A Suitable Artificial Intelligence Model for Inventory Level Optimization

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26510%2F16%3APU119403" target="_blank" >RIV/00216305:26510/16:PU119403 - isvavai.cz</a>

Result on the web

<a href="https://trends.fbm.vutbr.cz/index.php/trends/article/view/344" target="_blank" >https://trends.fbm.vutbr.cz/index.php/trends/article/view/344</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.13164/trends.2016.25.48" target="_blank" >10.13164/trends.2016.25.48</a>

Result language

angličtina

Original language name

A Suitable Artificial Intelligence Model for Inventory Level Optimization

Original language description

Purpose of the article: To examine suitable methods of artificial neural networks and their application in business operations, specifically to the supply chain management. The article discusses construction of an artificial neural networks model that can be used to facilitate optimization of inventory level and thus improve the ordering system and inventory management. For the data analysis from the area of wholesale trade with connecting material is used. Methodology/methods: Methods used in the paper consists especially of artificial neural networks and ANN-based modelling. For data analysis and preprocessing, MS Office Excel software is used. As an instrument for neural network forecasting MathWorks MATLAB Neural Network Tool was used. Deductive quantitative methods for research are also used. Scientific aim: The effort is directed at finding whether the method of prediction using artificial neural networks is suitable as a tool for enhancing the ordering system of an enterprise. The research also focuses on finding what architecture of the artificial neural networks model is the most suitable for subsequent prediction. Findings of the research show that artificial neural networks models can be used for inventory management and lot-sizing problem successfully. A network with the TRAINGDX training function and TANSIG transfer function and 6-8-1 architecture can be considered the most suitable for artificial neural network, as it shows the best results for subsequent prediction.. Conclusions resulting from the paper are beneficial for further research. It can be concluded that the created model of artificial neural network can be successfully used for predicting order size and therefore for improving the order cycle of an enterprise.

Czech name

—

Czech description

—

Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

AE - Management, administration and clerical work

OECD FORD branch

—

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2016

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

TRENDY EKONOMIKY A MANAGEMENTU

ISSN

1802-8527

e-ISSN

—

Volume of the periodical

10

Issue of the periodical within the volume

25

Country of publishing house

CZ - CZECH REPUBLIC

Number of pages

8

Pages from-to

48-55

UT code for WoS article

—

EID of the result in the Scopus database

—