You’re studying business informatics, business mathematics, logistics or a comparable field in a Bachelor''s or Master’s program with very good performance. You’re want to support us for 5–6 months as a Bachelor’s or Master’s student. You have solid programming skills, e.g., in Python or R. You’ve already gained some practical experience in logistics or supply chain management.

You’ll support the 4flow research team in application-oriented studies. You’ll work on scientific questions from data preparation and analysis to modeling, evaluation, and interpretation. Always in close collaboration with our experienced logistics researchers. You’ll process data from various sources and expert interviews and integrate them into a scalable analysis pipeline. You’ll implement models in Python (or similar) and evaluate their quality using established metrics. Inventory Optimization in Multi-Tier Supply Chains Reducing inventory can lead to significant cost savings. However, to ensure delivery reliability in volatile environments, inventory is essential at various stages of the supply chain. This topic focuses on developing and enhancing methods for holistic inventory optimization, considering uncertain lead and replenishment times. The goal is to ensure practical applicability by integrating diverse industry-specific characteristics. AI-Driven Transparency in Inbound Supply Chains for Risk Management Lack of visibility beyond Tier-1 suppliers makes inbound supply chains vulnerable to unforeseen risks and delays proactive action. This topic aims to develop an innovative ML/GenAI framework that automatically predicts geographic waypoints and transport modes, enabling data-driven insights to support inbound logistics management. Model for Predicting Logistics Infrastructure Usage in Transportation Limited transparency regarding logistics hubs used within supply chain routes leads to delayed risk detection and missed optimization opportunities. This topic involves developing an ML/GenAI framework that automatically predicts transshipment points and connections based on known origin-destination pairs, strengthening logistics planning through data-driven decision support.