代做36106 Managerial Decision Modeling Fall 2023代做Python语言
- 首页 >> Database作业36106-01/02/81: Managerial Decision Modeling
Fall 2023
Course Objectives
Perhaps more aptly titled “Optimization and Simulation Modeling 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 (or any other aspect of your life). 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
1. Optimization models (linear, nonlinear) - for resource allocation (how to utilize available resources optimally)
2. Decision tree models - for multiperiod sequential decision making
3. 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.
● Advanced modeling in R/Python: Time permitting, we will see a quick introduction of optimiza- tion and simulation using R/Python.
Instruction modality
The full time sections are in-person only, and students enrolled in the full time sections will not be able to attend the evening remote section. The evening remote sections will be recorded, but the recordings will only be made available to students under exceptional circumstance per University and Booth policies.
The lectures will be structured as a brief recap/introduction of a modeling framework (its strengths, weaknesses) followed by hands-on model building of a few (2-3) chosen example problems from areas including operations, marketing, finance, and strategy. For some of these examples, we will build models at a fairly brisk pace. The screengrab videos of the spreadsheet modeling part of the examples will be posted on Canvas after the lecture for your future reference.
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.
Assignment 0:
1. Mandatory: Excel refresher
(a) Visit https://edu.gcfglobal.org/en/excel2010/ and go through Lessons 1-10, 17 (Charts), 19 (Conditional Formatting).
(b) Next, visit https://edu.gcfglobal.org/en/excelformulas and go through Lessons 1–5 (About – Functions), 7 (Double-Check Your Formulas).
(Going through the other lessons on the website for self-edification is recommended but not required
(c) Finally, go through the following from https://edu.gcfglobal.org/en/excel-tips/ :
(19) How to Use Excel’s VLOOKUP Function , (20) Use SUMPRODUCT to Calculate Weighted Averages, (2) A Faster Way to Use the Fill Handle, (14) Absolute References with the F4 Key, (17) What are Named Ranges
2. Optional: Review from Powell and Baker: Appendix 1 (4th/5th Ed.) (For 3rd Ed. : Chapter 3, Chapter 4 (Sections 4.1-4.6))
Course Material
● Textbook (optional but recommended): The contents of the textbooks available on this topic do not vary much. The following textbook was chosen because it gets concepts across without being overly verbose. See the Supplemental Texts section for some books which are available free electronically through UChicago library.
Title: Business Analytics: The Art of Modeling with Spreadsheets Authors: Stephen G. Powell, Kenneth R. Baker
Edition: 5th Edition
ISBN: 978-1-119-29842-7
Publisher: John Wiley and Sons Inc.
(Note: The 5th edition is a small update of Management Science: The Art of Modeling with Spreadsheets by the same authors. For our purposes, Editions 4 (978-1-118-59269- 5) or 3 (ISBN: 978-0-470-39376-5) of this book are also sufficient. The major change in Edition 4 over Edition 3 is a slightly expanded treatment of data mining algorithms for classification and prediction. These will not be a major focus in the course.)
● Course Packet Through Canvas
● Software
1. Microsoft Excel 2013 or later
2. Excel Solver (included with Excel)
3. Some freely available Excel add-ins (e.g., Tuck Tornado, SolverTable, OpenSolver) - an- nounced as needed
4. DecisionTools Suite Industrial 8 by Palisade Corporation (includes Precision Tree, @RISK, and RiskOptimizer)
Note: Booth has purchased student licenses for 36106. Download the installer zip from Can- vas under “Modules → Software Installation Instructions and Links → Palisades Installer.zip.” This software may prove useful in other Booth courses.
Note: We will not use the Risk Solver platform. associated with the Powell and Baker textbook.
Important: Since the DecisionTools Suite is not compatible with Mac version of Excel, you must be Windows compatible. If you own a Mac, you can get Excel running under a Windows emulator, e.g. Parallels Desktop or VirtualBox.
Supplemental Texts/Readings
● Microsoft Office Excel 2013: Data Analysis and Business Modeling. Wayne L. Winston. Microsoft Press.
An excellent reference for learning relevant Excel functions and contains numerous examples on using Excel for modeling business decision problems.
● Practical Management Science (4th Edition). Wayne L. Winston, S. Christian Albright. South- Western.
A nice collection of decision modeling problems from different areas in business. This book uses the DecisionTools Suite as the platform. A free academic version of DecisionTools Suite is also available with new copies of the fourth edition.
● Managerial Decision Modeling : Business Analytics with Spreadsheets (4th edition). Balakrishnan, Render, Stair, Munson.
An electronic version is available free through U.Chicago libraries.
● Competing on Analytics: The New Science of Winning. Thomas H. Davenport, Jeanne G. Harris. Harvard Business School Press.
● The Flaw of Averages: Why we underestimate risk in the face of uncertainty. Sam L. Savage. Wiley.
Grades
● Based on exams, 7 assignments (2 individual, 5 group assignments), which include spreadsheet model building and case analyses, and class participation:
Homework + peer-reviews (7 × 5%) 35%
Midterm (Due Week 6, take home, 6 day duration) 20%
Final (Finals week, take home, 3 day duration) 40%
Class participation 5%
● Both the Midterm and Final exams are take home, open book/open notes, and mandatory. Com- puter will be needed.
● The final exam will be tentatively released at noon on Sunday, Dec 3 and due at noon on Wednesday, Dec 6. No requests for early or late Final will be accepted. Please make sure that you can devote the required number of hours for the Final during this window before enroling.
● This class can not be taken pass/fail or audited.
● Final exam will be comprehensive, and include material from week 1 through 5 as well.
● All grade disputes must be submitted in writing (or email) within two weeks of an assignment/exam being returned. You must indicate which questions you feel were graded incorrectly and clearly explain why you feel your answer is correct. Your entire assigment/exam may be regraded, possibly lowering your grade, if I find something that was misgraded.
Homework Assigments
● Homework assigments will be posted on the Canvas website one week before the due date.
● An electronic copy of the homework must be submitted to the Canvas site by the time indicated on the assigment. The online submission must include a write-up (MS Word or pdf file) and spreadsheet models (MS Excel file(s)).
● No late homework will be accepted.
● On all group assigments, you may work in groups of size 3 or 4. You may form. a group across sections. However, assignments must be turned in before the beginning of lecture of the earliest section any group member is registered for. You need to submit only a single piece of work. The first two assignments will be individual to give you time to form. groups.
● You may not discuss the homework assignment with anyone not in your group. You cannot share or transmit written or electronic answers or analysis to any other student who is not a member of your group.
● Peer evaluation: A good proportion of the learning in this course will happen while working on the assignments with your group members. To this end, at the end of the course you will be asked to fill a form. evaluating the members of your assignment group. How your peers grade you will play a role in the final homework score.