estimates results in the eponymous name for the option, the DM Range Option. The DM Range Option method is similar to the fuzzy method for real options. The following example (Fig. 7) uses a range of future estimated operating profits of a (pessimistic), b (optimistic) and m (mode or most-likely). For discount a, b and m by The classic DM Method presumes that the strike price is represented by a random variable (distribution ) with the option solution derived by simulation. Alternatively, without the burden of performing a simulation, applying the average or mean scalar value of the launch cost distribution (strike price) results in a conservative estimate of DM Range Option value. If the launch cost is predetermined as a scalar value, then the DM Range Option value calculation is exact. The expected value of the truncated triangular distribution (mean of the right tail), is The probability of the project being in the money and launched is the proportional area of the truncated distribution relative to the complete triangular distribution. This partial expectation is computed by the cumulative distribution function (CDF) given the probability distribution will be found at a value greater than or equal to X for a right tail: The DM Range Option value, or project investment, is: Use of a DM Range Option facilitates the application of real option valuation to future project investments. The DM Range Option provides an estimate of valuation that differs marginally with that of the DM Option algebraic lognormal distribution form. However, the projected future value outcome, S, of a project is rarely based on a lognormal distribution derived from historical asset returns, as is a financial option. Rather, the future value outcome, S, (as well as the strike price, X, and the standard deviation, SD), is more than likely a three-point estimation based on engineering and marketing parameters. Therefore, the ease of application of the DM Range Option is often justified by its expediency and is sufficient to estimate the conditional value of a future project. Demand Curve Integration Many early-stage projects find that the dominant unknown values are the first-order range estimates of the major components of operating profits: revenue and manufacturing cost of goods sold (COGS). In turn the uncertainty about revenue is driven by guesstimates of either market demand price or size. Market price and size can be estimated independently, though coupling them together in a market