2020
COMPARISON OF MINING PREDICTION WITH REAL MINING AS A TOOL FOR STRATEGIC MANAGEMENT
PAWLICZEK, Adam, Lenka TKAČÍKOVÁ, Ladislav MORAVEC, Daniela NAVRÁTILOVÁ, Renáta PAVLÍČKOVÁ et. al.Základní údaje
Originální název
COMPARISON OF MINING PREDICTION WITH REAL MINING AS A TOOL FOR STRATEGIC MANAGEMENT
Autoři
PAWLICZEK, Adam (203 Česká republika), Lenka TKAČÍKOVÁ (203 Česká republika, domácí), Ladislav MORAVEC (203 Česká republika, domácí), Daniela NAVRÁTILOVÁ (203 Česká republika, domácí) a Renáta PAVLÍČKOVÁ (203 Česká republika, domácí)
Vydání
Bulharsko, 20th International Multidisciplinary Scientific GeoConference SGEM 2020, od s. 43-50, 8 s. 2020
Nakladatel
International Multidisciplinary Scientific Geoconference SGEM
Další údaje
Jazyk
angličtina
Typ výsledku
Stať ve sborníku
Obor
50204 Business and management
Stát vydavatele
Bulharsko
Utajení
není předmětem státního či obchodního tajemství
Forma vydání
elektronická verze "online"
Odkazy
Kód RIV
RIV/26867184:_____/20:N0000030
Organizační jednotka
Moravská vysoká škola Olomouc
ISBN
978-619-7603-06-4
ISSN
Klíčová slova anglicky
Econometric models; comparison of results; managerial decisions; strategic management
Štítky
Změněno: 5. 5. 2021 13:12, Ing. Michaela Nováková
Anotace
V originále
This paper responds to published scientific papers which compile econometric models of extraction of selected mineral resources. Mining prediction models can be new tools to increase the competitiveness of mining enterprises. Comparing the results of mining prediction and real data is important for further research on the issue. Specification of the results will lead to better managerial decisions in strategic and operational management. The results of the paper can lead to the clarification of the so-called random component in econometric models and the refinement of the assembled models. Random components are different from macroeconomic indicators. These components cannot be quantitatively captured in calculations in terms of econometric models. These components are influenced by the economic models of the mineral extraction prediction. The aim of the paper is to estimate random components in the future when using mining predictions as support for managerial decisions. Other mining activities may react differently to other random components.