demand relationship is a better approach. COGS, the total of cost of product quantity to be sold, is the final component and trends according to an experience or learning curve cost relationship linked to market size. The interplay of these three market elements within a DM Real Options simulation, even with early-stage ranges, can reduce uncertainty for project planning by yielding reasonably narrowed target estimates for product pricing and production size that maximizes potential operating profits and improves option value. A market price demand curve graphs the relationship of price to size, or quantity demanded. The law of demand states there is an inverse relationship between price and quantity demanded, or simply as the price decreases product quantity demanded will increase. A second curve, the manufacturing cost graph, models the learning curve effect illustrating the relationship between the quantity of goods produced and the efficiency gains of that production. Fig. 9. Mathematically, the learning curve takes the form of a power function. A demand curve can be realistically modeled using an inverse lognormal distribution which convolves the market price distribution estimate with the market size range. A demand curve deftly models highly differentiated markets which through pricing distinguish selective product or service characteristics such as quality or grade, functional features, and availability, along with quantity sold. Examples are automobiles, shoes, smart phones, and computers. Airfare markets are highly differentiated where demand pricing and quantity sold are dependent on seasonality, day of week, time of day, routing, sale promotions, and seating or fare class. The airfare demand distribution pattern is well represented with an inverse lognormal distribution as shown in Fig. 10. Curves for all the above components, market price, size, and COGS, can be simulated with variability to yield an optimal operating profit input for the real option calculation (Fig. 11). For example, the simulation results represented in Fig. 12. indicate ranges for price and unit quantity that potentially will maximize profitability. Extracted from these first-order range estimates, a selection of the peak (frequency) values identifies a significantly narrowed spread of promising estimates. Knowing these optimal value spreads substantially reduces uncertainty and provides a better, more targeted set of parameters from which to confidently base innovation development plans and option value. Comparison to other methods The fuzzy pay-off method for real option valuation, created in 2009, provides another accessible approach to real option valuation. Though each use differing mathematical methods