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Title | Financial modeling trends for production companies in the context of Industry 4.0 |
Authors |
Kartanaitė, I.
Kovalov, Bohdan Leonidovych Kubatko, Oleksandr Vasylovych Krušinskas, R. |
ORCID |
http://orcid.org/0000-0002-1900-4090 http://orcid.org/0000-0001-6396-5772 |
Keywords |
Industry 4.0 financial modeling production management artificial intelligence |
Type | Article |
Date of Issue | 2021 |
URI | https://essuir.sumdu.edu.ua/handle/123456789/83359 |
Publisher | LLC “СPС “Business Perspectives” |
License | Creative Commons Attribution 4.0 International License |
Citation | Inga Kartanaitė, Bohdan Kovalov, Oleksandr Kubatko and Rytis Krušinskas (2021). Financial modeling trends for production companies in the context of Industry 4.0. Investment Management and Financial Innovations, 18(1), 270-284. doi:10.21511/imfi.18(1).2021.23 |
Abstract |
Over the years, technological progress has accelerated highly, and the speed, flexibility, human error reduction, and the ability to manage the process in real time have
become more critical and required production companies to adapt production and
business models according to the needs. The demand for real-time decision support
systems adapted to these raising business needs is continuously growing. Nevertheless,
businesses usually face challenges in identifying new indicators, data sources, and appropriate financial modeling methods to analyze them. This paper aims to define and
summarize the main financial/economic forecasting methods for production companies in the context of Industry 4.0. Main findings show forecasting accuracy of up to
96% when combining economic and demand information, optimal forecasting period
from 10 months to five years, more frequent use of soft indicators in forecasting, the
relationship between company’s size and production planning. Four groups of indicators used in financial modeling, such as (I) production-related, (II) customers’ and
demand-oriented, (III) industry-specific, and (IV) media information indicators, were
separated. The analysis forms a suggestion for decision-makers to pay more attention
to the forecasting object identification, indicators’ selection peculiarities, data collection possibilities, and the choice of appropriate methods of financial modeling. |
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