Large-scale co-located industrial clusters often require a continuous energy supply to support critical processes in hard-to-abate sectors, such as cement plants. Any loss of power may lead to recovery periods ranging from a few hours to multiple days before restoring optimal operating levels, resulting in substantial operations and economic setbacks. Although decades of development have produced highly reliable interconnected power systems, industries often deploy on-site fossil-fuel-based power sources alongside the central power grid to reduce vulnerability. Fossil fuel-based power sources increase their carbon footprint and operating costs while reducing efficiency. Transitioning to renewable energy sources (RESs), such as solar and wind power, is one of the most effective strategies for decarbonization. However, adequately sizing RESs and energy storage systems (ESSs) to meet demands during central grid outages, while accounting for techno-economic, operational, and weather-related variability, remains a significant challenge. Several commercial and open-source optimization tools have been developed and are extensively used in the industry to optimally size, plan, and manage RESs and ESSs, accounting for system integration, technical operations, weather conditions, capital, operational and maintenance expenditures, energy markets, and asset degradation. In this paper, an optimisation framework is designed for the optimal sizing, planning, and operational management of multi-energy systems supplying large-scale industrial plants, and its performance is compared with that of existing optimisation tools. The model integrates solar and wind generation, battery energy storage systems (BESSs) and the main grid. To evaluate its performance, the framework was applied to a reference industrial plant utilizing a 15-minute resolution annual power demand profile. The key indicators, such as optimal RES and BESS capacities, power curtailment, grid power imports, and total system costs, were compared with different optimization tools based on different optimization goals and constraints. The analysis is performed using identical power demand profiles, system configurations, and solver settings across all platforms, to the extent possible, to ensure a fair comparison. It is observed that some optimization tools have predefined algorithms, constraints, and functions that optimize assets based on preferred factors that they consider necessary. These preferred factors yield divergent results and sometimes even lead to impractical system configurations. Finally, it is concluded that it is necessary to understand the underlying principles of these optimization tools, as several of them lack transparency in their prioritization mechanisms that can lead to technically infeasible or economically suboptimal investment decisions in industrial energy infrastructure.