1.INTRODUCTIONIn this paper we are interested in exploring how typical simulation based investment analysis can be enhanced to offer better managerial decision-support in terms of providing better actionable information about threshold values for identified important-to-the-investment variables that affect investment profitability.Our focus is on analysis that is performed before the investment decision. The decision support before investment is "actionable", because it is before the investment that decision managers are often in a position that allows them to still plan and steer investments towards the most profitable configurations, and by their actions ensure that critical to profitability issues are properly accounted for.What we propose is a new approach that we call "simulation decomposition". The method is based on setting artificial (expert chosen) thresholds to divide the possible value distributions of the most important uncertain variables of an analyzed investment. Typically in Monte Carlo simulation these distributions are from where the simulator draws random variable values. After having "decomposed" each variable's uncertainty, or "range", into sub-ranges, the combinations of these sub-ranges are listed. When the simulation is run, the results are registered separately for each combination, in addition to the overall simulation results. This allows for constructing a separate distribution of outcomes for each combination that is a sub-distribution of the overall simulation result. By studying the sub-distributions managers can infer important information about the profitability-critical threshold values for each variable that will help them plan their actions with regards to managing the investment better.Modern investment decision-making is most often an exercise that involves comparing the value of an upfront investment cost and a stream of uncertain future cash-flows that is expected to result from the prospective investment. In practice, the methods "used for the job" are the "classical" capital budgeting methods, such as the net present value (NPV) method, the pay-back method, and the internal rate of return method (IRR) that are often used together with complementary sensitivity, scenario, and simulation analysis methods (Block, 2007; Graham and Harvey, 2001; Ryan and Ryan, 2002). Real option analysis (Amram and Kulatilaka, 1998; Trigeorgis, 1995) is among the latest additions into the investment analysis toolkit of managers and has been gaining a foothold in academic research, as well as, a following in the industry. The benefit of real option analysis over the classical methods is that it is able to capture the value of managerial flexibility that is to be found within investments, and when investments are considered as a whole. Often a mixture of different investment analysis techniques is used simultaneously in hopes of gaining a better holistic picture of the situation surrounding the investment and in order to comprehensively treat the risks involved.Simulation and more specifically Monte Carlo simulation (commonly attributed to Stanislaw Ulam), is a technique that has been used in asset valuation since the late 1970's, e.g., (Boyle, 1977), and in investment analysis of real investments for more than two decades. In connection with the classical profitability analysis methods simulation has been used, e.g., in enhancing scenario analysis for complementing the investment analysis (Sheel, 1995). Simulation has also been used together with dynamic system models in investment analysis and profitability evaluation of, e.g., mining and oil investments (Johnson et al., 2006; O'Regan and Moles, 2001 ; O'Regan and Moles, 2006; Sontamino and Drebenstedt, 2014; Tan, Anderson et al., 2010), and to offer a better understanding of how uncertain variables affect profitability of energy investments (Bastian-Pinto, et al., 2010; Boomsma et al., 2012; Kozlova et al., 2015; Monjas-Barroso and Balibrea-Iniesta, 2013; Reuter et al. …