Course Detail (Course Description By Faculty)

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PLEASE REFER TO THE COURSE SYLLABUS FOR MORE DETAILS; if you have any questions about this course, please do not hesitate to email me at philipp.afeche@chicagobooth.edu

OVERVIEW AND LEARNING OBJECTIVES

The modern business world presents managers with important and challenging decision problems that involve complex situations, uncertainty and many possible options. This course focuses on managerial decision problems that are amenable to quantitative analysis.

This course teaches how to systematically structure, model, analyze and ultimately solve such managerial decision problems using Excel spreadsheets. We consider problems from business areas including operations, marketing, finance, and strategy. For each business problem we discuss how to build an appropriate mathematical model to structure and formulate the problem; how to analyze and solve the model; and how to analyze and discuss the managerial interpretation of the solution.

This course takes a practical approach that focuses on modeling, analysis and interpretation, rather than on developing the underlying theory or algorithms. We develop and use Excel spreadsheets as a modeling platform, because spreadsheets have become an essential medium of business analysis. Using Excel commands, functions, and add-ins, you learn how to select, apply and interpret the solution of the appropriate analytical tool, including optimization, simulation, and decision trees. This practical approach has proven to be accessible and effective for managers, who find spreadsheets to be an intuitive and user-friendly platform for business analysis.

In sum, you will learn how to:

• Structure a decision problem: identifying the objective, decision alternatives, input parameters, and sources of uncertainty.
• Build a mathematical model to formalize the decision problem:
  
  • Optimization models (linear, nonlinear) - for resource allocation (how to utilize available resources optimally)
  • Simulation models - for risk analysis and incorporating uncertainty in problem parameters
  • Decision tree models - for multiperiod sequential decision making.
• Analyze the model solution: Is the decision fairly robust, or very sensitive to the input parameters of the model ? What is the managerial interpretation of the model solution?
• Use Microsoft Excel as a platform for model building, solution, and analysis: Spreadsheets are a useful medium for learning the decision modeling concepts mentioned above without the large time investment of learning a general purpose programming language (like Python). In addition to standard Excel tools such as Goal Seek and Data Table, we will use add-ins such as Solver, SolverTable, Precision Tree, and @RISK. This is NOT a course for learning Excel, the students will be expected to go through a introductory tutorial of Excel before the first class. 

The classes will comprise mini-lectures to introduce the modeling and analytical concepts, followed by an interactive discussion of a few (3-5) representative problems. We will develop the models for these problems and implement, solve and analyze them in Excel.

There are NO strict prerequisites. However, the following preparation/background is helpful.

1. Basic Statistics: Any previous or concurrent exposure to statistics at the level of 41000 will be helpful. Basic statistical concepts such as random variables, probability distributions, variance, covariance will be used. We will briefly review the important concepts when necessary, but will not spend a lot of time trying to teach such concepts.
2. Basic Finance/Accounting: Although the example models discussed in this class cross many functions of business, very little prior background in those areas is sufficient. Basic financial concepts such as net present value, discounted cash flow analysis, stocks and options, and so on will often be used. We will briefly review the important concepts when necessary, but will not spend a lot of time to teach such concepts. Our emphasis will be on applying analytical modeling techniques to analyze the business problem on hand and demonstrate how such concepts can be used in our models.
3. Basic Excel: I will assume that students have some familiarity with Excel, but are not an Excel expert. For example, the following are expected:
   • knowing how to enter and copy simple formulas involving relative and absolute cell addresses (A1 and $A$1)
   • how to use general-purpose Excel functions (for example, the IF() function)
   • how to draw different types of graphs (bar/line) in Excel
4. Computing: This course heavily relies on hands-on, in-class learning. Therefore, it is absolutely essential to bring your laptop to each class, so that you can actively participate in developing and analyzing the example problems during class.

Cannot enroll in BUSN 36106 if BUSN 20510 taken previously.

Based on exams, homework assignments, which include spreadsheet model building and case analyses, and class participation. Both the Midterm and Final exams are take home, open book/open notes, and mandatory. Computer will be needed. 
Cannot be taken pass/fail. No auditors. No early grades. (Provisional grades permitted.)