i nomr al 1Jnnr ma l niform fxponential eom etu l t l W ibull Be ta lly pe r

I nomr al 1jnnr ma l niform fxponential eom etu l t l

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!!_inomral ~ ] ·•1Jnnrmal !,!niform fxponential §.eometu~ l:t, l...~ ~ W,-.ibull Beta llyperqeomelric Cu~lom l&....._ ~ l~~~lli~~ lain_ IOK1 tancel More Ell.. l Ir Help- , normal distribution for the sales growth estimate from the gallery of distributions provided by Crystal Ball and shown at the bottom of the fig-ure. Then, using the spreadsheet model in Table 3.5, I asked Crystal Ball to display the results of 500 trials as a frequency chart. In less than a minute, I had the result shown. I could have allowed virtually all of the as-sumptions in the spreadsheet to vary, and to vary in correlation with one
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I l 100 Part Two Planning F11t11re Fi11a11cial Pe1frmna11ce another, but this is enough to provide a taste of how easy simulations have become. T he principal advantage of simulation relative to_ s~nsitivity _analysis and scenario analysis is that it allows all of the uncertain 1?put v~nables to change at once. The principal disadvantage, in my experience, is ?1at the results are often hard to interpret. One reason is that fe~ _e~ecuaves are used to thinking about future events in terms of probab1liaes. The fre-quency chart in Figure 3.1 indicates there is a 2.00 percent chance that R&E's external funding needs will exceed $1.844 million. Is a 2.00 percent chance so remote that R&E can safely raise Jess than $1.844 million, or might the prudent course be to raise even more just in case? How big a chance should the company be willing to take that it will be unable to meet its external funding requirement: 10 percent, 2 percent, or is .02 per-cent the right number? The answer isn't obvious. A second difficulty with simulation in practice recalls President Eisenhower's dictum "It's not the plans but the planning that matters." With simulation much of the "plan-ning" goes on inside the computer, and managers too often see only the results. Consequently, they may not gain the depth of insight into the company and its future prospects that they would if they used simpler techniques. . The complete Crystal Ball program is available on a one-week trial ba-s'.s at ~.crystalball.com. For practice using the program to build a s1mulanon model, see problem 12 at the end of this chapter. Cash Flow Forecasts A cash flow forecast is simply a listing of all anticipate? sources o_f cash to and uses of cash by the company over the forecast penod. The d1ffere~ce between forecasted sources and forecasted uses is the external financmg required. Table 3.6 shows a 2003 cash flow forecast for R&E Supplies. The assumptions underlying the forecast are the same as those used to construct R&E's initial pro forma statements in Table 3 .3. Cash flow forecasts are straightforward, easily understood, and com-monly used. Their principal weakness compared to pro forma statements is that they are less informative. R&E's pro forrna statements not only in-dicate the size of the external funding required but also provide informa-tion that is useful for evaluating the company's ability to raise this amount of money. Thus, a loan officer can assess the company's future financial position by analyzing the pro forma statements. Because
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