Lahore University of Management Sciences DISC 321 - DECISION ANALYSIS Fall Semester 2014 Instructor Room No. Office Hours Email Telephone Secretary/TA TA Office Hours Course URL (if any)
Kamran Ali Chatha t 4-36, 4 Floor, SDSB Building M/W 2:00 to 3:00pm
[email protected] 042 – 3560 8094 TBA TBA suraj.lums.edu.pk
COURSE BASICS Credit Hours Lecture(s) Recitation/Lab Recitation/Lab (per week) Tutorial (per week)
4 Nbr of Lec(s) Per Week Nbr of Lec(s) Per Week Nbr of Lec(s) Per Week
2
Duration Duration Duration
110 minutes
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, multi-objective decisions, optimization models, matrix games, negotiation analysis to name a few. 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 a capability to use quantitative techniques (in relation to decision analysis) for analyzing and solving real world managerial problems; (3) 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; 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 organization’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 holistic or 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.
Lahore University of Management Sciences 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) • •
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) To learn using quantitative techniques (in relation to decision analysis) for analyzing and solving real world managerial problems; (3) 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
LEARNING OUTCOMES • • •
Decision Analysis Process, and accompanying concepts, methods and techniques. Palisade Suite for conducting quantitative analyses. Capability to take managerial decisions.
Lahore University of Management Sciences 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 Understanding 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 – Understanding 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) – Discipline Specific Knowledge and Understanding Objective: Students will demonstrate knowledge of key business disciplines and how they interact including application to real world situations (including subject knowledge). Goal 7 (b) – Understanding 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 Understanding and Reasoning Goal 3 – Analytical Thinking and Problem Solving Skills
Course Learning Objectives
Course Assessment Item Written Case Analyses. Group Project (Presentation).
To learn using quantitative techniques (in relation to decision analysis) for analyzing and solving real world managerial problems (Obj-2);
Written Case Analyses. Midterm Exam Final Exam
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); Goal 4 – Application of Information Technology Goal 5 – Teamwork in Diverse and Multicultural Environments Goal 6 – Understanding Organizational Ecosystems
Written Case Analyses. Group Project.
Lahore University of Management Sciences Goal 7 (a) – Discipline Specific Knowledge and Understanding
To expose students basic concepts, methods and techniques of decision analysis (Obj-1);;
Class Participation. Group Project.
To learn using quantitative techniques (in relation to decision analysis) for analyzing and solving real world managerial problems (Obj-2);
Goal 7 (b) – Understanding the “science” behind the decision-making process
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.
Quizzes Midterm Exam Final Exam
GRADING BREAKUP AND POLICY Written Cases Analyses / Assignment(s): 20% Home Work: Quiz(s): 10% Class Participation: 15% Attendance: Midterm Examination: 10% Project: 15% Final Examination: 30% The instructor has the right of 5% re-assigning of the criteria. Class Participation Policy Class participation grading will be carried out as per the following rules: a)
- 0.5 for being absent from the class.
b)
0.5 for attending the class.
c)
1.0 for little participation in the class discussion (granted for asking questions relevant to a discussion, describing case facts, giving an opinion or idea in relation to the discussion).
d)
1.5 for good participation in the class discussion (granted 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 (granted for hitting multiple “ds” as mentioned above)
f)
2.5 for excellent participation in the class discussion (granted 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.
Lahore University of Management Sciences EXAMINATION DETAIL
Midterm Exam
Final Exam
Yes/No: Yes Combine Separate: Combine Duration: 3 Hours in the Lab Preferred Date: Exam Specifications: Closed Books / Open Notes
Yes/No: Yes Combine Separate: Combine Duration: 4 Hours in the Lab Exam Specifications: Closed Books / Open Notes
DETAILED COURSE OUTLINE S. NO.
SESSION TYPE
TOPICS & TEXTS
SESSION OBJECTIVES INTRODUCTION
1.
Class
Topic: Introduction Read: PB-Chapter-2: Modeling in a Problem Solving Framework (Sections – 2.1, 2.2, 2.3, 2.4)
2.
Lab
Topic: Using Spreadsheet for Probability & Probability Distributions Read & Practice Solved Examples:
Decision analysis and problem solving.
Revision of probability distributions.
(1) AWZ-Chapter-5: Probability and Probability Distribution (Sections – 5.1, 5.2, 5.3, 5.4, 5.5, 5.6) (2) AWZ-Chapter-6: Normal, Binomial, Poisson, and Exponential Distributions (Sections – 6.1, 6.2, 6.3, 6.7) MODELING DECISIONS 3.
Class
Topic: Decision Analysis Case: Athens Glass Works
Making Influence Diagrams.
Read: CLEMEN-Chapter-3: Structuring Decisions pp43-65 4.
Class
Topic: Decision Trees Case: Freemark Abbey
Making and analyzing decision trees.
Read: (1) Decision Trees for Decision Making (2) CLEMEN-Chapter-3: Structuring Decisions pp69-83 5.
Class
Topic: Sensitivity Analysis Case: Dhahran Roads (A)
Role of sensitivity analysis in decision modeling, analyzing and making.
Lahore University of Management Sciences Read: (1) CLEMEN-Chapter-5: Sensitivity Analysis pp174-192 (2) Cash Flow and Time Value of Money 6.
Lab
Topic: Using Spreadsheet for Decision Trees Read & Solve Problems:
Making decision trees using spreadsheet.
AWZ-Chapter-7: Decision Making under Uncertainty, Section-7.3, (Solve Problems 36 and 37 given at the end of the chapter) MODELING UNCERTAINTY 7.
Class
Topic: Decision Making under Uncertainty Case: George’s T-Shirts
Making decisions in probabilistic situations.
Read: CLEMEN-Chapter-4: Making Choices pp111-145 8.
Class
Topic: Value of Information Case: Integrated Siting Systems, Inc. Read: CLEMEN-Chapter-12: Value of Information, pp496-509
9.
Lab
Topic: Spreadsheet Modeling for Decision Making under Uncertainty Read & Practice Solved Examples: AWZ-Chapter-7: Decision Making under
The influence of availability of information on decision.
Practicing probabilistic decisions using spreadsheets.
Uncertainty, Section-7.4, 7.5 (Examples 7.2, 7.3, 7.4) 10.
Lab
Topic: Simulation Modeling with Spreadsheets Read & Practice Solved Examples: AWZ-Chapter-16: Introduction to Simulation Modeling, Sections – 16.4, 16.5, and 16.6 along with accompanying examples.
11.
Lab
Topic: Simulation Modeling with Spreadsheets Read & Practice Solved Examples: AWZ-Chapter-17: Simulation Models, Sections – 17.2, 17.3, 17.5 and accompanying Examples)
12.
Class
Topic: Monte Carlo Simulations Case: (1) Calambra Olive Oil (A)
Understanding RISK as a package to model decisions using simulations.
Understanding RISK as a package to model decisions using simulations.
Understanding Monte Carlo simulation method.
(2) Calambra Olive Oil (B) Read: CLEMEN-Chapter-11: Monte Carlo Simulation pp 459-487 13.
Class
Topic: Risk Analysis in Monte Carlo Simulations Case: Sprigg Lane (A) Read: Probability Distributions and S imulation
Practicing Monte Carlo simulation method.
Lahore University of Management Sciences MIDTERM EXAM
MODELING PREFERENCES 14.
Class
Topic: Risk Attitude Case: Risk Preference Utility Caselets Read: CLEMEN-Chapter-13: Risk Attitude, pp 527-555
15.
Class
Topic: Risk Attitude Case: Risk Analysis for Merck & Company: Product KL-798 Read: CLEMEN-Chapter-13: Risk Attitude, pp 527-555
16.
Lab
Topic: Incorporating Risk Attitude Read & Practice Solved Examples:
Understanding the influence of manager risk attitude on decisions.
Understanding the influence of manager risk attitude on decisions.
Practicing risk attitude using spreadsheet.
(1) CLEMEN-Chapter-13: Read section named “Modeling Preferences Using PrecisionTree on page-546” and solve Problem therein. (2) AWZ-Chapter-7: Decision Making under Uncertainty, Section-7.6, Example 7.5. (3) AWZ-Chapter-7: Decision Making under Uncertainty, and solve Problems: 77, 79, 80 17.
Class
Topic: Structuring Multi-Objective Decisions Case: William Taylor and Associates (A) Read: CLEMEN-Chapter-15: Conflicting Objectives I: Fundamental
Understanding multiobjective decisions and structuring them.
Objectives and the Additive Utility Function pp599-621 18.
Class
Topic: Additive Utility Function Case: Sleepmore Mattress Manufacturing; Plant Consolidation Read: CLEMEN-Chapter-15: Conflicting Objectives I: Fundamental
Additive utility function as a method of analyzing multi-objective decisions.
Objectives and the Additive Utility Function pp599-621 19.
Class
Topic: Multi-attribute Utility Models Case: Whirlpool Research and Engineering (A) & (B) Read: CLEMEN-Chapter-16: Conflicting Objectives II: Multi-attribute Utility Models with Interactions pp644-659
Multiplicative utility function as a method of analyzing multi-objective decisions.
OPTIMIZATION MODELS 20.
Class
Topic: Optimization Value Case: Salmones Puyuhuapi; Production Planning Read: Introduction to Optimization Models (Section before Integer Programming)
Decision analysis for optimization or goal programming.
Lahore University of Management Sciences 21.
Class
Topic: Mixed Linear/Integer Programming Case: JCG Global Air Services Read: Intro. to Optimization Models (Section on Linear & Integer Programming)
22.
Class
Topic: Using Linear Programming; Results and Sensitivity Case: Chandpur Enterprises Limited: The Steel Division
Decision analysis for optimization of integer functions.
Sensitivity analysis in optimization problems.
Read: Intro. to Optimization Models (Section on Sensitivity Analysis and Beyond) STRATEGIC INTERACTIVE DECISIONS 23.
Class
Topic: Matrix Game Analysis Case: Lesser Antilles Lines: The Island of San Huberto (A) Read: (1) Structuring competitive analysis
Modeling and analyzing game theoretic situations using decision models.
(2) Competitor Analysis 24.
Class
Topic: Matrix Game Analysis Case: Germania Fluggesellschaft MBH (A) & (B) Read: (1) Structuring competitive analysis
Modeling and analyzing game theoretic situations using decision models.
(2) Competitor Analysis 25.
Class
Topic: Negotiation Analysis
Modeling and analyzing negotiations using decision models.
Case: Kelly Solar Read: None 26.
Class
Topic: Bidding and Auctions Case: Bidding For Hertz; Leveraged Buyout Read: A note on sealed bid auctions
Modeling and analyzing bidding and auction problems using decision models.
SUMMARY 27.
Class
Topic: A Comprehensive Case
Revision.
Case: Airbus and Boeing: Super Jumbo Decisions Read: None 28.
Class
Project Presentations
Project presentations. FINAL EXAM
Lahore University of Management Sciences 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.
