Dynamics of business activities often force managers to make decisions based on subjective mental models, reflecting their experiences. To support the decision-making process, it is necessary to investigate quantitative forecasting models that generate interpretable predictions while enabling intuitive parameter adjustments. Also, global economic indicators are included in the analysis to gain a better understanding of external influences.

To introduce quantitative models for generating predictions, the forecasting process is examined based on conceptual foundations of time series analysis. Relevant Machine Learning (ML) and statistical forecasting models are critically analysed and economic indicators as explanatory variables are evaluated. The forecasting model is proposed based on model accuracy and interpretability.

The critical analysis of forecasting models emphasises a regression model provided by “Prophet” that handles non-linear data and time series components automatically. It enables parameter adjustment based on domain knowledge and therefore improves interpretability. Also, the output of the model demonstrates an increase in forecast accuracy compared to commonly used benchmark models by including the Gross Domestic Product (GDP) as an additional regressor. In addition, the empirical research serves as a guidance to standardise the overall approach on generating predictions. The proposed model allows to incorporate data-driven forecasts in the decision-making process and thus counters biased and subjective inputs posed by the AS-IS situation at E+H.