MAGAZINE  №4 (99) August 2020

AUTHORS ASLAKHANOV A.R., PAVLOVA E.V.

CATEGORY  Information technologies in logistics and SCM Logistics service management Optimization and mathematical modelling Simulation modelling

 

ABSTRACT

In recent years, both in Russia and worldwide, there has been an annual increase in the number of museum visitors with the most popular exhibitions being visited by millions of people. In 2020, in the context of quarantine measures caused by the COVID-19 pandemic, the issue of managing museum visitors flows has become especially acute. If earlier museums throughput was limited by the maximum duration of possible evacuation from a museum building, exhibition space and the number of employees working with the visitors, in 2020, due to the observance of sanitary and epidemiological rules, the throughput of museums was further reduced. This determines the relevance of analytical solutions for museums as in order to manage visitor flows and adapt services to the high demand, it is necessary to have an effective forecasting model that takes into account the determinism of demand by a number of factors. The purpose of this paper is to develop a forecasting model for the number of excursion groups in specification museum-day-hour. A modification of random forest with the inclusion of more than 450 independent variables in the model is proposed as a forecasting method. The modification of the model relies on changing the mechanism for combining forecasts of trees in the forest in such a way that the weight of the tree in the model is inversely proportional to the measurement error of this tree. The proposed model is tested on the basis of data on more than 20,000 excursion groups of the State Russian Museum for the period 2018-2020. The proposed model showed high accuracy (36.6% WAPE and 0.5% BIAS).

 

 Electronic version

 Keywords:  forecasting machine learning random forest combining forecasts Python forecast museum Service service quality

MAGAZINE №4(87) August 2018

AUTHOR KUZNETSOV V.O. - Postgraduate student, Department of Logistics and Supply Chain Management, National Research University Higher School of Economics (St.-Petersburg, Russia)

CATEGORY Supply chains reliability and sustainability Analysis in logistics and SCM

ABSTRACT

One of the options for a more flexible approach to analyzing the reliability of supply chains is the principal component analysis (PCA). With a large number of variables describing supply chain, it is a difficult task to analyze the structure of variables in two-dimensional space. Within the analysis of the variables dependencies PCA allows to go from multidimensional space to low-dimensional space, leaving the most informative data in the array for analysis. Based on the generated data set, this paper demonstrates a possibility of applying PCA to supply chain reliability analysis. The generated data set includes observations of 50 supply chains described by five variables. Based on the array, maximizing the linear combination of parameters for each observation, we determined load coefficients and estimates of each of the main components. The calculation of these coefficients made it possible to move from multidimensional space to a two-dimensional one. The two-dimensional representation of all the data whose axes are the first two main components, explaining 84% of the variance, allows to see the structure of all supply chains, to identify outsiders and leaders in this set.

 Electronic version

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Wednesday, 02 January 2019 20:45

Intellectual Supply Chain Creation

MAGAZINE №4(87) August 2018

AUTHORS KOREPIN V.

CATEGORY Analytics and reviews Information technologies in logistics and SCM Modern concepts and technologies in logistics and SCM

ABSTRACT

This article examines main influence of the Fourth industrial revolution announced in 2011 and of a lot of new technologies penetrating everyday life on existing processes in supply chain management and on its future development. The article reviews components of intelligent supply chains which are divided into two unequal groups: revolutionary and evolutionary technologies. The former ones are Internet of things, machine learning, 3d printing, blockchain, artificial intelligence and bots -all of which radically change the logic of supply chain work. The second group – evolutionary technologies – is more about successfully developing graphic technologies and functional organization of a workplace. The article mainly focuses on the first group. As a result, the article concludes that all these technologies closely intersect, which forms additional synergic effect of their contact; and that functions and responsibilities of an expert of supply chain management in a company should be thoroughly revised.  

 Electronic version

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