managers to solve application tasks in various fields, including supply chain management. Hence, there is a need for development of new planning models in logistics and supply chain management in the context of incomplete information and methods that are used to investigate situations of risk and uncertainty.
The article covers issues of supply chain modeling being an important step in the decision-
making process. Logistics and supply chain management consider movement and transition
of material flow as well as financial, information and other flows associated with it. The
characteristics of a flow must be measured considering the dynamics of the flow movement.
This determines the importance of simulation modeling in decision-making support system as
the approach involves the implementation of modeling system algorithm functioning in a virtual
The literature analysis, on the one hand, allowed to conclude that the traditional approach
to functioning process characteristics determination involving consistent problem solving on
the level of supply chain element, is limited or not reflecting the specifics of real processes
where the parameters variability in time is possible. On the other hand, there is lack of specific
recommendations on building models based on principles of system dynamics as a simulation
modeling tool which allows to consider process characteristics variability. This determines
the aim of the research a part of which presents supply chain simulation model
illustrating the possibilities of the approach. The results of the work can be used both in practice
for industrial enterprises and for future research.
The current state of management practice is characterized by the presence of a demand to improve the efficiency and effectiveness of logistics processes, on the one hand, and an insufficient level of application of one of the main tools for achieving this goal - optimization modeling, on the other. One of the main reasons of this phenomenon is the lack of a universal basis of the proposed optimization models that does not allow them to be applied widely enough in companies with different business process structures. The aim of the research was to develop a universal, based on the SCOR framework, integrated model for optimizing the logistics service of an enterprise. During the research process, the overview of the developed models for logistics service optimization, the analysis of the limitations of the logistics system optimization models, the adaptation of the map of SCOR process metrics have been carried out; the influence diagram of the optimization model components has been developed; the models of cost optimization and logistics service optimization have been combined into a single integrated optimization model; an algorithm for the optimal solution search has been elaborated; the implementation of the model and algorithm as a program for the solution search has been introduced. As a result, the integrated optimization model based on the components of the SCOR model has been developed, combining the cost and service level optimization models, using the outputs of one model as inputs for another when searching for an optimal solution. Building the optimization model on the basis of the SCOR model components provides universal character of its application, taking into account the set of costs, arising in a logistics system, including indirect ones, and the set of metrics of logistics service reflects the links between functional departments, the ability to maintain a level of total costs at the efficiency frontier, achieving the goal of the profit maximization at the same time, provides a link between the tactical and operational levels of decision-making, which together leads to an increase in the reasonableness and quality of management decisions, and creates prerequisites for the overall optimal functioning of an enterprise logistics system.