Scrap is the most important secondary raw material in the transformation to low carbon dioxide (CO<inf>2</inf>) steel. However, the suitable use of different scrap types for producing high quality steels with the right chemical composition is non-trivial. It requires process control and detailed knowledge of all input materials used. SHapley Additive exPlanations (SHAP), a game-theoretic approach, is often used to interpret machine learning models through visualizations and feature attributions. In this paper, we present a novel application of SHAP values. This enables more precise control of material composition in steel production without the need for additional sensors. This makes it extremely practical for real steel production environments and enables better control of the materials used in the steel production process. As a basis for this approach, various machine learning models were trained and the respective SHAP values computed. To validate the approach, the results were compared with the values from the steel plant. Comparing the calculated values with the historical estimates, the results agree for most input materials and target elements. The key innovation lies in using SHAP values not only for model interpretability, but also as a quantitative tool to estimate the chemical content of input materials (e.g., steel scrap) based on process data. The framework enables chemical composition estimation, relying solely on routinely collected process data. This is a novel application of SHAP and allows the back-calculation of predicted values and can be used in a wide range of applications in industry and academia.