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UVA-MOD-0101Y Rev. Nov. 5, 2013 DECISION ANALYSIS Syllabus Introduction Business leaders and managers make countless decisions both large and small. Some of these decisions warrant careful consideration, whereas some can be nearly automated. Regardless of the differences among them, nearly all business decisions are complicated by the presence of uncertainty. Uncertainty exists in business because managers must choose a course of action before they can know exactly what outcome will transpire. By extension, managers must choose between competing courses of action before they know which action will lead to the most desired outcome. We say that uncertainty gives rise to risk: If we agree that a manager must choose a course of action without knowing its precise outcome, then risk is the degree to which the actual outcome might deviate from the manager’s expectations. About Darden Course Syllabi The Darden Graduate School of Business Administration is regularly recognized as having one of the world’s premier teaching faculties within business education. Darden Business Publishing is pleased to provide current Darden course syllabi for verified faculty members. These syllabi provide instructors with context as to how cases could be used in a particular sequence to achieve the learning outcomes of the teaching teams at the Darden School. Use the modules in these course syllabi as a reference for updating case materials within your school’s programs. All too often, decision makers make risky choices from a qualitative gut reaction. This behavior is understandable: The human mind has both cognitive and emotional difficulties in dealing with risk. In seeking to improve on this behavior, the course is based on the following claims: 1. Nearly all business decisions can be evaluated quantitatively: The influencing factors and potential outcomes for any decision can be represented and understood numerically. Objectives that are not strictly economic can be included. 2. Individual judgment and intuition based on experience can be incorporated into the quantitative approach. This syllabus was prepared by the Decision Analysis faculty of the Darden Graduate School of Business Administration at the University of Virginia. Copyright 2011 by the University of Virginia Darden School Foundation, Charlottesville, VA. All rights reserved. To order copies, send an e-mail to [email protected]. No part of this publication may be reproduced, stored in a retrieval system, used in a spreadsheet, or transmitted in any form or by any means—electronic, mechanical, photocopying, recording, or otherwise—without the permission of the Darden School Foundation. -2- UVA-MOD-0101Y 3. Business people will achieve consistently better outcomes when they explicitly incorporate risk into their decision making. Course Description This course highlights the analytical methods that decision makers use to gain insight into risk and uncertainty to help students develop the skill and sophistication to artfully use them. Students will become better decision makers as they learn to proactively manage risk in creative ways. The course emphasizes the design of analyses to fit circumstances and interpretation of results; it does not emphasize the mastery of sophisticated mathematical techniques. It studies and integrates individual judgment and personal intuition in realistic business situations with the most widely applicable methodologies of decision and risk analysis, probability and statistics, competitive analysis, and management science. The goal is always the quality of the decisions made in light of better analysis and deeper thinking, rather than simply the analysis itself. The first half of this course focuses more on a general framework for thinking about and managing risk. Students use the language of probability distributions to describe the uncertainty they face and simulation as a tool to explore the impact of that uncertainty for the decisions they face. Situations involving uncertain future cash-flow streams and the role of the timing of those cash flows on the valuing of opportunities will be examined. The role data can play in shaping calibration of future uncertainties, specifically touching on the use of parametric probability distributions as a way to describe uncertainty, sampling as a source of data, and regression as a tool to capture the relationship between uncertain quantities to produce better forecasts are considered. Students will also gain skill in spreadsheet modeling. The second half of the course builds on the first by focusing more on ways to proactively manage risk, particularly through identifying and/or creating opportunities to add value and/or reduce risk through the sequencing of decisions. Students will consider the value of acquiring additional information before decisions have to be made, as well as the value of strategies to reduce (or eliminate) risk at the time of decision. Gaining experience with assessing uncertainties and forecasting probability distributions and learning ways to address competitive situations, where uncertainty includes not knowing how a competitor might behave, will be topics. Students will learn how to influence active competitors that are capable of anticipating their actions and responding to them (in part, via analysis of matrix games). Finally, the use of linear constrained optimization models (and Excel Solver) to aid decisions with a large number of decision variables and constraints will be studied. -3- UVA-MOD-0101Y Objectives To develop a process for knowing when and how to do managerially relevant analysis under conditions of uncertainty, many decision variables, and unstructured contexts, using both data and personal judgment. To develop a framework for understanding uncertainty, a language for describing uncertainty, and methodologies for making decisions in light of uncertainty, all through field-based case studies about practicing managers. To provide the basic skills and conceptual understanding of the most widely applicable methodologies of decision and risk analysis, probability and statistics, competitive analysis, optimization, and management science. Course Materials The cases and technical notes included in the course outline are required course materials to be prepared prior to each class meeting. Some days, there will be an available electronic spreadsheet containing data from the case. There will be online learning modules occasionally made available to students. These can be tutorials, software demonstrations, or computing tasks. Electronic items are linked from assignments or found in the online learning management system. Detailed assignment questions suggest specific steps in preparing for class. These skillbuilding days are based on expository reading about methodology to be mastered in carrying out an analysis. Sometimes only the case is indicated with a single assignment question (or perhaps none). This does not mean that no quantitative analysis is needed. Indeed, the task for students includes diagnosing the situation and structuring an approach to the analysis from the case itself as well as carrying out the analysis. Later, students will diagnose the problem and structure a framework for a decision key. A list of supplementary references follows. Since the course approaches topics from a pragmatic, decision-oriented perspective, students should, in general, be wary of devoting too much time to reading text but use the better sources of help provided by a learning team or instructor. Nonetheless, of the supplementary references, the first three are among the best. Bell, D. E. and A. Schleifer, Jr., Decision-Making Under Uncertainty (Cambridge, MA: Course Technology, Inc., 1995). Bodily, S. E., R. L. Carraway, S. C. Frey Jr., and P. E. Pfeifer, Quantitative Business Analysis: Text and Cases (New York: Irwin McGraw-Hill, 1998). Brightman, H. J., Statistics in Plain English (Cincinnati, OH: South-Western Publishing Co., 1986). -4- UVA-MOD-0101Y Clemen, R. T., Making Hard Decisions: An Introduction to Decision Analysis (Boston: PWS-Kent Publishing Company, 1991). Curwin, J. and R. Slater, Numeracy Skills for Business (London: Chapman & Hall, 1994). Eppen, G. D., F. J. Gould, and C. P. Schmidt, Introductory Management Science, 2nd ed. (Englewood Cliffs, NJ: Prentice-Hall, Inc., 1987). Freedman, D., R. Pisani, and R. Purves, Statistics (NY: W.W. Norton & Company, Inc., 1978). Hamburg, M., Statistical Analysis for Decision Making, 4th ed. (NY: Harcourt Brace Jovanovich, Publishers, 1987). Holloway, C. A., Decision Making Under Uncertainty (Englewood Cliffs, NJ: PrenticeHall, Inc., 1979). Keeney, R. L., Value-Focused Thinking: A Path to Creative Decision-Making (Harvard University Press, 1996). Kirkwood, C. W., Strategic Decision Making: Multi-Objective Decision Analysis with Spreadsheets (Belmont, CA: Duxbury Press, 1997). Lapin, L. L., Statistics for Modern Business Decisions, 2nd ed. (NY: Harcourt Brace Jovanovich, Publishers, 1978). Plane, D. E., Management Science: A Spreadsheet Approach (Danvers, MA: Boyd & Fraser Publishing, 1994). Powell, S. G. and K. R. Baker, Management Science: The Art of Modeling with Spreadsheets, 2nd ed. (Hoboken, NJ: 2007). Ragsdale, C. T., Spreadsheet Modeling and Decision Analysis: A Practical Introduction to Management Science (Cambridge, MA: Course Technology, Inc., 1995). Rosenthal, J. S., Struck by Lightning: The Curious World of Probabilities (Washington, DC: Joseph Henry Press, 2006). Savage, S. L., The Flaw of Averages: Why We Underestimate Risk in the Face of Uncertainty (Hoboken, NJ: John Wiley & Sons, Inc., 2009). Prior to class, students should not discuss the case assigned to that class with anyone who has sat through a previous (or simultaneous) class discussion of the case as side discussion and information impairs the pedagogical design of many classes and interferes with the learning process. Students are preparing for situations in which they will not have hindsight information, and they should get ready for them now. -5- UVA-MOD-0101Y Darden Course Instructors Darden Teaching Faculty Phillip Pfeifer Yael Grushka-Cockayne Casey Lichtendahl Samuel Bodily Cases by This Author Pfeifer cases Grushka-Cockayne cases Lichtendahl cases Bodily cases Course Outline Class Materials Introduction 1 “Reid’s Raisin Company (A)” (Dartmouth Tuck case) “Influence Diagram Exercises” (UVA-QA-0373) “Framework for Analyzing Risk” (UVA-MOD-0101) 2 “George’s T-Shirts” (UVA-QA-0346) “George’s T-Shirts Addendum” (UVA-QA-0548) 3 “George’s Revised Forecasts” (UVA-QA-0560) Probability Distributions: Ch. 11, QBA “PriceMax Forecasting Exercise” (UVA-QA-0748) Supplemental Spreadsheet Available. “Eliciting & Evaluating Expert Forecasts” (UVA-QA0734) 5 “Skanska USA Building” (UVA-QA-0735R) Supplemental Spreadsheet Available. Postclass learning module: Building Dhahran Spreadsheet “Evaluating Multiperiod Performance” (UVA-QA-0518) 6 “Appshop, Inc.” (UVA-QA-0618) “Quick Start Guide to Crystal Ball” (UVA-QA-0658) “Analyzing Uncertainty, Probability Distributions, and Simulation” (UVA-QA-0660) Learning Module: Crystal Ball Litigate Demo “Data and Distributions” (UVA-MOD-0102) 7 “Engine Services, Inc.” (UVA-QA-0663) Supplemental Spreadsheet Available. “A Brief Primer on Probability Distributions” (UVA-QA0517) 8 “Sprigg Lane (A)” (UVA-QA-0372) Supplemental Spreadsheet Available. 9 “Ponca City Cogeneration Plant” (UVA-QA-0469) “Ponca City Cogeneration Plant: Model Improvement and Final Decision” (UVA-QA-0747) “Ponca City Cogeneration Plant Supplement” (UVA-QA0470) 4 Topic Building and Vetting Spreadsheets; Search for Optimal Quantity Choice and Expected Monetary Value Language, Forecasting, and Use of Probability Distributions for Discrete and Continuous Uncertainties Comparing Forecasters, Overconfidence Multiperiod Pro Forma and NPV Simulating NPV Analytical Distribution from Data; Searching for a Quantity Probability Distributions and Spreadsheet Modeling; Risk Risk Management -6- UVA-MOD-0101Y Supplemental Spreadsheet Available. Learning Module: Tornado Sensitivity 10 “Amore Frozen Foods (A) Macaroni and Cheese Fill Sample Uncertainty Targets” (UVA-QA-0317) Supplemental Spreadsheet Available. “Sampling” (UVA-QA-0513) “Regression” (UVA-MOD-0103) 11 “Hightower Department Stores: Imported Stuffed Animals Introduction to Regression Introduction to Least Squares” (UVA-QA-0268) Supplemental Spreadsheet Available. 12 “Edgcomb Metals (A)” (UVA-OM-0549) Multiple Regression and Dummy Variables Supplemental Spreadsheet Available. 13 “Ballis’s Benchmark (A)” (UVA-F-1621) Data Filtering, Pivot Tables, and Regression Supplemental Spreadsheet Available. “Ballis’s Benchmark (A) Supplement” (UVA-F-1623) 14 “The Oakland A’s (A)” (UVA-QA-0282) Regression Modeling The Oakland A’s (A) Supplement “Linear Model-Building” (UVA-QA-0293) 15 “Lac Leman Festival de la Musique (A)”(UVA-QA-0707) Summary “Lac Leman Festival de lat Musique (B)” (UVA-QA0708) Supplemental Spreadsheet Available. “Decision Trees and Downstream Decisions” (UVA-MOD-0104) 16 “Freemark Abbey Winery” (HBSP case) Decision Tree Analysis, Value of Information Learning Module: Building a Freemark Decision Tree with & Control, Risk Aversion TreePlan 17 “Orion Controls (A)” (UVA-QA-0602) Downstream Decision 18 “Merck & Company: Product KL-798” (UVA-QA-0582) Decision Tree Analysis Practice 19 “Orion Controls (B)” (UVA-QA-0603) Downstream Decision with Continuous Uncertainties 20 “Chance Encounters II” (UVA-QA-0783) Real Options Supplemental Spreadsheet Available. “Strategic Interactive Decisions” (UVA-MOD-0105) 21 “Lesser Antilles Lines: The Island of San Huberto” (UVA- Matrix Game Analysis QA-0355) Supplemental Spreadsheet and Link to Online Competition Available. “Structuring a Competitive Analysis: Decision Trees, Decision Forests, and Payoff Matrices” (UVA-QA-0674) “Competitor Analysis” (UVA-QA-0527) 22 “Germania Fluggesellschaft MBH (A)” (UVA-QA-0620) Structuring Competitive Analysis 23 “Kelly Solar” (UVA-F-1614) Negotiation Analysis 24 “Bidding for the Legacy Hotel” UVA-QA-0696) Bidding and Auctions Supplemental Spreadsheet Available. 25 “Maxco, Inc., and the Gambit Company” (HBSP case) Competitive Bidding “Optimization with Solver” (UVA-MOD-0106) 26 “Jaikumar Textiles, Ltd. (A):The Nylon Division” (UVA- Optimizing with Constraints QA-0364) -7“Jaikumar Textiles, Ltd. (B): The Nylon Division” (UVAQA-0391) 28 “Chandpur Enterprises Limited, Steel Division” (UVAQA-0761) Supplemental Spreadsheet Available. 29 “Solver Optimization Modeling Exercises” (UVA-QA0606) Supplemental Spreadsheet Available. “Integration” 30 “Salmones Puyuhuapi (A)” (UVA-QA-0749) “Salmones Puyuhuapi (B)” (UVA-QA-0750) “Salmones Puyuhuapi (C)” (UVA-QA-0751) Supplemental Spreadsheet Available. 27 UVA-MOD-0101Y Linear Programming; Optimization Using Excel’s Solver Add-In Using Linear Programming; Results and Sensitivity Optimization Modeling with Solver Integration and Summary