Mining companies want to achieve their Environmental, Social, and Governance (ESG) goals. Adopting electric trucks for haulage operations can help them to significantly reduce carbon emissions. But there are considerable challenges in the adoption of this technology, like constraints with respect to vehicle acquisition and inadequate trial data. Traditional asset acquisition methods are proving inadequate for the unique demands of fleet electrification, leading to delays that jeopardise the timely achievement of ESG goals. This paper presents a dual-layer approach that combines advanced simulation using CATIA DYMOLA with operational analysis to accelerate fleet electrification in the mining sector. Initially, CATIA DYMOLA is employed to develop and contrast various electrification options, including Hybrid Electric Vehicles (HEVs) and Battery Electric Vehicles (BEVs). The simulation outputs include critical data such as range estimations, battery state-of-charge (SOC) across different operational states, optimal charging point locations, and studying the total cost of ownership (TCO) and emissions savings. Following the simulation phase, we transition to the operational layer, where the integration of electric, diesel, and hybrid fleets is analysed in real-world scenarios. How introduction of electric trucks affects operational metrics like total tonnage transported or congestion at charging points is analysed. Several scenarios are explored, such as increasing the number of electric trucks or adjusting the charging infrastructure, to assess their effects on operational efficiency and ESG compliance. Also, the paper explores various operational scenarios as modifying shift schedules, charging schedules, and an increasing number of charging stations, to assess their effects on operational efficiency. This dual-layer approach helps to perform virtual trials rapidly so that mining companies can prioritise investments in electric fleet.