This course is designed to familiarize you with fundamental quantitative tools for business decision-making and planning, and to help you think quantitatively about decision and planning problems. It is not possible to cover every relevant quantitative technique in the time available, so we will focus on a relatively small number of generic, commonly-used methods that form the foundations of many others. All the techniques we will cover are based on:
The first part of the course will be about optimization modeling, in situations where the problem data are known without significant uncertainty. Even relatively simple problems of this kind are hard for people to solve by intuition or "back-of-the-envelope" calculations, but many yield easily to mathematical and computer modeling and analysis. Within this topic, we will briefly explore a variety of subtopics, and try to impart an understanding of the situations in which they arise:
As we cover these topics, we may also review, as necessary, some relevant microeconomic concepts, including sunk costs, fixed costs, allocated costs, and net present value. In paraticular, we will spend some time discussing allocated costs and how they can adversely affect decision-making.
In the second part of the course, we consider decision making under uncertainty. Planning systematically under uncertainty requires the mathematics of uncertainty, called probability theory, which is also used in statistics. We consider two techniques for planning under uncertainty: first, when the situation is simple enough (or has been sufficiently simplified by your model) that it is possible to consider every possible contingency, one may apply decision trees, also called decision analysis. After decision trees, we will cover a very general, powerful technique: probabilistic Monte Carlo simulation modeling, which samples all possible outcome rather than individually analyzing every one of them.
Finally, we will work throughout the course on enlarging and sharpening your spreadsheet modeling skills.
Your grade will be based on the following, in order of importance
Because quantitative material must be learned by solving problems oneself and the class format leaves very limited time for homework assignments, portions of the class meeting time will be used for in-class problem solving, either individually or in small groups. During these periods, I will remain available in the classroom to assist students. I will not formally grade such in-class work, but I might consider the in-class problem sessions to be part of "class participation". Students may collaborate during in-class problem-solving sessions, but not on exams or homework. When collaborating on in-class problems, it is best to works in groups of at most three people, with each group member solving the problem in parallel on his or her own computer.
I propose to use the following grading scheme, subject to change:
For the classes and homework assignments, you should have a Windows or Mac laptop computer with Microsoft Excel. For Windows, any version of Excel 2003 or later will work (that is, Excel 2003, 2007, 2010, 2013, or 2016). For Mac computers, you cannot use Excel 2008. The best version of Mac Excel for the course is 2011. It should also be possible to use the 2016 Mac version, although you may encounter slow performance in the simulation exercises.
In the first half of the course, we will use the Solver add-in. Microsoft supplies a version of this add-in with Excel, although sometimes it is not automatically installed. Thus, it may already be available by enabling it in Excel's Add-ins dialog box, or installable from your Microsoft Office installation disk(s). If not, the textbook contains a CD-ROM from which you may install a "risk solver platform" version of Solver.
In the decision tree section of the course, you may use the TreePlan add-in or the similar tool in the textbook's "risk solver platform" software, although you will should find the problems simple enough to work through by hand with the aid of a calculator, and you will have to solve them that way on the second exam.
In the simulation portion of the course, I plan to use the free YASAI add-in (which I designed myself, and programmed portions of). The book includes similar capability as part of its "risk solver platform". This software resembles YASAI, but YASAI is much simpler to use, in my experience causes far fewer software problems, and is now compatible with Macintosh Excel (except 2008) and as well as all versions of Windows Excel since 2003.
On the following page is my detailed plan for the course, which is subject to change.