UNIVERSITY
School WALSH for Professional Studies
BUS 320
Quantitative Methods II, Decision Models
(8-Week Format)
ASSIGNMENTS DUE ON THE FIRST NIGHT OF CLASS: SEE PAGE 4
PREREQUISITE BUS 220
July 2006
DIVISION OF BUSINESS AND ECONOMCS BUS 320 Quantitative Methods II, Decision Models
COURSE DESCRIPTION The study of selected math-based models used by organization decision makers in analyzing frequently encountered decisions. Topics include: decision game theory, forecasting and time series analysis, linear programming, project scheduling and simulation. COURSE OBJECTIVES This course provides students with a conceptual understanding of the role that quantitative methods play in the decision making process. Upon completion of this course, students will be able to: Identify and define the business problem. Develop the mathematical models that describe the objectives, constraints, and other relationships the problem that exist in the problem. Distinguish the differences between qualitative and quantitative analysis in Decision making. Illustrate various decision theories and models such as Linear Programming, Analyze the Simulations and Markove processes. Applied inventory management models with and without independent demands. Develop the queuing models in decision-making process. Apply project scheduling model in order to manage the planning and control a wide variety of projects. Develop and illustrate a transportation model in order to minimize the cost of shipping goods from the origins to the destinations. Illustrate a multi-criteria decision problem and apply goal programming techniques. Define and identify the components of time series and utilize smoothing methods in forecasting. MATERIALS OF INSTRUCTION Required Text: Quantitative Methods for Business by David R. Anderson, Dennis J. Sweeney, and Thomas A. Williams, South-Western College Publishing Co. Tenth Edition, 2006. *Please review the School for Professional Studies textbook list for most up to date edition.
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NOTES REGARDING SUCCESSFUL COMPLETION OF COURSE This course is concerned with the application of quantitative methods to assist managers in decision-making process. The emphasis is not on the methods themselves, but rather on showing how they can contribute to a better decision-making process. The approach is to describe and demonstrate situations in which quantitative methods have been applied successfully and then show how a manager can use those models and methods to make a better decision. This course requires students to use computer applications such as The Management Scientist v6.0, the TreePlan add-in and Crystal ball Pro packaged on the student CD-ROM that is included as part of this textbook along with Microsoft Excel for decision analysis issues. This is an intensive and fast-paced course. This course will meet once a week, for four hours at a time. The accelerated pace requires students to take a great deal of responsibility for their own learning outcomes. Therefore, it is strongly suggested that students regularly work at least ten (10) hours outside class on assigned chapters, problems, and case studies. It is imperative that students make a strong commitment to complete assignments before each class session. Self-Test Problems: In addition of weekly assignments, your facilitator may assigns certain at the end of chapters problems as identified as self-test problems. Completely worked-out solutions for these problems are provided in Appendix G at the end of text. Students can attempt the self-test problems and immediately check the solution to evaluate their understanding of the concepts presented in the chapter. Students can also check their answers to even numbered problems in Appendix F.
GRADING CRITERIA The suggested weight for the grading criteria is below. The facilitator may present alternative grading criteria at the first night of class. Item First test Second Test Ten (10) Case problems Final Exam Total Points 100 points 100 points 100 points 200 points 500 points
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GRADING SCALE A letter grade will be assigned based on total points earned by each student as follows: A AB+ B BC+ C CD+ D DF 470-500 points 450-469 435-449 420-434 400-419 385-399 370-384 350-369 335-349 320-334 300-319 Below 300 points
ASSIGNMENTS TO BE COMPLETED PRIOR TO WORKSHOP ONE 1. Be sure that you have all the required course materials. 2. Read Chapters One and Four. 3. Work on the following Problems: Chapter 1: 8 (self-test), 10, 11, 12, 14, 15 and 16. Chapter 4: 1 (self-test), 2, 3, 4, 5, 6, 7, 8, 9, 14 (self-test), 16, 19 and 22. Case Problem: Lawsuit Defense Strategy on Page 145.
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WORKSHOP ONE _____________________________________________________________
OBJECTIVES At the end of this class, students are expected to: State the basic problem solving and decision-making. Analyze models of cost, revenue, and profit. Illustrate and analyze the payoff tables and decision trees. Define decision making process with optimistic, conservative, and mini-max regret approaches. Explain and show risk analysis and sensitivity analysis.
ACTIVITIES
1. 2. 3. 4. Introduction and completion of administrative paperwork. The class reviews the course objectives, homework, tests, and grading criteria. Lecture and discussion over Chapters One and Four in the textbook. Work on assigned homework.
ASSIGNMENTS TO BE COMPLETED PRIOR TO WORKSHIP TWO 1. Read Chapter Five and Six. 2. Work on the following problems on: Chapter Five: 1 (self-test), 2, 3, 5, 6, 8, 9, 10, 12, 14, 15, and 16. Chapter Six: 1 and 2 (self-test), 3, 4, 5, 7, 8, 9, 10, 14, 15, and 16. Case problem: Forecasting Sales by using Excel on Page 224.
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WORKSHOP TWO _____________________________________________________________
OBJECTIVES At the end of this class, students are expected to: State the basic components of Expected Utility Approach. Demonstrate steps for determining the utility of money. Distinguish between Risk Avoider and Risk takers by applying Utility theory. Differentiate Expected Monetary value versus Expected utility. Explain how Game Theory can be used for competing for market share. Demonstrate mixed strategy games. Compute Moving Averages, Weighted Moving Averages, and exponential smoothing models. time-series Demonstrate time-series data to identify and analyze trends and seasonality. Use Excel to analyze a variety of forecasting models. ACTIVITIES 1. Lecture and discussion on Chapter Five and Six. 2. Work on assigned homework on Chapter Five and Six. 3. Discuss the case problem on Chapter Six..
ASSIGNMENTS TO BE COMPLETED PRIOR TO WORKSHOP THREE 1. Read Chapters Seven and Eight. 2. Work on the following problems on Chapters Seven and Eight: Chapter Seven: 7 (self-test), 8,10 (self-test), 11, 12, 13, 14, 15, 21, 24, 29, and 31. Case Problem: Production Strategy on Page 282. Chapter Eight: 1 (self test), 2, 3, 4, 7, 12 (self Test), and 16. Case Problem; Production Mix on Page 342. 3. Prepare to take FIRST TEST over Chapters 1, 4, 5 and 6.
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WORKSHOP THREE
OBJECTIVES At the end of this class, students are expected to: Develop of a Linear Programming Model. Formulate a Linear Programming Problem. Use graphical procedures to solve LP problems with only two variables. Illustrate special situations such as feasibility, optimal solution with maximizing and minimizing objectives. Demonstrate how linear programming is applied using Excel and the Management Scientist v6.0 software( The CD packaged with the text).
ACTIVITIES
1. Lecture/discussion over Chapters Seven and Eight materials. 2. Work on assigned homework on chapters Seven and Eight. 3. Review assigned chapters for first test. 4. FIRST EXAM over chapters 1, 4, 5 and Six
. ASSIGNMENTS TO BE COMPLETED PRIOR TO WORKSHOP FOUR 1. Read Chapters Nine and Ten. 2. Work on the following problems on Chapter Nine and Chapter Ten by using Management Scientist CD: Chapter Nine: 1 (self-test), 2, 3, 4, 5, 6, 7, 11 and 15. Case Problem: Planning an Advertising Campaign on Page 413. Use Management Scientist on your CD. Chapter Ten: 1 (self-Test), 4, 5, 6, 7,9, 10, 12, 13,14, and 18. Case Problem: Distribution System design on Page 464.
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WORKSHOP FOUR _____________________________________________________________
OBJECTIVES At the end of this class, students are expected to: Model a wide variety of Linear Programming (LP) problems. Gain experience in setting up and solving problems using Excel and Management Scientist software. Apply a general linear programming model for the transportation problem. Explain how a general linear programming would be applied for assignment problem. Apply transportation model to solve production and inventory problems. Show how the network model is used in the transshipment problems.
ACTIVITIES
1. Lecture/discussion over Chapters Nine and Ten materials. 2. Work on assigned homework on chapters Nine and ten. 3. Class discussion over case problems.
ASSIGNMENTS TO BE COMPLETED PRIOR TO WORKSHOP FIVE 1. Read Chapters Twelve and Thirteen. 2. Work on the following problems on Chapters twelve and Thirteen: Chapter Twelve: 2, 3, 4, 5, 7,8, 9, 10 (self-test), 13, 15, 17, 21 and 24. Case Problem: R.C. Coleman on Page 552 Chapter Thirteen: 1 (self-test), 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, and 20. Case Problem: Wagner Fabricating company on Page 597.
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WORKSHOP FIVE _____________________________________________________________ OBJECTIVES
At the end of this class, students are expected to: Develop a PERT network. Determine earliest start, earliest finish, latest start, latest finish, and slack times for each activity. Explain Economic Order Quantity (EOQ) Model. Determine the How-Much and When-to-Order decisions. Illustrate the sensitivity analysis in the EOQ model. Explain inventory model with planned shortages and quantity discounts.
ACTIVITIES
1. Lecture and discussion over Chapters Twelve and Thirteen. 2. Work on assigned problems on Chapters Twelve and Thirteen. 3. Class discussion ...