ESD 71 Uncertainty Exercise Page 1 of 3 ESD 71 -- Engineering Systems Analysis for Design UNCERTAINTY ASSIGNMENT Last updated: September 2009 Learning objective: The overall purpose of this exercise is to introduce you to the fundamental concept underlying this course. This is the fact that the future is uncertain. Specifically, designers cannot expect to know the future loads and requirements for a system accurately. The development and use of a system over time almost inescapably takes place in the context of changing technical, economic, political, industrial and regulatory conditions. These factors will generally disrupt initial forecasts, and make the actual outcomes be quite different from the best original estimates. In short, the general rule is that the “forecast is always wrong”. Part 1 provides you with a current example of how changing technical, economic, political, industrial, and regulatory conditions have affected forecasts. It is one of many that we could have chosen; the recent and past experience is full of parallel examples. In this case, we give you the ways current loads on a system turned out to be different from forecasts, and invite you to investigate the sources of the disruptions to the forecasts, according to a general checklist we provide. We would like you to • appreciate the kinds of significant contextual changes that disrupt forecasts, and • take-away a checklist of issues that you could use to anticipate the possible uncertainties that may affect a design issue you may be working on. Part 2 gives you the opportunity to apply what you have worked on in Part 1, to reinforce and generalize that learning. PART 1 The purpose of Part I is for you to understand some of the sources of uncertainty in engineering projects and the ways in which they can impact forecasts, by considering a specific example. In the aviation industry, the development of infrastructure and equipment can take a long time. As a result, large-scale investment decisions must be made in anticipation of transportation behavior ten or more years in the future. As this example demonstrates, future trends (in this case in transportation) are highly dependant on unpredictable or uncertain events. Between 1995 (when the nearly-complete US $4B1 upgrade project for Boston-Logan International Airport was initiated) and 2006, air traffic into and out of the Boston metropolitan area changed significantly. This means that airport designs based on earlier forecasts may be inappropriate for current conditions. For example, the plan to construct a massive new passenger terminal for Delta Airlines, did not anticipate that this airline would go bankrupt just before the opening of the terminal (and may not be able to pay for it!). Figure 1 indicates the total traffic, in passengers and aircraft, which departed from Boston Logan airport in 1999 and the actual and US Federal Aviation Administration (FAA) forecast for annual passenger and aircraft traffic in Boston in 20042. 1 http://www.massport.com/logan/about_histo.html, (1 Sept. 2006) 2 http://www.nerasp.com/Tech_Paper_2.pdf, (1 Sept. 2006)ESD 71 Uncertainty Exercise Page 2 of 3 Figure 1: Actual and forecast annual total passengers and flights, Boston-Logan International Airport, 1999 and 2004 [Source: Massport, 01 Sept. 2006; FAA 01 Sept. 2006 / Logan ESPR] Task: Use online resources (such as Google, Wikipedia, and media available through the libraries) to identify possible sources for the differences between forecasts and actual results. There is no secret “right answer” for you to discover. The purpose of the exercise to is for you to demonstrate an understanding that forecasts are subject to many unforeseen risks. Include a citation or online source for each potential cause. Your answer on 1page should be sufficient, but in any case should not be more than 2 pages in Word. The following general categories of uncertainty that affect the accuracy of forecasts may help your thinking: • Technical change: The introduction of new technology may change conditions significantly. For example, regional jets (RJs) became important elements of the airline fleets. As smaller aircraft need larger separations during the landing approach when sequenced behind larger aircraft, their presence at an airport can lower its capacity, measured both in number of operations and of passengers. • Economic/Financial: Periods of boom and recession affect demand, as do absolute or relative prices (as for jet fuel, the value of the Euro, or the comparative fares by rail or on airlines through nearby cities). • Regulatory: Regulatory changes can change what can (or cannot) be done. Thus, new security regulations since 2001 made air travel less attractive compared to rail, car, or video conferencing (where these are substitutes). They also changed the space needs in passenger buildings. • Industrial: The composition of the industry can change the availability and supply of service from a system. For example, the sale of TWA to American Airlines drastically reduced service to St Louis, as American moved flights to its hubs in Dallas and Chicago. Similarly, the expansion a of low cost carrier (Southwest) into cities near Boston (Providence and Manchester) increased the attractiveness of these airports to travelers. • Political: Changes in international relations may alter the system. Thus, the implementation of the North American Free Trade Alliance (NAFTA) means that US and Canadian airlines can change their route patterns. • Other: Of course, this is not an exhaustive list. You can think of other uncertainties that impact engineering projects. 1999Base Year Forecast Actual13,531,330 14,500,000 13,074,190Domestic Jet 10,325,619 9,681,745Regional Carriers 1,059,355 1,291,626International 2,146,357 2,100,819494,816 529,129 409,066Mainline Jet 255,953 265,058 236,758Regional Carriers 189,680 222,949 128,972All-Cargo 11,267 13,180 12,100General Aviation 37,916 27,942 31,236Passenger EmplanementsAircraft Operations excl. Mil.2004ESD 71 Uncertainty Exercise Page 3 of 3 PART 2: Uncertainty in Agricultural Products Background: The World Bank and other institutions often finance large-scale projects – such as flood control and irrigation projects -- to develop agriculture. They justify these loans based on the prospective value of the commodities that will be produced. To