Course Detail

Managerial Decision Modeling (36106)

Course Description by Faculty

  • Gupta, Varun
  • Content
    Perhaps more aptly titled "Optimization for the Management Student," this course is designed to teach students the basic optimization tools and analytic problem solving skills required for decision making in business. We will learn how to:

    • Structure a decision problem: identifying the objective, decision alternatives (i.e., outputs), input parameters, and sources of uncertainty.
    • Build a mathematical model to formalize the decision problem: We will learn about
      • Optimization models (linear, nonlinear) - for resource allocation (how to utilize available resources optimally)
      • Decision tree models - for multiperiod sequential decision making
      • Simulation models - for risk analysis and incorporating uncertainty in problem parameters.
    • Analyze 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: While spreadsheets are somewhat limited in the size and nature of models we can build, they 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-ons such as Tornado charts, 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 lecture. 

    The lectures will be structured as a brief introduction to an optimization model (its strengths, weaknesses) followed by an interactive discussion of a few (3-5) chosen toy problems from business areas including operations, marketing, finance, and strategy. We will develop the optimization models for these problems and implement them in Excel.
  • Prerequisites

    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 trying to teach such concepts. Our emphasis would 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. Props: This course involves in-class software demonstrations and “hands-on” practices. To get the most out of the lectures, and to minimize the number of hours spent outside lectures, students are expected to bring their laptop to class each week.

    Cannot enroll in BUSN 36106 if BUSN 20510 taken previously.
  • Grades
    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.
    • Allow Provisional Grades (For joint degree and non-Booth students only)

    • No auditors

    • No pass/fail grades

  • Syllabus
  • Autumn 2023Section: 36106-01F 8:30AM-11:30AMHarper CenterC04In-Person Only
  • Autumn 2023Section: 36106-02F 1:30PM-4:30PMHarper CenterC04In-Person Only
  • Autumn 2023Section: 36106-81F 6:00PM-9:00PMLocation: TBDRemote-Only
Description and/or course criteria last updated: September 25 2023