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. AcknowledgmentThis work was partly supported by Project No. 0121U100470 “Sustainable development and resource security: from disruptive technologies to digital transformation of Ukrainian economy”.
The curiosity of how startups become unicorns is increasing. Only one-fifth of unicorns operating in the world trade their shares publicly. Financial data from the balance sheets and profit (loss) statements of 97 unicorns, which had IPOs between 2009-2018, was collected with the aim to analyse what specific characteristics of financial ratios over a particular IPO related period can be identified for unicorns operating in different regions and sectors. ANOVA was used to analyse the financial efficiency from different perspectives: (I) the financial profile of a unicorn, (II) the financial efficiency of a unicorn based on the business sector (Software; Products and Services; Technology; Internet and Healthcare sectors), and (III) the financial efficiency of a unicorn based on the region of origin (US+, Europe and Asia). Research showed that unicorns are mostly financed by investors, but remain unprofitable. Positive profitability was found in Europe, and the highest liquidity - in Healthcare sector.
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