A Case Study on Automotive Battery System Design - Title Page - MIT

3.2: The Garage <strong>Case</strong>Professor Richard de Neufville at MIT has provided an example on how point forecasts willfrequently lead to non-optimal decisions, given that future forecasts are assumptions, not fact.The “Garage <strong>Case</strong>” is a study on the building of a parking garage structure at a new shoppingmall(de Neufville, Scholtes, & Wang, Real Options by Spreadsheet: Parking Garage <strong>Case</strong>Example, 2006). A parking garage is a long-term investment with a high initial CAPEX. Howmany levels should be built for the garage? If we estimate too low for future usage in order tolimit downside losses, the garage will be full all the time and additional potential revenue will belost. If we estimate too high in trying to capture upside, the garage will be mostly empty and wewill have spent additional CAPEX. Forecasting demand is difficult, as many exogenous factorscan impact predictions. In the garage case, demand could have a distribution of potentialoutcomes (i.e. adding uncertainty to our assumptions) showing the potential for large losseswhen demand does not materialize (e.g. when a competing mall is built in the future), while theupside is “capped” by the number of floors (limiting demand when retail is booming).Professor de Neufville has developed a third way to evaluate the problem, using flexibility tocreate an “option for future action” for the garage developer. Ultimately, the garage developerwould like to limit downside losses with the “option” to capture upside gains. For example, theparking garage might be built with stronger pylons and prefabricated connectors on the upperlevel, such that expansion in the future is possible. Here, the uncertainty is demand, the time fordeveloping the flexibility is in product design and manufacturing, and the time for exercising theoption is during consumer use.25