Book
Proceedings of Analytics for Management and Economics Conference AMEC 2019
Key topics of the conference:
- Empirical economics, firms and industry studies
- Quantitative corporate and international finance
- SMART marketing and customer analytics
- New HR trends and people analytics
- Game theory and market design
- Institutions of Public Sector: Empirical Evidence
- Intangible-driven economy and data-led business models
- BRICS: emerging trends, leapfrogging and reverse innovations
- National and regional innovation systems
- Corporate innovations and competitive advantages
- Big data for business and economics studies
Collaboration and trust relationships are important success factors in supply chain management. However, in practice relationships between counterparties in supply chain face conflicts preventing from building ideal supply chain collaboration. This paper proposes a conceptual framework of agent-based model that helps to understand how individual behavior of counterparties in conflict situations and collaboration strategy effect on supply chain efficiency in dynamics. The research is based on Russian retail case study, describing a grocery sector where key market stakeholders are retailers and suppliers (manufacturers). The important feature of Russian grocery sector is a dominating power of retailers over suppliers. Author investigates the main drivers of conflicts in retailer-supplier’s relationships and offers a specification of agent-based model.
In recent years, an increase in interest in museum activities has been observed, the number of museum visitors shows a stable positive trend. The research question of this paper is as follows: which months for museums have maximum and minimum attendance rates? The aim of the research is to predict the number of visitors to United Kingdom museums per month, both at the macro-level (country) and at the level of individual museums. For forecasting purposes, seasonal multiplicative forecasting models with a linear and logarithmic trend are used. The used model makes it possible to identify the seasonal component of each month in the structure of demand for museum services. Three indicators are used to assess the accuracy of the model: Mean Error (ME), Mean Average Percentage Error (MAPE), and Root Mean Square Error (RMSE). The obtained results allow us to use the developed model for a point forecast of the number of visitors to UK museums for the month. In addition, seasonal components of each month were identified. These indicators make it possible to identify the most active and passive months of visiting museums, and can be used to arrive at managerial decisions concerning the organization of work with museum visitors.
The article introduces the notion of public governance analytics as the set of instruments and results of public governance practice analysis, and of proposals for practice of governance improvement. Main requrrements for analytic of public governance are defined. Preliminary hypothesis about the sources of differences between analytics of public governance and of public governance as scientific research area. and why the results of analytics of publci governance are not recognized as the scientific articles, is proposed.
Comment expliquer le ralentissement de la croissance russe en 2013 ? Quelles réformes ont été engagées dans les industries spatiales et navales ? La libération de l’ancien patron de Ioukos Mikhaïl Khodorkovski annonce-t-elle une ouverture politique de la part de Vladimir Poutine ? Dans quelles valeurs la société russe se reconnaît- elle ? Quels sont les enjeux de la réforme controversée de l’Académie des sciences ? La Russie a-t-elle une stratégie en Arctique ? Pourquoi les Jeux olympiques de Sotchi ont-ils coûté plus cher que prévu ? Les crises syrienne et ukrainienne illustrent-elles le « retour » du Kremlin sur la scène internationale ou les limites de son influence ?
« Russie 2014 », deuxième rapport annuel de l’Observatoire franco- russe, a pour ambition de fournir l’analyse la plus complète possible de la situation en Russie. Économie, politique intérieure et société, régions, politique étrangère et « miscellanées franco-russes », illustrant l’ancienneté, la diversité et la richesse exceptionnelle des relations entre nos deux pays, font de cet ouvrage un document de référence.
Les auteurs
Dirigé par Arnaud Dubien, directeur de l’Observatoire franco-russe et ancien directeur de recherche à l’IRIS, « Russie 2014 » réunit les contributions d’une cinquantaine d’experts russes et français reconnus, parmi lesquels Alain Blum, Isabelle Facon, Evgueni Gavrilenkov, Sergueï Karaganov, Nathalie Lapina, Fiodor Loukianov, Rouslan Poukhov, Jean Radvanyi, Marie-Pierre Rey, Konstantin Simonov, Anne de Tinguy.
In this paper we consider one approach to solving the problems of organizing an effective and "friendly" service that implements a web-environment analytical functionality of a multi-dimensional storage of macroeconomic indicators of the economy.
Workshop proceedings
High performance querying and ad-hoc querying are commonly viewed as mutually exclusive goals in massively parallel processing databases. Furthermore, there is a contradiction between ease of extending the data model and ease of analysis. The modern 'Data Lake' approach, promises extreme ease of adding new data to a data model, however it is prone to eventually becoming a Data Swamp - unstructured, ungoverned, and out of control Data Lake where due to a lack of process, standards and governance, data is hard to find, hard to use and is consumed out of context. This paper introduces a novel technique, highly normalized Big Data using Anchor modeling, that provides a very efficient way to store information and utilize resources, thereby providing ad-hoc querying with high performance for the first time in massively parallel processing databases. This technique is almost as convenient for expanding data model as a Data Lake, while it is internally protected from transforming to Data Swamp. A case study of how this approach is used for a Data Warehouse at Avito over a three-year period, with estimates for and results of real data experiments carried out in HP Vertica, an MPP RDBMS, is also presented. This paper is an extension of theses from The 34th International Conference on Conceptual Modeling (ER 2015) (Golov and Rönnbäck 2015) [1], it is complemented with numerical results about key operating areas of highly normalized big data warehouse, collected over several (1-3) years of commercial operation. Also, the limitations, imposed by using a single MPP database cluster, are described, and cluster fragmentation approach is proposed.
This paper describes an approach for fast ad-hoc analysis of Big Data inside a relational data model. The approach strives to achieve maximal utilization of highly normalized temporary tables through the merge join algorithm. It is designed for the Anchor modeling technique, which requires a very high level of table normalization. Anchor modeling is a novel data warehouse modeling technique, designed for classical databases and adapted by the authors of the article for Big Data environment and a MPP database. Anchor modeling provides flexibility and high speed of data loading, where the presented approach adds support for fast ad-hoc analysis of Big Data sets (tens of terabytes). Different approaches to query plan optimization are described and estimated, for row-based and column-based databases. Theoretical estimations and results of real data experiments carried out in a column-based MPP environment (HP Vertica) are presented and compared. The results show that the approach is particularly favorable when the available RAM resources are scarce, so that a switch is made from pure in-memory processing to spilling over from hard disk, while executing ad-hoc queries. Scaling is also investigated by running the same analysis on different numbers of nodes in the MPP cluster. Configurations of 5, 10 and 12 nodes were tested, using click stream data of Avito, the biggest classified site of Russia.
портовый менеджмент, показатели деятельности, анализ эффективности, система учета, распределение издержек, методы анализа деятельности портовой системы
At present many industries reveal tendency for setting up of vertically integrated companies (VIC) the structure of which unites all technological processes. This tendency proved its efficiency in oil industry where coordination of all successive stages of technological process, namely, oil prospecting and production -oil transportation - oil processing - oil chemistry - oil products and oil chemicals marketing, is necessary. The article considers specific features of introduction of "personnel management" module at enterprises of oil and gas industry.
vertically integrated companies; personnel management