DISC 321 Decision Analysis Spring Semester 2018 Instructor Kamran Ali Chatha Room No. 4 36, 4 th Floor, SDSB Building Office Hours TBA Email kamranali@lums.edu.pk Telephone 042 3560 8094 Secretary/TA Sec: Muhammad Umar Manzoor, TA: TBA TA Office Hours TBA Course URL (if any) http://suraj.lums.edu.pk/~ro/ COURSE BASICS Credit Hours 4 Lecture(s) Nbr of Lec(s) Per Week 2 Duration 110 minutes Recitation/Lab (per week) Nbr of Lec(s) Per Week Duration Tutorial (per week) Nbr of Lec(s) Per Week Duration COURSE DISTRIBUTION Core Elective Open for Student Category Close for Student Category Core COURSE DESCRIPTION (BRIEF) Decision Analysis is a branch of science that focuses on utilizing quantitative techniques for the purpose for making sound managerial decisions under various forms of constraints (economic, temporal and behavioral). This course exposes students to the concepts, methods and techniques of decision analysis to conceptualize real world managerial problems, analyze them and find workable solutions. The course covers topics such as: decision trees, decision making under uncertainty, value of information, risk analysis using Monte Carlo simulation, risk attitude, and multi objective decisions. A real world project and written case analyses provide avenues for practical learning. COURSE DESCRIPTION (ELABORATE) Major objectives of this course are: (1) To understand basic concepts, methods and techniques of decision analysis; (2) To develop capability to use quantitative techniques (in relation to decision analysis) for analyzing and solving real world managerial problems; (3) To have hands on experience of developing spreadsheet models (using Microsoft Excel and an add on software namely Palisade Suite) for modeling and analyzing decisions; Decision Analysis / Science is a branch of science that focuses on utilizing quantitative techniques for the purpose for making sound managerial decisions under various forms of constraints (economic, temporal and behavioral) faced in the real world problems. These problems may belong to an organziation s functional areas such as finance, operations, engineering, HRM and marketing functions etc. The problems may also be interdisciplinary in nature in which case function or discipline specific techniques when applied to solving these problems may not necessarily result into practical solutions. In such scenarios the techniques developed within the discipline of decision analysis may provide broader frameworks and concepts that render practical solutions to such problems. There are numerous examples in various disciplines where decision analysis concepts are needed for making sound decisions, for example in software engineering (e.g. decision about choosing one technology or process over the other), legal decisions (e.g., understanding the effects of economic pressures on attributions of responsibility), risk assessments (e.g., assessing risks of nuclear power or missile tests), marketing (e.g. launching specific product in a market) and managerial decision making (e.g., correcting
biases in the assessment of risk). The decision analysis concepts and frameworks are equally applicable in problems belonging to many other disciplines as well. Decision analysis relies heavily on decision theory which is concerned with identifying values of different alternatives, uncertainties involved, their utilities, and other issues relevant to a given decision, its rationality, and the resulting optimal decision. In order to exercise these concepts decision theory borrows some of the concepts from probability theory. In order to achieve aforementioned objectives two major steps have been taken while designing the course: (1) a number of real world case studies are used in order to better comprehend applicability of decision analysis concepts and techniques in real world problems. Extended class room discussions on case study analyses will be instrumental in understanding key issues pertaining to application, managerial concerns, and assumptions around the technique while focusing on the real world problem, (2) a number of lab sessions have been included in order to develop practical skills of configuring and using spreadsheets for decision analysis. COURSE PREREQUISITE(S) DISC 203 Probability & Statistics DISC 212 Introduction to Management Science DISC 230 Introduction to Business Process Modeling (Participants should possess basic knowledge of Probability / Statistics and calculus. Students should have taken DISC 203 or an equivalent course.) COURSE LEARNING OBJECTIVES Major objectives of this course are: 1. To expose students basic concepts, methods and techniques of decision analysis; 2. 3. To learn using quantitative techniques (in relation to decision analysis) for analyzing and solving real world managerial problems; To have a hands on experience of developing spreadsheet models (using Microsoft Excel and an add on software namely Palisade Suite) for modeling and analyzing decisions EXAMINATION DETAIL Midterm Exam Yes/No: Yes Combine Separate: Combine Duration: 3 Hours in the Lab Preferred Date: Exam Specifications: Closed Books / Open Notes Final Exam Yes/No: Yes Combine Separate: Combine Duration: 4 Hours in the Lab Exam Specifications: Closed Books / Open Notes UNDERGRADUATE PROGRAM LEARNING GOALS & OBJECTIVES General Learning Goals & Objectives Goal 1 Effective Written and Oral Communication Objective: Students will demonstrate effective writing and oral communication skills Goal 2 Ethical and Reasoning Objective: Students will demonstrate that they are able to identify and address ethical issues in an organizational context. Goal 3 Analytical Thinking and Problem Solving Skills Objective: Students will demonstrate that they are able to identify key problems and generate viable solutions. Goal 4 Application of Information Technology Objective: Students will demonstrate that they are able to use current technologies in business and management context. Goal 5 Teamwork in Diverse and Multicultural Environments Objective: Students will demonstrate that they are able to work effectively in diverse environments.
Goal 6 Organizational Ecosystems Objective: Students will demonstrate that they have an understanding of Economic, Political, Regulatory, Legal, Technological, and Social environment of organizations. Major Specific Learning Goals & Objectives Goal 7 (a) Program Specific Knowledge and Objective: Students will demonstrate knowledge of key business disciplines and how they interact including application to real world situations. Goal 7 (b) the science behind the decision making process (for MGS Majors) Objective: Students will demonstrate ability to analyze a business problem, design and apply appropriate decision support tools, interpret results and make meaningful recommendations to support the decision maker Indicate below how the course learning objectives specifically relate to any program learning goals and objectives. PROGRAM LEARNING GOALS AND OBJECTIVES Goal 1 Effective Written and Oral Communication Goal 2 Ethical and Reasoning Goal 3 Analytical Thinking and Problem Solving Skills Goal 4 Application of Information Technology Goal 5 Teamwork in Diverse and Multicultural Environments Goal 6 Organizational Ecosystems Goal 7 (a) Program Specific Knowledge and Goal 7 (b) the science behind the decision making process COURSE LEARNING OBJECTIVES To learn using quantitative techniques (in relation to decision analysis) for analyzing and solving real world managerial problems (Obj 2); To have a hands on experience of developing spreadsheet models (using Microsoft Excel and an add on software namely Palisade Suite) for modeling and analyzing decisions (Obj 3); To expose students basic concepts, methods and techniques of decision analysis (Obj 1);; To learn using quantitative techniques (in relation to decision analysis) for analyzing and solving real world managerial problems (Obj 2); To have a hands on experience of developing spreadsheet models (using Microsoft Excel and an add on software namely Palisade Suite) for modeling and analyzing decisions (Obj 3); To understand basic concepts, methods and techniques of decision analysis. COURSE ASSESSMENT ITEM Written Case Analyses. Group Project (Presentation). Written Case Analyses. Midterm Exam Final Exam Written Case Analyses. Group Project. Class Participation. Group Project. Quizzes Midterm Exam Final Exam
LEARNING OUTCOMES Decision Analysis Process, and accompanying concepts, methods and techniques. Palisade Suite for conducting quantitative analyses. Capability to take managerial decisions. GRADING BREAKUP AND POLICY Written Cases Analyses / Assignment(s): 20% Quiz(s): 10% (generally announced, occasionally unannounced) Midterm Examination: 10% Project: 15% Final Examination: 30% Class Participation: 15% The instructor has the right of re assigning 5% of the grading criteria. Class Participation Policy Class participation grading will be carried out as per the following rules: a) 0 for attending the class but coming late. b) 0.25 for attending the class without any participation in class discussions. c) 0.5 to 0.7 for little participation in the class discussion (awarded for engaging in a discussion, asking questions relevant to a discussion, describing case facts, giving an opinion or idea in relation to the discussion). d) 1.0 to 1.5 for good participation in the class discussion (awarded for giving a valid contradictory viewpoint or comprehensive argument or rationale behind a concept). e) 2.0 for very good participation in the class discussion (awarded for hitting multiple es as mentioned above) f) 2.5 for excellent participation in the class discussion (awarded for bringing to the class and supporting with solid argument some concepts which even instructor does not know) Group Project Students will engage in a group project. The group size will be decided based on course enrollment. Students will identify a decision situation in an organization and apply course concepts thus formulating and analyzing the problem. Following this they will synthesize and suggest an appropriate solution to the problem. They will share their solution with the case study organization, and understand from company personnel the likely problems in implementing their solutions. The feedback obtained from the company personnel will be incorporated in the final project report. A detailed description on group project will be provided once the course starts. *** A few of the student projects will be shortlisted for conversion into teaching cases. Students will be asked if they are interested to convert their projects into teaching cases that will be published in an international conference / case journal and will make part of the DA course in the future.
DETAILED COURSE OUTLINE COURSE OVERVIEW S. NO. SESSION TYPE 1. Class 2. 3. Class 4. Class 5. Class 6. Class TOPIC Introduction Objectives Hierarchy, Influence Diagrams, and Payoff Table Decision Trees Decision Trees 7. Class Decision Making CASES AND READINGS INTRODUCTION Readings: (1) PB Chapter 2: Modeling in a Problem Solving Framework (Sections 2.1, 2.2, 2.3, 2.4) (2) Learning by the Case Method, by Hammond, J.S. (HBS # 9 376 241). MODELING DECISIONS Chapter Structuring Decisions pp43 65 Case: Athens Glass Works Readings: CLEMEN Chapter 3: Structuring Decisions pp69 83 ASSIGNMENT QUESTIONS As two class sessions are devoted to this: Read PB Chapter 2 for the first class session. Read Learning by the Case Method for the second class session. What is the difference between fundamental and means objectives? How to structure decisions with influence diagrams? In class exercises on influence diagram. Focusing just on the prices discussed by Christina Matthews and Robert Alexander, which price would you recommend, $2.15 or $2.36? What are various elements of a decision tree? How are decision trees analyzed? In class exercises on making decision trees. Case: Freemark Abbey 1. Assuming Mr. Jaeger chooses to harvest the Riesling grapes before the storm arrives, how much money will he make? 2. Assuming Mr. Jaeger chooses to leave the grapes on the vine, what is the probability that the grapes will end up with botrytis, and how much money will he make if that occurs? 3. Taking account of all the various possibilities, what should Mr. Jaeger do? Chapter 4: Making Read specified material and SESSION OBJECTIVES Decision analysis and problemsolving. Developing and analyzing Influence Diagrams. Developing and analyzing Influence Diagrams. Developing and analyzing decision trees. Developing and analyzing decision trees. Making decisions in probabilistic
8. Class 9. Class under Uncertainty Sensitivity Analysis Sensitivity Analysis 10. Lab Using Spreadsheet for Decision Trees 11. Class Decision Making under Uncertainty Choices pp111 145 Chapter 5: Sensitivity Analysis pp174 192 Case: Dhahran Roads (A) Reading: Cash Flow and Time Value of Money (SKIM) MODELING UNCERTAINTY Reading: AWZ Chapter 7: Decision Making under Uncertainty, Section 7.2, 7.3. In class exercises on risk profiles (4.7, 4.8) Take home practice problems: 4.4, 4.6, 4.16, 4.19 Read specified material and 1. What do you recommend regarding the proposed contract for the Dhahran Roads project? 2. Be sure that your recommendation acknowledges any key sources of risk in the conduct of the project and any negotiable parameters of the proposed contract. 3. Does sensitivity analysis change your decision when compared to the base case? Read specified material and develop an understanding of various functions of PrecisionTree module that relate to making and analyzing decision trees using software. Solve problems 36 and 37 given at the end of the chapter. Case: George s T Shirts 1. What are the financial outcomes if Lassiter orders 5,000 T shirts? 7,500? 10,000? 2. How many T shirts should Lassiter order? situations. The role of sensitivity analysis in decision modeling and analyzing. The role of sensitivity analysis in decision modeling, analyzing and making. Making decision trees using a spreadsheet. Making decisions in probabilistic situations. MID TERM EXAM 12. Class 13. Class Value of Information Value of Information Chapter 12: Value of Information, pp496 509 Case: Integrated Siting Systems, Inc. Read specified material and Solve problems 12.2, 12.3 and 12.4 in the class. 1. What do you recommend Ms. Scott of what decision should be taken? 2. How concerned should you be about the probability of the standard system not working? The influence of the availability of information on the decision. The influence of the availability of information on the decision.
14. Lab Spreadsheet Modeling for Decision Making under Uncertainty 15. 16. Lab Simulation Modeling with Spreadsheet s 17. Class Monte Carlo Simulations How far off would your assessment have to be before you would change your recommendation? Reading: AWZ Chapter 7: Decision Making under Uncertainty, Section 7.4, 7.5. Reading: AWZ Chapter 16: (1) Probability Distributions and Simulation. (2) Introduction to Simulation Modeling, Sections 16.3, 16.4, 16.5, and 16.6. Case: (1) Calambra Olive Oil (A) (2) Calambra Olive Oil (B) 3. What about reputation? Can you afford the chance of such a visible failure? How much does reputation have to be worth to change the decision on economic grounds? 4. What is this test worth to you? What would you pay for a perfect information? Practice examples 7.2, 7.3, 7.4 in the lab. Practicing these examples will help you solve the following assignment. HOME ASSIGNMENT: Solve problems 19, 21, 22 individually and submit your solutions. Read specified material before the lab. Practice examples 16.1, 16.2, 16.3, 16.4 and 16.5 in the lab. Practicing these examples will help you solve the following assignment. HOME ASSIGNMENT: Solve problems 11, 17, 22, 26 individually and submit your solutions. To help Frank Lockfeld figure out how many gallons of olive oil he should order in 1994. Part A: In the first part, you should use the spreadsheet model LIQUIDGOLD.XLS and the ranges provided by Frank Lockfeld to develop a tornado chart to identify the important uncertainties in the problem. Be sure you can explain any surprising findings in this analysis. Part B: Using information about the uncertainties, you should develop a simulation model to resolve the key questions of the case: How much olive oil should Frank Lockfeld order? How risky is this venture? Practicing probabilistic decisions using spreadsheets. RISK as a package to model decisions using simulations. Applying Monte Carlo Simulation method in a reallife business problem.
18. Lab Simulation 19. Modeling with 20. Spreadsheet s 21. Class Risk Attitude 22. Lab Incorporatin g Risk Attitude 23. Class Class Risk Attitude Risk Attitude Reading: AWZ Chapter 17: Simulation Models, Sections 17.2, 17.3, 17.4. MODELING PREFERENCES Chapter 13: Risk Attitude, pp 527 555. Reading: AWZ Chapter 7: Decision Making under Uncertainty, Section 7.6. Caselets: Risk Preference Utility Caselets Case: Risk Analysis for Merck & Company: Product KL 798 Practice examples 17.1, 17.2, 17.3, 17.4, 17.5, 17.7, 17.8, 17.9 in the lab. Practicing these examples will help you solve the following assignment. HOME ASSIGNMENT: Solve problems 17, 18, 20 individually and submit your solutions. Read specified material and In class exercises on risk attitude (problem 13.17, 13.24). Practice example 7.5 in the lab. Solve problems 77, 79, 80 in the lab. Find solutions to the questions given in these caselets. For questions 1 and 2 only, assume that Merck will follow the advice of George W. Merck, We try never to forget that medicine is for the people. It is not for the profits. The profits follow, and if we have remembered that, they have never failed to appear. 1. First, do a risk neutral analysis. (a) What is the expected monetary value of the KL 798 opportunity? Be very clear about how your spreadsheet works. 2. Now consider risk aversion in your analysis. What would be the certainty equivalent for the total opportunity? 3. Assume that we ignore George W. Merck s advice and always seek the financially best path. (a) Draw a decision tree of the sequence of decisions and uncertainties and integrate it with the influence diagram from question 1a. (b) What now is the expected monetary value of KL 798? Clearly describe how you arrived at this solution and provide information on how your spreadsheet model works. (c) What is the certainty RISK as a package to model decisions using simulations. the influence of risk attitude on decisions. Practicing risk attitude using a spreadsheet. Practicing risk attitude. the influence of manager risk attitude on decisions.
24. Class 25. Class 26. Class 27. Structuring Multi Objective Decisions Additive Utility Function equivalent of KL 798 to Merck when considering their risk Chapter 15: Conflicting Objectives I: Fundamental Objectives and the Additive Utility Function pp599 621. Case: Sleepmore Mattress Manufacturing; Plant Consolidation. Project presentations (mandatory attendance by all students) FINAL EXAM preference? Read specified material and 1. Be prepared to discuss all the decision approaches described in the note and consider how the approaches might be applied to the case. 2. Rate the four quantitative attributes, determine the appropriate weights for the attributes and compare the three locations. If you had to phase in the consolidations one at a time, in what order would you do them? 3. How sensitive is your ranking to the weights you assigned? 4. How would you score plant size at site 1 if the sales were $30 million at plant A rather than $3 million? Would you change the range of the scale, or the weight of the attribute, or both? 5. Implicit in your analysis are some trade offs that can be calculated. For example, what is the dollar value (in terms of initial cost) of improving the labor attribute by 1 unit on the 10 point scale? multi objective decisions and structuring them. Additive utility function as a method of analyzing multiobjective decisions. TEXTBOOK(S)/SUPPLEMENTARY READINGS Following books are recommended for this course however, students are strongly encouraged to consult any other resources such as: books, journals, magazines, sharing personal experiences to enhance their learning. [AWZ]: Albright, S.C., Winston, W.L., and Zappe, C., 2006, Data Analysis & Decision Making With Microsoft Excel, 3e, Thomson, South Western, ISBN: 0 324 40083 7. [CLEMEN]: Clemen, R. T., 2001, Making Hard Decisions: An Introduction to Decision Analysis with Decision Tools, Duxbury Press, Thomson Learning, ISBN: 0 534 36597 3. [PB]: Powell, S.G., and Baker, K.R., 2009, Management Science The Art of Modeling with Spreadsheets, John Wiley & Sons Inc., ISBN 13: 978 0 470 39376 5. [ASW] Anderson, Sweeney & Williams, Statistics for Business and Economics.