1. Automated Demand Forecasting in Production at Continental (Amit Tyagi and Lars Schleithoff)

In the Tire Division of Continental, demand planning is crucial as an input for the supply chain and previously involved mainly manual forecasting of almost 100 business experts. In this talk we will reveal how the Continental Advanced Analytics team, together with Informationsfabrik, created a machine learning framework that – deployed on a state-of-the-art infrastructure – today automates large parts of this tedious task. In particular, we will give insight into the problem complexity, concrete improvements achieved as well as the technical and – importantly – organizational challenges that arise from automating manual processes.

2. Deli Salad Sales Forecast at Kugler GmbH (Antje Dittmer and Maria-Alexandra Cimpeanu)

Kugler Feinkost GmbH manufactures a large variety of deli salads and distributes them to their customers – beer gardens, restaurants, and shops. With Machine Learning techniques a production forecast resulting in 100% deliverability and only 0.3% waste is achievable with suitable features e.g. ‘WeekdayAverage’, but also ‘IsGoodBeerGardenWeather’. It will be discussed how predictive analytics can help make informed decisions in a low-tech environment using the knowledge of domain experts.

3. AI Algorithms for Forecasting Container Traffic – Is More Complexity Always Better? (Sven F. Crone)

AI/ML researchers are preoccupied with the most advanced and complex algorihtms, notably deepnets and xgboost, but fail to deliver POC results. In this talk we will explore 30+ forecasting algorithms in AI, ML and statistics for a logistics case study of real-world container traffic forecasting at Hamburg harbour. We develop a landscape of algorithm accuracy versus complexity, runtime, and robustness which shows that simple algorithms designed with domain expert know-how improve performance.