FLEXIBILITY IN DESIGN - Title Page - MIT

de Neufville + Scholtes D R A F T September 30, 2009of the 1000s of simulated possibilities. This is the value we can expect to receive from the project,on average. It is the factor that directly compares with the NPV estimated using a singleprojection, a forecast of the average conditions. In line with the Flaw of Averages, and as Table3.4 shows, we typically see that the ENPV differs substantially from the naïve NPV calculatedaround a single forecast.[Table 3.4 and Figure 3.4 about here]Notice in Table 3.4 that the ENPV values for the parking garage are not only different butalso all lower than the naïve estimates based the single projected demand. Why is this? Theexplanation in this case is that higher demands for parking spaces do not increase valuesufficiently to compensate for the losses in revenues corresponding to equivalent lower demands.When the parking garage is full, it cannot accommodate more cars, and cannot profit from greaterdemand. This capacity limitation thus systematically affects the value of the project.Monte Carlo simulation helps us understand what is going on. The process generates theinformation needed to show us in detail what happens to the system. In addition to providing uswith summary statistics, such as the ENPV, it generates graphs of the uncertain systemperformance. These illustrate what is happening. Figure 3.4 for example shows the histogram forthe distribution of the net present value for a design of 6 levels, obtained from 10,000 sampleddemand scenarios. It documents the fact that while the project generates a distribution of resultswith a downside tail of significant losses if demand is low, it does not deliver a counter-balancingupside tail of higher gains when demand is high. As Figure 3.5 shows, the wide range of possiblehigh demands all lead to the maximum value the garage can deliver at full utilization. Thedistribution of high demands compacts into a narrow range of highest values, thus giving morethan 30% probability of being in the highest range, as the far right of the histogram shows. 2It is important to recognize that this skewed distribution of performance can result frombalanced distributions of inputs. This is exactly what happens in this example. The uncertaintiesequally distributed around the single forecast have led to the skewed results shown in Figure 3.5,and indicated in Box 3.2. What happens is that the working of the system transforms the data thatcomes in, often to something that is quite different in character. In this particular case the limitedsize of the garage, the capacity constraint, distorts the results downwards. In other cases, thedistortion can be upwards. In any event, a distortion generally occurs and this is the mechanismthat leads to the flaw of averages.[Figure 3.5, Box 3.2 and Table 3.4 about here]Notice now further that the design that maximizes value under realistic conditions is notthe same as the one that appears best using a simple forecast. Indeed, the ENPV of the 6-storygarage, averaged over the 10,000 demand scenario, is negative. The 5-story garage has anaverage NPV of 0.3M and outperforms the 6-story design. The characteristics of the system thatchange the distribution of the uncertainties can thereby also change the relative value of thePart 1: Chapters 1 to 3 Page 49 of 69