Digitalisierung des Forecasting-Prozesses am Fallbeispiel eines Automobilherstellers

Abstract

Forecasting and planning face growing challenges in today’s volatile global markets, shaped by geopolitical crises, pandemics, and rapid technological transformation. Traditional forecasting methods are increasingly showing their limitations — they are often too rigid, slow, and not adaptable enough. While topics such as digitalisation, artificial intelligence (AI), and predictive analytics (PA) have been widely studied, there is still a notable gap in research concerning their concrete application in financial forecasting within the automotive sector. The aim of this thesis is to examine how digital technologies – particularly AI, PA, and planning software – can enhance forecast accuracy and reduce manual effort. The analysis focuses on identifying opportunities and risks, deriving technical and organizational requirements for digital tools, and formulating actionable recommendations for implementing digital forecasting processes in the automotive industry. Methodologically, the study combines a systematic literature review with a qualitative case study. The empirical part consists of expert interviews, a document analysis, and a utility value analysis. This mixed-methods approach enables a well-founded assessment of the current state, potential, and implementation strategies for digitalising forecast processes. The literature highlights significant advantages of predictive methods in forecasting: improved accuracy, integration of external influencing factors, and reduced bias through algorithmic objectivity. Machine learning (ML) allows for dynamic and partially automated forecast adjustments. However, manual and Excel-based processes continue to dominate in practice. A lack of system integration, heterogeneous data environments, and high coordination efforts often hinder digital transformation. Key success factors include standardization, data quality, the use of suitable software solutions, and both, technical and organizational integration. Major challenges remain in areas such as employee expertise, data availability, and the acceptance of AI-supported systems. Digitalising the forecasting process requires more than technological tools; it demands a structural and cultural shift. Organizations must build data literacy, standardize processes, and strategically manage the deployment of digital tools. Close collaboration between finance, IT, and operational departments, along with effective change management, is essential to successfully implement and scale digital forecasting systems.

Date of Award	2025
Original language	German (Austria)
Supervisor	Peter Hofer (Supervisor)