Chemical assay is widely employed in the minerals industry with mineral resource estimation and as a valid, though indirect input to factors to drive key decision points across exploration, resource development, mine planning, metallurgical functions and through project study phases. However, a quantitative assessment of mineral composition provides a robust understanding of mineral system configuration that enables a direct correlation to recoverable metal assessments and project optimisation assumptions. Since January 2022, automated Scanning Electron Microscopy (SEM) based mineralogical data has been routinely acquired to support resource characterisation across three strategic projects and operational mines within BHP's Nickel West (NiW) portfolio. The samples were analysed with the NiW in-house TESCaN TIMa (TESCaN Integrated Mineral analyzer) instrument, validated and reported into a geological database. The database captured SEM generated assay, mineral abundance, elemental deportment, size distribution, liberation and facilitates a direct connection to 3D modelling software. Mineralogical domaining and numeric block model estimation via traditional linear methods was completed using 3D modelling software packages alongside routine resource estimation projects. Key output variables included nickel deportment across mineral phases and mineralogical flags. The case studies presented in this paper outline key observations from the output numeric block estimates and highlight contrasting nickel-sulfide mineral abundance characteristics that are challenging to identify through assay-based proxies. The numeric estimation of mineralogical variables brings to surface the nuance of nickel mineral systems that can have a material impact to key assumptions and decisions from the initial exploration phase through to mine planning and the operational success of a project. The integration of mineralogical data into the resource and grade control model appropriately characterises the mineral system, facilitating a spatial and direct diagnostic link through to metallurgical analysis, project planning, optimisation opportunities, risks, mineral resource classification and operational decisions.