MAGAZINE  №2-3 (103) - 2021

AUTHORS 

INYUTSINA V.S. - Data Analyst, OZON (Moscow, Russia)

NOVIKOV V.E. 

CATEGORY  Supply chain planning Information technologies in logistics and SCM Modern concepts and technologies in logistics and SCM

ABSTRACT

Nowadays, e-commerce became one of the most dynamic technology markets in Russia. It is gradually becoming an essential part of the national economy. This trend has intensified especially during the pandemic, when online shopping became an affordable tool of necessity. The growing demand has determined the basic principles of leadership on the e-commerce market, such as having a wide coverage of regions, fast and free delivery due to a developed logistics infrastructure and accurate sales forecasting. At the current stage of e-commerce development, participants have entered the level of competition through the use of the latest technologies, among which are: artificial intelligence, augmented reality and machine learning tools.

For the company in 2021, the operational efficiency is at the most priority. Marketplace will focus on launching new and developing current products and services, increasing the supplier base, and expanding the logistics infrastructure.

The main result of this study is an analysis of the state of the logistics infrastructure in an online retail company, including a generalization of the number of pick-up points and automated post offices in each region, as well as their throughput. In addition, the long-term short-term memory (LSTM) neural network will be used as the main solution to the problem. The final result is the selection of various regions in which the existing logistics infrastructure of the company does not agree with the predicted volume of future sales, and a method for calculating the demand for a pickup point required to cover the demand will be proposed.

Before starting the experiment, a basic analysis of business and logistics will be carried out to understand the current state of the infrastructure. This will be followed by an analysis of the current process for managing the network of pick-up points, with a further understanding of the models and data used. The importance of this part is due to one of the goals of this study - it offers the company an innovative approach to the points of issue of orders. In addition, a review of networks about the available neural structures will be carried out with a justification of the model, which will be announced in the experiment. Possible examples of neural network structures for studying are recurrent neural networks, LSTMs, multilayer perceptrons, and others.

The company's research will expand market coverage, reduce lost sales and overall lead times.

 Electronic version

 Keywords:  

 
Published in Supply chain planning

MAGAZINE №3 (74) June 2016

AUTHOR 

PENZEV V.N.

KULAKOV V.N. - Manager of product distribution, LLC "IRWIN 2" (Moscow, Russia)

CATEGORY Optimization and economic-mathematical modeling Planning the supply chain 

ABSTRACT

The deterioration of the economic situation in the world and destabilization of many sectors of the economy create absolutely new conditions for the work to various companies to which they have to adapt. The task of increasing the efficiency of using resources in the current economic environment is extremely relevant, and interest to the forecasting as a tool and one of the stages of the planning process is growing constantly. This article considers a classical method of seasonal decomposition of time series. It is also presented a modified method of seasonal decomposition of time series. The main objective of the creation of the modified method was to increase the flexibility of the forecasting by highlighting the target optimization parameter. This allows managers to create different forecast scenarios. The example of forecasting sales product groups was considered for both classical and modified models. The main features and differences between methods were either described. The quality of the sales forecast was assessed and results obtained by both forecasting methods were compared. According to the results, the modified model has shown the best results in comparison with the classical one.

 Keywords:   

Published in Supply chain planning

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