Mine-to-mill (M2M) programs aim to maximise comminution circuit performance by integrating blasting and milling operations. However, the effectiveness of these programs depends on a representative ore characterisation strategy to accurately quantify ore competency and its impact on the plant. In this context, the Geopyörä device offers a fast characterisation tool that requires only a small sample mass to perform testing. The device is capable of testing both blast chips and drill core samples while delivering near real-time results through automated data processing within the Geopyörä online system (Geodata platform). This paper presents the methodology and workflow developed to integrate the Geopyörä test into a Mine-to-Mill framework at a large copper mine. The proposed approach utilises blast-derived and core-derived samples to generate hardness parameters that were incorporated into comminution circuit throughput forecasting models. Initial validation results are presented through comparisons between predicted throughput and actual plant performance data. In parallel, a geometallurgical modelling workflow was developed through submitting some of the samples tested on the Geopyörä to XRD analysis to obtain their geochemical data. Machine-learning techniques were applied to build the forecasting models for the Drop Weight Index (DWi) and Bond Ball Mill Work Index (BWi), allowing comminution properties to be forecasted for future ore blocks. The findings demonstrate the potential of the Geopyörä device as a practical and reliable tool for both short-term Mine-to-Mill optimisation and long-term geometallurgical modelling in large-scale operations.