THE APPLICATION OF FORECASTING SALES OF SERVICES TO INCREASE BUSINESS COMPETITIVENESS
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F20%3A10245190" target="_blank" >RIV/61989100:27510/20:10245190 - isvavai.cz</a>
Result on the web
<a href="https://www.cjournal.cz/files/367.pdf" target="_blank" >https://www.cjournal.cz/files/367.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.7441/joc.2020.02.06" target="_blank" >10.7441/joc.2020.02.06</a>
Alternative languages
Result language
angličtina
Original language name
THE APPLICATION OF FORECASTING SALES OF SERVICES TO INCREASE BUSINESS COMPETITIVENESS
Original language description
The accurate forecasting of business variables is a key element for a company's competitiveness which is becoming increasing necessary in this globalized and digitalized environment. Companies are responding to this need by intensifying accuracy requirements for forecasting economic variables. The objective of this article is to verify the correctness of the models predicting revenue in the service sector against 6 precision criteria to determine whether the use of certain criteria may lead to the adoption of particular models to improve competitive forecasting. This article seeks to determine the best accuracy predictors in 32 service areas broken down by NACE. Exponential smoothing models, ARIMA models, BATS models and artificial neural network models were selected for the assessment. Six criteria were chosen to measure accuracy using a group of scale-dependent errors and scaled errors. Services for which the result was ambiguous were subject to complete forecasting, both ex-post and ex-ante. Based on the analysis, the main result of the article is that only two types of services do not achieve the same accuracy results when using other measure criteria. It can therefore be said that for 93.75% of services, an assessment according to one precision parameter would suffice. Thus, a model's competitiveness is not affected by the choice of accuracy.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
50206 - Finance
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of Competitiveness
ISSN
1804-171X
e-ISSN
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Volume of the periodical
12
Issue of the periodical within the volume
2
Country of publishing house
CZ - CZECH REPUBLIC
Number of pages
16
Pages from-to
90-105
UT code for WoS article
000546258100007
EID of the result in the Scopus database
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