DETERMINATION OF IRON PROCUREMENT STRATEGY FOR MANUFACTURING COMPANIES

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F75081431%3A_____%2F23%3A00002723" target="_blank" >RIV/75081431:_____/23:00002723 - isvavai.cz</a>

Výsledek na webu

<a href="https://jssidoi.org/jesi/article/1145" target="_blank" >https://jssidoi.org/jesi/article/1145</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

DETERMINATION OF IRON PROCUREMENT STRATEGY FOR MANUFACTURING COMPANIES

Popis výsledku v původním jazyce

The objective of this paper is to evaluate the price development of iron (steel rebar and hot rolled coil steel) on commodity exchanges, to determine the dependence of the price of iron on prices of other major commodities (crude oil and natural gas), to forecast its future development and to propose a particular iron procurement strategy for manufacturing companies in the South Bohemian Region until the end of 2028. The content analysis method was selected to evaluate the price development. It was also used to assess the dependence of iron prices on other major commodities, which was considered using the correlation analysis method. The artificial neural network method, multilayer perceptron networks, was selected and used to forecast future price development. All calculations are performed using Statistica software (version 13). Linear regression is conducted using different functions, with 1,000 neural structures being generated each time, out of which 5 structures showing the best characteristics are selected. These are retained to forecast future prices for the 2023-2028 period in three experiments. Results are presented in tables and graphs processed in Microsoft Excel. Based on the selected variants of future steel price forecasting, a specific iron procurement strategy can be recommended for manufacturing companies in the South Bohemian Region until the end of 2028.

Název v anglickém jazyce

DETERMINATION OF IRON PROCUREMENT STRATEGY FOR MANUFACTURING COMPANIES

Popis výsledku anglicky

The objective of this paper is to evaluate the price development of iron (steel rebar and hot rolled coil steel) on commodity exchanges, to determine the dependence of the price of iron on prices of other major commodities (crude oil and natural gas), to forecast its future development and to propose a particular iron procurement strategy for manufacturing companies in the South Bohemian Region until the end of 2028. The content analysis method was selected to evaluate the price development. It was also used to assess the dependence of iron prices on other major commodities, which was considered using the correlation analysis method. The artificial neural network method, multilayer perceptron networks, was selected and used to forecast future price development. All calculations are performed using Statistica software (version 13). Linear regression is conducted using different functions, with 1,000 neural structures being generated each time, out of which 5 structures showing the best characteristics are selected. These are retained to forecast future prices for the 2023-2028 period in three experiments. Results are presented in tables and graphs processed in Microsoft Excel. Based on the selected variants of future steel price forecasting, a specific iron procurement strategy can be recommended for manufacturing companies in the South Bohemian Region until the end of 2028.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

50200 - Economics and Business

Projekt

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Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

Entrepreneurship and Sustainability Issues

ISSN

2345-0282

e-ISSN

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Svazek periodika

11

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

LT - Litevská republika

Počet stran výsledku

18

Strana od-do

331-348

Kód UT WoS článku

001131808300008

EID výsledku v databázi Scopus

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