Decision trees are models which allow you to both visualize and quantify a range of possible outcomes when faced with complex choices. These models incorporate the timing and estimated probability of outcomes along branches on a tree to help you identify the most promising path forward.
Review the following:
Real Options and the Value of Information
Did you know that roughly 60 percent of new restaurant businesses fail within the first three years of operation (Abrams, 2004)?
Suppose you have a close friend who is employed in a high-paying position in the banking industry with tremendous potential for her professional and financial growth. However, your friend wants to leave this position and start a little restaurant. It is your job to help your friend make a sound decision. What do you do?
Often, the valuations on which decisions are based require the input of information neither easily deduced nor accurately available. There are so many options and so little time. The values of these options can be clouded in uncertainty. The likelihood of each outcome—both those that are dependent and those that are independent—is shrouded in a variety of likely scenarios.
Like your friend, you must assess the value of certain options, including those choices foregone. If your friend, the restaurateur, leaves her job, what is the income she has given up? What is the probability that her business will flourish for a year or two years? What is the likelihood today that she will be in business three years from today (you would immediately think 40 percent)? What if she creates a great restaurant that is widely acclaimed, but the market, well beyond her control, suddenly crashes? How might you have incorporated that information in your forecast of probabilities?
As you can see, decision points combine with scenarios, including events beyond the chooser’s control, to increase the complexity of choosing. Fortunately you have a tool, scenario analysis, which works in conjunction with decision trees where multiple outcomes and the likelihood of those outcomes can be evaluated in light of an uncertain future and the need for a choice today. As much as you need to rationalize your choices and incorporate information accurately and reasonably, you must also learn to forecast reasonably, and identify those biases that undermine your choices.
Now assume your friend, Jennifer the banker (formerly a bank teller), has asked for your advice as to whether she should quit her job and pursue her passion in order to become a restaurateur.
Assume the following facts as well as the above information:
As a banker, Jennifer makes $135,000 a year with up to a 25 percent bonus. Her maximum raise per year is 10 percent (raises are skewed to capture inflation). She has $250,000 in savings. Her expenses are $5000 a month after taxes.
Jennifer is eligible for a promotion in twelve months; the promotion comes with a 50 percent increase in pay and 25 percent bonus. She is competing with three other employees for the position. If Jennifer does not receive the promotion, in all probability she will be considered for promotion to the same position after another twelve months, be asked to stay in her current role for the foreseeable future, or be asked to leave the bank.
Jennifer enjoys her current job but she wants to manage her own business at some point in her career. She is an avid chef, having had some experience in college and afterwards. She is also personally inclined towards more entrepreneurial ventures. Jennifer gets enormous personal value out of the pursuit of her personal and professional goals.
Jennifer’s current employer focuses exclusively on small- and medium-sized business clients along with their families. If Jennifer left the bank, her former employer would be a ready and willing source of financing with a reasonable business plan in place.
Opening a restaurant will require a $200,000 cash investment for capital improvements and materials. The bank normally provides new restaurants with access to a $100,000 rolling line of credit at an 8 percent cost for the first $50,000 and a 12 percent cost for the second $50,000.
The restaurant will, in the case of modest success, lose $25,000 in the first twelve months; generate a 20 percent net profit in months 13–24, and 25 percent in months 25–36.
About this Assignment
The goal of this assignment is to thoroughly analyze Jennifer's situation through use of a decision tree which you will create. Decision trees can be drawn by hand or created in any number of software tools including Microsoft Word and Excel
Choose a method for creating a decision tree. Download and review the decision tree template example offered on this page which you will use, along with your chosen method for decision tree creation, to address the following:
Map out the various scenarios that Jennifer faces—for example, bankruptcy, breakeven, modest success, home run—and produce a scenario model.
Assign probabilities to the various nodes and use the tools to offer the best advice you can.
State well-reasoned decisions about the market and Jennifer’s future prospects in your models.
Work backwards from assumption five in the above list of facts and determine, in terms of sales in dollars, how large the restaurant needs to be to break even.
Write a 3–4-page paper in Word format describing your decision making process, conclusions, and recommendations for Jennifer. Apply APA standards to citation of sources. Use the following file naming convention: LastnameFirstInitial_M4_A2.doc.
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Assignment 2 case solution Jennifer Decision Tree
body preview (6 words)
xxxxxxxxxx x xxxx xxxxxxxx xxxxxxxx Decision Tree
file1.doc preview (1248 words)
Running Head: DECISION TREE 1 DECISION TREE � PAGE xx xxxxxxxxxxx �2�
xxxxxxxxxx xx xxxxxxx xx x xxxxx Award
Jennifer is a xxxxxx xxxx xxxxxxxxxx xxxxxxxxx for professional xxx xxxxxxxxx xxxxxx in xxx xxxxxxx xxxxxxx However, xxx has an xxxxxxxxxxxxxxx streak and xxxxx to open x xxxxxxxxxx xxxxxxx of her xxxx skills. It’s x crucial decision that requires thorough xxxxxxxx xx the xxxxxx xxxxxxxxx xx xxx xxxxxxxxxxx especially with xxx xxxxxxxx average of xx percent of new xxxxxxxxxxx closing xxxxxx xxxxxxx xxxxx years xx xxxxxxxxxx (Abrams, 2004). xx xxxxxxxx would have xx xxxxx xxx xxxxxxxxx xxx xxx xxxxxxxxx xxx xxx restaurant, this paper analyses both xxx xxxxxxx that xx to xxxxxxxx her xxx or to start xxx restaurant.
Decision Making xxxxxxxx
Decision tree is an effective xxxx for xxxxxxxx xxxxxx especially for those projects xx which xxxx is sequential and not discrete xxxüxxx & Küxxx 2009). xxxx xxxx also
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file2.xlsx preview (3240 words)
|xxxx xxxxxxx xxx the Value xx Information|
|xxx you know xxxx xxxxxxx xx xxxxxxx xx xxx restaurant xxxxxxxxxx fail xxxxxx xxx first three years of operation (Abrams, xxxxxx|
|xxxxxxx you xxxx x xxxxx xxxxxx xxx is xxxxxxxx xx x xxxx xxxxxx position in xxx xxxxxxx industry xxxx xxxxxxxxxx potential xxx both professional xxx xxxxxxxxx xxxxxxx Nevertheless, your xxxxxx xxxxx to xxxxx and xxxxx x xxxxxx restaurant. xx xx your xxx to xxxx xxxx xxxxxx xxxx x xxxxx decision. What do you xxx|
|xxxxx the xxxxxxxxxx on which decisions xxx based require xxx input xx xxxxxxxxxxx xxxxxxx xxxxxx deduced xxx accurately xxxxxxxxxx xxxxx xxx so many options and xx xxxxxx time. The values xx xxxxx options can be xxxxxxx in xxxxxxxxxxxx The likelihood xx xxxx outcome—xxxx those xxxx xxx xxxxxxxxx xxx xxxxx xxxx xxx xxxxxxxxxxx—is shrouded xx a xxxxxxx xx likely scenarios.|
|Like your friend, you must xxxxxx the xxxxx of xxxxxxx options, including those choices xxxxxxxxx If your xxxxxxx xxx|
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