Increased competition and low oil prices coupled with promising prospects for new oil and gas (O&G) reserves in the Arctic region has led to expansion of activities into the offshore Arctic. This brings along new challenges for the offshore logistics that need to be addressed. These challenges impose more stringent requirements for the logistics system setup, especially on the design and operation of vessels. Copying the logistics system and vessels designed for the North Sea operations is not a sustainable way forward. The few existing studies related to Arctic logistics mainly focus on ship technology solutions for cold and ice infested areas or solutions to the area-specific operational challenges for shipping companies. However, there is a need to understand how these solutions are connected and impact each other in a larger offshore supply logistics system, and thus address the challenges of Arctic logistics as a whole. A methodology for quick evaluation of the feasibility and costs of the logistics system in the early stages of offshore supply planning was developed and presented in previous research . It allows for testing the effects of using alternative ship designs and the overall supply fleet composition on system’s cost and performance while satisfying prospective campaign requirements. Safety standards and requirements for emergency preparedness and environmental performance are taken into account while cost effectiveness of the logistics system as a whole is the main quantifiable measure. Building on the new methodology a simulation tool for remote offshore operations has been developed and is presented in current work. Simulation models allow us to consider the dynamic and uncertain nature of variables, such as variation in weekly transport demand, weather impact on sailing times and fuel consumption, and schedule deviations. The evaluation of the performance of a logistic system is done by simulating the logistic operation over a large number of scenarios. Input parameters are weather data generated from historical observations and probability distributions for transport demand. Output from the tool are key performance indicators for: system costs, logistic robustness and emergency preparedness. The tool consists of three main components: simulation of a regular supply logistics operation, simulation of emergency situations, and visualization of the simulated operations. The proposed methodology and tool are tested on real-life cases for offshore supply planning of drilling campaigns in remote areas for one of the major international O&G operators.