Modeling Business Decisions
Code
2346
Academic unit
null
Department
null
Credits
3,5
Teacher in charge
Sofia Margarida Fernandes Franco
Teaching language
English
Objectives
Managers usually find spreadsheets natural, intuitive and user-friendly platforms for organizing information and performing what if analyses. Spreadsheets have therefore become indispensable tools of modern business analysis. This course will focus on structuring, analyzing, and solving managerial decision problems on Excel spreadsheets.
We will address problems of resource allocation (how to utilize available resources optimally), risk analysis (how to incorporate uncertainty in problem parameters), decision analysis (how to synthesize a sequence of decisions involving uncertainty), data analysis (how to summarize available data into useful information), and forecasting (how to extrapolate past data into the future).
In each area, we will consider specific managerial decision problems, model them on Excel spreadsheets, analyze and solve the models using available Excel commands, functions, tools, and add-ins, and study economic interpretations of the solutions obtained.
Prerequisites
This course involves a hands-on, in-class learning experience, so attending each class and bringing a laptop computer to class are absolutely essential. Course requirements also consist of creating and analyzing models of assigned problems and cases on spreadsheets.
Everyone is expected to know the basics of working with Excel spreadsheets, multivariable calculus, statistics and microeconomics. Basic knowledge of Excel include developing and copying formulas with relative and absolute cell addresses, and using the function and chart wizards. Students should also be familiar with equilibrium comparative statics with unconstrained and constrained optimization, the Lagrange multiplier and shadow prices, local and global optima and the envelope theorem. Everyone is also expected to have some familiarity with microeconomics and functional areas of management such as operations, and finance, marketing. Finally, one should not be averse to analytical thinking and quantitative analysis in general.
Subject matter
The Course Outline is:
1. Introduction
2. Introduction to Optimization Modeling
3. Linear Programming Models
4. Sensitivity Analysis
5. Network Models
6. Optimization Models with Integer Variables
7. Nonlinear Optimization Models
8. Forecasting Models
9. Introduction to Simulation Modeling
10. Monte Carlo Simulation Models
Bibliography
There is only one required textbook for this course: Albright, S.C. and Winston, W.L., Management Science Modeling, 3rd or 4th edition are OK, Cengage Learning. Chapters: 1, 3-7, 11 and 12.
Students should not expect the instructor to write down lecture notes on each topic and post them in moodle or to email them. However, in each class, the instructor will highlight the material she feels most important or that is conceptually difficult, where she can provide an alternate explanation or modeling to complement the textbook´s presentation. This is a very practical/interactive course and students should attend lectures and take their own notes. Whenever appropriate the instructor will provide some additional readings. Students should always read the textbook chapters on the topics discussed in class.
Teaching method
A highly interactive format with exercises and assignments is designed to engage students in problem based learning in order to help them understand complex concepts quickly. Each student should bring his or her own laptop (with Excel 2007 or higher), or else can use the facilities in the PC Pool. Course is limited to 20 students.
Evaluation method
Peer assessment
Peer Assessment is compulsory and individual.
I will made available peer evaluation forms on the last day of the course. Use them to evaluate the performance of your peers on a scale of 1 (very poor) to 5 (very good) based on the criteria listed on the form. The Assignment Participation grade of the person being evaluated for the homework assignments will be the mean score from all evaluators on the team. Discussions about Homework Participation scores are not allowed (no collaboration in determining scores). If you do not hand in peer evaluations for other members of your team, you will receive a score of zero no matter how others on the team evaluate you and/or you may be marked down 2.5 points on your final score for this course (depending on your overall course assessment by the instructor and by your peers). Peer evaluations are due by EMAIL on the same day that your LAST homework assignment is due. Late submissions are NOT accepted.
Grading
Final Grade
The course grade will be based on the following assignments. Each assignment is worth the following:
1st assignment: 30% of your grade.
2nd assignment (sent out to students on the last lecture day of the course): 40% of your grade.
Individual assignment (in class, open book), last lecture day of the course: 30% of your grade.
Note: There is NO pre-established date for the 1st assignment. The first assignment is sent out whenever the instructor feels the class is ready for that assignment. Usually, the first assignment is given half-way (3rd week) of the course.
Peer Assessment will also be factored on your final score for the group assignments. Should there be differences in the peer assessment forms, the student (s)will be required to either submit in writing to, or meet with the instructor and others (as deemed appropriate) to provide an explanation for the discrepancy. A differential allocation of grade may result from this process.
The assignments are time consuming but are designed to enhance your understanding of the process of modeling and analysis on spreadsheets learned in class. Sufficient guidelines and numerical answers will be provided (when appropriate) for each assignment, so grading will not be based on correctness of the answers per se, but on the demonstrated comprehension of the problem, the logic of your model, analytical setup and application of the spreadsheet skills learned in class. The credit will be distributed approximately as follows:
Write-up: (40%)
Introduction = Key issue, solution method, solution & insights.
Analytical Model = Set up of the mathematical formulation of the problem.
Spreadsheet Model = Decision variables, Objective, Assumptions and Formulation/Structure.
Analysis/Discussion = Excel tools and methods used.
Conclusions = Answer to problem & its interpretation and discussion.
Clarity = Clarity of the written analysis and report presentation and structure. The report must flow as a continuous story and not as a collage of different sections.
Proofreading = if the text was examined carefully to find and correct typographical errors and mistakes in grammar, style, and spelling.
Guidelines = Must be respected. Additional information will be given when an assignment is handed out.
Spreadsheets + Analytical Model: (60%)
Questions Answered = (15%) specific questions asked.
Spreadsheet Organization = (20%) for the spreadsheet organization, clarity and documentation, using formula list, gridlines, row and column headings, color coding, shading, etc.
Analytical Model = (25%) for notation, setup/structure, clarity.