Original scientific paper
https://doi.org/10.31784/zvr.14.1.8
Effectiveness of forecasting using machine learning for controlling needs in the decision-making process
Uwe Lebefromm
; Cooperative State University Mannheim, Faculty of Business Informatics, Mannheim. Germany
Neda Vitezić
orcid.org/0000-0001-7495-1069
; University of Rijeka, Faculty of Economics and Business, Rijeka, Croatia
*
Antonija Petrlić
orcid.org/0000-0001-9750-4923
; University of Rijeka, Faculty of Economics and Business, Rijeka, Croatia
* Corresponding author.
Abstract
The subject of this research is the development and application of prediction models that are created with the help of mathematical and statistical methods and artificial intelligence, i.e. machine learning. The aim of the work is to develop a model for the implementation of digital transformation in operational decision-making processes and controlling and management requirements. Controlling as a discipline and function in a company is responsible for analysing existing and predicting future business using sophisticated methods and tools. This paper shows that business forecasting based on historical facts has evolved into business forecasting estimated on the basis of machine learning. The paper presents an innovative stochastic forecasting model using the example of the provision of services in a marina in Croatia. The results of the study are four presented models for contracting and acquisition of customers using the services of a marina. The results obtained confirm the high reliability of the forecasting model for the use of digital tools for controlling needs in the decision-making process. Through the developed methods of converting manual processes into optionally automated decision-making processes, this work contributes to the innovation of controlling in a digital environment.
Keywords
forecasting; machine learning; controlling; decision-making
Hrčak ID:
348091
URI
Publication date:
3.7.2026.
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