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 half 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:
In the second half of the course, we consider decision making under uncertainty. Planning systematically under uncertainty requires the mathematics of uncertainty, called probability theory, 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 explore every possible contingency, one may apply decision trees, also called decision analysis. After decision trees, we will cover a very general, powerful technique: probabilistic simulation modeling.
As we cover these topics, we will also review some relevant microeconomic concepts, including sunk costs, fixed costs, allocated costs, and net present value. 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 at least partially 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 in the classroom to assist students. I will not formally grade such in-class work, but might consider the in-class problem sessions as part of "class participation". Students may collaborate during in-class problem-solving sessions, but not on exams or homework.
I anticipate a grading weight of 15% for each homework assignment, and 35% for each exam, subject to change. If I decide to assign some weight to class participation, the weight assigned to some or all of the homework assignments and exams will be reduced somewhat to compensate.
You must have a laptop computer running Microsoft Excel. Ideally, you should have Microsoft Excel 2000, XP, or 2003; Microsoft Excel 2007 is not recommended due to add-in compatibility issues, but we will attempt to work with what is available. Versions of Excel older than 2000 are also not recommended.
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 the Tools->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 "premium" version of Solver.
In the decision tree section of the course, you may use the TreePlan add-in, although you may find the problems simple enough to work through by hand with the aid of a calculator.
In the simulation portion of the course, I plan to use the YASAI add-in (which I designed myself, and programmed portions of). If we encounter compatibility or installation problems, we will also or instead use the Crystal Ball add-in, which is also included on the textbook software disk. The two add-ins are very similar, but YASAI is much simpler.
Below is my detailed plan for this course, which is subject to change.