Schnitzel-Prediction: Designing Human-AI Collaboration For Cafeteria Demand Forecasting

Cappel, Justus; Strohmann, Timo; Burger, Mara; Voss, Marleen; vom Brocke, Jan

Abstract

Cafeteria demand planning requires both algorithmic pattern recognition and human expertise, yet current systems treat these separately, which generates significant food waste. This paper reports on a 9-month action design research (ADR) project at a German financial services firm. Using a practice-driven abductive approach, we developed a collaborative forecasting system that leverages semantic processing using large language models (LLMs) to solve the “cold-start” problem for novel menu items while preserving human agency via override mechanisms. Our evaluation combines algorithmic benchmarking, reducing forecast errors by 30% over naive baselines, with two think-aloud sessions showing that human judgment remains critical for high-uncertainty events. We distill our findings into a meta-design and four design principles (DPs), grounded in kernel theories, for systems where human contextual intelligence and algorithmic recognition must coexist. We contribute to the discourse on human-AI collaboration and sustainable IS by providing a rigorous blueprint for designing synergistic, trustworthy, and diagnostic operational planning tools.