PAWLICZEK, Adam, Lenka TKAČÍKOVÁ, Ladislav MORAVEC, Daniela NAVRÁTILOVÁ and Renáta PAVLÍČKOVÁ. COMPARISON OF MINING PREDICTION WITH REAL MINING AS A TOOL FOR STRATEGIC MANAGEMENT. Online. In 20th International Multidisciplinary Scientific GeoConference SGEM 2020. Bulharsko: International Multidisciplinary Scientific Geoconference SGEM, 2020, p. 43-50. ISBN 978-619-7603-06-4. Available from: https://dx.doi.org/10.5593/sgem2020/1.2/s03.006.
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Basic information
Original name COMPARISON OF MINING PREDICTION WITH REAL MINING AS A TOOL FOR STRATEGIC MANAGEMENT
Authors PAWLICZEK, Adam (203 Czech Republic), Lenka TKAČÍKOVÁ (203 Czech Republic, belonging to the institution), Ladislav MORAVEC (203 Czech Republic, belonging to the institution), Daniela NAVRÁTILOVÁ (203 Czech Republic, belonging to the institution) and Renáta PAVLÍČKOVÁ (203 Czech Republic, belonging to the institution).
Edition Bulharsko, 20th International Multidisciplinary Scientific GeoConference SGEM 2020, p. 43-50, 8 pp. 2020.
Publisher International Multidisciplinary Scientific Geoconference SGEM
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 50204 Business and management
Country of publisher Bulgaria
Confidentiality degree is not subject to a state or trade secret
Publication form electronic version available online
WWW URL
RIV identification code RIV/26867184:_____/20:N0000030
Organization unit Moravian Business College Olomouc
ISBN 978-619-7603-06-4
ISSN 1314-2704
Doi http://dx.doi.org/10.5593/sgem2020/1.2/s03.006
Keywords in English Econometric models; comparison of results; managerial decisions; strategic management
Tags RIV20
Changed by Changed by: Ing. Michaela Nováková, učo 5293. Changed: 5/5/2021 13:12.
Abstract
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.
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