Forecast model of international project business : partial revenue recognition method

Abstract

This study investigates a solution for a challenge in developing a group-level financial forecasting model for the project business of an international company. The primary goal of the study was to create a group-level long-term forecasting tool based on data from various source systems within the organization. The study is divided into two sections: a theoretical section and an empirical section. The theory section describes POC method, where the net sales is recognized over time as a revenue by using partial revenue recognition. Since listed group companies must adhere to International Financial Reporting Standards (IFRS) for partial revenue recognition, the theoretical section of this study focuses on IFRS 15, which outlines the method for partial revenue recognition. The empirical section of the study addresses the practical challenge of forecasting in project-based businesses at group level. The modelling was conducted in Excel, integrating data from various sources systems into a unified system to enable comprehensive analysis within the group. This study employed a mixed-methods approach, utilizing both quantitative data from the financial records of the group companies and qualitative insights gathered through interviews with company personnel. The quantitative data provided a comprehensive basis for analysis, while the qualitative interviews helped interpret the numbers and provided a deeper understanding of the project business of different country units. This combined approach allowed for a more nuanced interpretation of the data and revealed key insights that would not have been evident with quantitative analysis alone. The research resulted in the development of a forecasting model based on project profiling. By combining various features, a reliable method for classifying projects across different business activities was established. This approach enables forecasting based on project behaviour models, utilizing historical data to predict future outcomes. This study shows the potential of project profiling as an effective tool for improving forecasting accuracy. By combining and leveraging historical data from different source systems, the model provides a robust framework for predicting project outcomes across different business contexts. This approach contributes to better risk management and decision-making in group-level long-term forecasting.