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by: Emily Laurienti

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Emily Laurienti
USC

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These notes cover decision trees if you have any questions, feel free to email me at elaurien@usc.edu
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Operations Management
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Class Notes
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This 4 page Class Notes was uploaded by Emily Laurienti on Sunday October 16, 2016. The Class Notes belongs to BUAD 311 at University of Southern California taught by Prof. Hamid Nazer-Zadeh in Fall 2016. Since its upload, it has received 6 views. For similar materials see Operations Management in Business Administration at University of Southern California.

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Date Created: 10/16/16
10.13.16 Decision Trees  A tool to design strategy in uncertain environments  Squares = decisions  Circles = scenarios  Hotel Revenue Management Example  One room left for tonight  Normal rate = \$350  Someone comes in asking for the room for \$250  70% chance someone else arrives  30% chance nobody shows up  Should you accept?  Expected value of selling = 250 dollars  Expected value of waitng = 30% * 0 + 70% * 350 = 245  According to the math, you should accept the offer and sell now  HOWEVER, this trains customers to think that they can always do this, so most hotel chains will turn down any offer below asking price  Drug Development Example  Your drug has passed the first two phases of trial  90% probability for success of phase 3  Demand = 5 million  Profit = \$50 per person per year  Total profit = 250 milliion  Options  Contract now for 150 million  Wait until after phase three and contract for 180 million  When do we contract?  Contract now approved = 250 million – 150 million = 100 million profit  Contract now not approved = -150 million  Expected value = .9 * 100 million + .1 * -150 million = 75 million  Contract later approved = 250 million – 180 million = 70 million  Contract later not approved = 0 made  Expected value = .9 * 70 million + .1 * 0 = 63 millon  Therefore, it is more profitable to contract now. However, most execs would probably not make this decision  What is the maximum manufacturing cost such that it is beneficial for us to wait to contract?  We would contract later if our expected value was at least 75 million  75 million = (250 – p) *.9  P = 166.7 million is the highest cost we will pay in order to make it beneficial to wait to contract  Market Research Example  Two Decisions:  Invest 200 million to introduce the new sandwich nationally  60% chance high response  High response = 700 million revenue  40% chance low response  Low response = 150 million revenue  Invest 20 million to evaluate it in a regional test market  70% chance high response  Remain regional = 200 million revenue  Go national = invest 200 million  90% chance high response = 700 million revenue  10% chance low response = 150 million revenue  30% chance low response  Remain regional = 100 million revenue  Go national = invest 200 million dollars  5% chance high response = 700 million  95% chance low response = 150 million See Decision Tree Below  Find expected values  Go national first  Cost 200 million  High response profit = 700 – 200 = 500 million  Low response profit = 150 – 200 = -50 million  Expected value = 500 * .6 + -50 * .4 = 280 million  Go regional first  Low response  Remain regional = 100 million – 20 = 80 million  Go national  High response = 700 million – 200 – 20 = 480 million  Low response = 150 million – 200 – 20 = -70 million  Expected Value = -42.5 million  If we go regional and response is low, we will choose to remain regional  High response  Remain regional = 200 million – 20 = 180 million  Go national  High response = 700 – 20 – 200 = 480 million  Low response = 150 – 20 – 200 = -70 million  Expected value = .9 * 480 + .1 * -70 = 425  If we go regional and the response is high, we will go national  Total regional Expected value  .7 * 425 + .3 * 80 = 321.5  Regional first will make us 321.5 million, national first will make us an expected 280.  We should go regional first.

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