Models of the development strategy of the information and management system of logistics of the trade enterprise are an integral component that will increase the efficiency of trade enterprise management. Such a solution to the problem is possible using the neural network apparatus. The experiment is performed using the analytical platform Deductor for the construction and visualization of Kohonen maps. The Kohonen network is studied by the method of successive approximations. During the iterative learning procedure, the network is organized in such a way that the elements that correspond to the centers located close to each other in the space of entrances will be located close to each other and on the topological map. Accordingly, the algorithm of self-learning maps shows how clustering of multidimensional vectors. The constructed Kochhonen maps, which show the ratio of assets and liabilities, income and expenses of the enterprise, provide an opportunity to analyze and determine the structural indicators of the enterprise and manage them. The use of the proposed models and methods made it possible to identify the main factors and sources of improving the information management system efficiency through the use of logistics approaches and system developments.
Management of the trade enterprise logistics information systems as part of a single information space of the organization and the use of various content and form components of the information resource management system is associated with the development of new approaches and means of combining the interaction of material, financial and information flows.
Copyright © 2020 for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0) Coordinated management of logistics information systems and the use of various in content and form components of the information resource of trade enterprise management systems is an important problem that requires the use of scientific approaches and means to solve it.
Designing an information management system for logistics of a trading enterprise is a complex and multifaceted process that uses all the achievements of modern information technologies, each of which makes it possible to manage the organization and interaction of structural units of the enterprise successfully.
One of the important ways to increase the efficiency of trading companies and networks is creating an effective information management system, which is a set of tools, techniques, artists, providing the information necessary and sufficient implementation of all operational management processes, and the use of modern information technology.
Thus, the design of a model strategy for the development of information and management systems of trade enterprise logistics is quite relevant and the introduction of such an information system will increase the efficiency of the management of trade enterprises.
To solve this problem, it is proposed to use the means of the neural network apparatus. The increase in information about all production, technological processes and equipment, raw materials and their quality, range of updated products, staff skills, market relations lead to a significant complication of management tasks and especially in making the right effective decision, implementation of business ideas, creating the strategy of development in the process of enterprise management and the industry as a whole.
An important role is played by information and management systems, which combine the positive qualities of technical electronic means of collecting information, it is processing, and the person as a direct participant in the formation of management actions, effective decision-making on enterprise management.
The article aims to use the method of construction of Kohonen maps to find patterns in the data, which allows for exploratory analysis of data, which differs from classical statistical procedures. Based on the forecast, obtain a prediction of the values of the time series for the number of samples, which corresponds to a given forecasting horizon (this is a financial structure that will correspond to the required level of commercial enterprises). Conduct an experiment using the Deductor analytical platform to build and visualize Kohonen maps. 2
Effective application of various approaches and methods of modeling to the strategy of
development of information and management system of the trade enterprise logistics
acquires a decisive advantage in building business models of trade enterprises. The use
of neural network apparatus is proposed, in particular the use of Kohonen networks and
Kohonen maps.
Kohonen mapping methods described in the works of authors Chang, Wui Lee, Pang,
Lie Meng, Tay Kai Meng [
But today the issue of using Cohonen networks and Cohonen maps in modeling the strategy of development of information and management system of logistics of a trading enterprise is not sufficiently revealed and studied. 3
One of the methods used to build commercial enterprise information and management logistic system is the use of artificial neural networks. Artificial neural networks are a collection of neural elements and the connections between them. The main element of a neural network is a formal neuron.
The principle of operation of such a neuron is as follows: the input signals ( ), which have the appropriate weights ( ), are grouped and pass through the transfer function, generate the result and at the final stage, the output is obtained.
Various learning algorithms and their modifications are used for network training.
One of the methods used to build commercial enterprise information and management
logistic system is the use of Kohonen neural networks and Kohonen maps [
Kohonen networks (layers) [
Clustering is the division of a large number of objects under study into groups of
identical (similar in properties) objects, called clusters [
Solving problems of classification is to assign each object to one of the predefined classes. The division of objects into clusters is carried out while forming clusters. Kohonen networks are used to cluster objects that are described by quantitative characteristics
Kohonen network has only two layers: input and output, called self-organizing map. The elements of the map are located in some space, usually two-dimensional.
Kohonen network operation algorithm: 1. Network initialization. The weights of the network are given small random values.
The initial neighborhood area is shown. 2. Presenting the network with a new input signal. 3. Calculation of the distance to all neurons in the network is the distances dj from the input signal to each neuron j are determined by the formula:
= ∑ =1( ( ) ∗ ( ))2 (1) ner. where is the th element of the input signal at time , ( ) is the importance of the connection from the th element of the input signal to the neuron at time . 4. Selection of the neuron with the shortest distance is the winning neuron ∗ is selected, for which the distance is the smallest. 5. Adjusting the weights of the neuron ∗ and its neighbors is the scales are adjusted for the neuron ∗ and all neurons from its vicinity . New weight values: ( + 1) = ( ) + ( )( ( ) − ( )). (2) 6. Return to step 2.
The algorithm uses the coefficient of learning speed, which is gradually reduced for a more subtle correction at a new level As a result, the position of the center is set in a certain place, which qualitatively clusters the examples for which the neuron is the win
In the learning algorithm, the correction is applied not only to the winning neuron
but also to all neurons in its current environment. As a result of this change of
environment, the initial rather large sections of the network migrate towards the educational
examples [
The network forms a rough structure of topological order, in which similar examples activate groups of neurons that are closed on the topological map. With each new era, the learning speed and the size of the environment decrease, thus within the areas of the map are more subtle differences, which ultimately leads to a more precise adjustment of each neuron. Often training is deliberately divided into two phases: shorter, highspeed training, and large neighborhoods, and longer training with low learning speed and zero or near-zero neighborhoods. Once the network is trained to recognize the structure of data (goods), it can be used as a visualization tool in data analysis.
The algorithm of functioning of self-learning maps (Self Organizing Maps, SOM) is
one of the options for clustering multidimensional vectors. An example of such
algorithms is the k-means algorithm [
The use of one- and two-dimensional grids is since there are problems with the display of larger dimension spatial structures (problems with reducing the dimension to two-dimensional appear again). Usually, neurons are located in nodes of a two-dimensional grid with rectangular or hexagonal cells. In this case, as already mentioned, neurons also interact with each other. The magnitude of this interaction is determined by the distance between the neurons on the map.
This is easy to see that a hexagonal grid distance between neurons longer coincides with the Euclidean distance than for a quadrangular grid. The number of neurons in the grid determines the degree of detail of the result of the algorithm, and as a result, the accuracy of the generalizing ability of the map depends on it.
Kohonen maps (self-organizing map or SOM) [
As a result, similar in some metric input vector network Kohonen belong to one neuron (cluster center), and Kohonen map can relate to different closely spaced on a grid of neurons. Usually, neurons are located in the nodes of a two-dimensional grid of rectangular or hexagonal cells. Neighbor neurons are determined by the distance between the neurons on the map.
Each cell corresponds to a neuron of the Kohonen network. The number of cells of
the map depends on the required detail of the image and is selected experimentally. For
each cell, one of the statistical characteristics of the selected component of the input
vectors trapped in the cell is calculated [
Management decisions at different hierarchical levels require increasing their objectivity, which is achieved by using economic and mathematical methods in their calculation methods including multivariate statistical cluster and factor analysis, and the relatively novel approach based on Kohonen maps.
The process of creating a self-organizing map involves establishing a connection
between the input and output layers of neurons and thus displays larger dimension data
on a smaller dimension map, consisting of “neural lattices” [
Assessment of the financial condition of the trade enterprise is carried out at a qualitatively new level, taking into account its structural specifics, receipts, and analysis of funds from sales, storage, and transportation, belonging to the relevant specialized groups and the relationship between the objects of each group. The platform for building and visualizing Kohonen maps is Deductor, an analytical platform that allows you to create an effective business decision support system in a short time.
With powerful import mechanism, Deductor possible to create a unified analytical
add-on to all existing company systems: “Information system protection;” “Defining
the strategy and technology of resource allocation;” “Optimization of hard wear and
soft wear systems;” “Transportation of goods;” “Sales of goods;” “Material flows of
logistics;” “Information logistics;” etc. [
Completed in Deductor based technology allows a single architecture to pass all
stages of construction analysis system—from creating data warehouses to automatic
selection of models and visualization of the results. Deductor provides the tools needed
to solve a variety of analytical tasks. Corporate reporting, forecasting, segmentation,
search for patterns—these and other tasks that use such analysis techniques as OLAP,
Knowledge Discovery in Databases, and Data Mining [
After processing the initial data in Deducto Studio Academic the tool “Sliding
Window” is used in Fig. 4. The Sliding Window tool converts a sequence of row values into
a table where adjacent records are represented as adjacent data fields (a window because
only some continuous section of data is allocated, a sliding one because that window
“moves” throughout the set). The need for quantitative data (tables) often occurs when
building models, analyzing, and forecasting time series when applying for entry models
mentioned several adjacent samples from the original data set [
Based on the created “Sliding Window” Data Mining tools are used: “Neural Network”
(Fig. 5) and “Self-organizing Kohonen maps” (Fig. 6) based on which the forecast of
the future periods is made Fig. 7.
Neural networks are self-learning models that mimic the activity of the human brain.
They are able not only to perform a once programmed sequence of actions on certain
data but also to analyze the coming data [
The main advantage of neural networks is the ability to build an effective nonlinear dependence, a more accurate description of data sets compared to linear methods of statistics. This data processor allows you to specify the structure of the neural network, determine its parameters, and teach using one of the algorithms available in the system.
The result will be a neural network emulator, which can be used to solve problems
of forecasting, classification, finding hidden patterns, data compression, and many
other applications [
Forecasting allows obtaining prediction values of time series on the number of samples that match the prediction horizon. This algorithm works as follows. Suppose that as a result of the transformation by the method of “sliding window” a sequence of time samples was obtained: (− ), . . . , (−2), − (− 1), 0, (+1) (4) where + 1 predicted value obtained by the previous processing step (for example, linear regression) based on previous values.
Then to build a forecast for the value (+2), you need to move the whole sequence,
one count, to the left, so that the previously made prediction (+1) is also included in
the number of initial values. Then the algorithm for calculating the predicted value will
be started again, and (+2) will be calculated taking into account (+1) and so on
according to the forecast given by the horizon [
Prediction built on the results of self-organizing Kohonen maps showed different results, which is logical.
Deducto Studio Academic allows you to perform various operations using Data Mining and analyzing the history of the periods of each of them to draw conclusions and use the desired result in the further activities of the trade enterprise. 6
We believe that the proposed apparatus of neural networks and Kohonen maps, which in contrast to the existing mathematical models reflects the results of the study in the form of visual two-dimensional maps, which allows increasing the efficiency of visualization and analysis of results and forecasting, allows not only to determine the most profitable logistics strategy but also to assess the financial condition of each object of study.
Accordingly, using the above models and methods, the main factors and sources of improving the management information system efficiency through the use of logistics approaches and system developments are identified.
The model of the strategy of development of the information-managing system of logistics of the enterprise of trade using the device of neural networks is presented. It suggests that this approach encourages companies to apply strategies based on cognitive scientific methods and methodologies.
In the future authors plan to research concepts of modeling of informational management system strategy to implement this in Smart City models.