Smarter Planning in Logistics: Intelligent Budget Planning Through Ai-Powered Predictive Models

In the logistics sector, data-driven forecasts are used for annual budget planning to allocate budgets across numerous cost centers. Companies often rely on conventional forecasting tools for this purpose. Within a project with an internationally operating logistics company, we further developed this established process using a data-driven approach. The core idea is the application of advanced time series methods and AI-based machine learning models, enabling more informed operational and strategic decision-making.

Objective: Improved Forecasting for Operational and Strategic Decision-Making

The project focuses on the question of what potential modern time series methods and AI-based machine learning models offer compared to existing forecasting tools. This makes it transparent where current forecasts already perform well and where the greatest opportunities for improvement lie. The implemented models are systematically compared with the company’s existing forecasts.

To ensure rapid transfer into practice, a Proof-of-Concept (PoC) application was developed, representing an initial prototype to test the approach under real-world conditions. It provides controllers with easy access to the new forecasts based on existing databases. The application is modular in design, allowing additional cost centers or locations within the corporate network to be independently integrated and analyzed.