Quantitative Methods Course Outline and Schedule

Quantitative Methods – Course Outline and Schedule

Instructor: Francis Jones

Start Date: Oct. 17 2008

End: Nov 7 2008

Course Detail: The course introduces students to the basic principles and models of Decision Making Analysis. Some understanding of mathematics, calculus, statistics, and probability will be needed as a pre-requisite or at least a co-requisite. The course will use a combination of lectures, tutorials, group assignments. The text for the course will be Render et al., 2007, Quantitative Analysis for Management 9th Ed..

Evaluation

The passing grade is 40%.

Your grade will be a weighted average as follows:

Tutorial Participation – 10%

Assignments (2) – 30% (15% + 15%)

Final Exam – 60%

Lecture / Text Readings / Core Principles / Assigned Questions / Learning Outcomes
Lecture 1
Course Outline
Introduction to Quantitative Methods for Decision Making / Ch. 1, core text / i.) quantitative analysis approach
ii.) decision making
iii) selecting models
iv.) critical thinking / pp. 1-20

end of chapter self-test
end of chapter glossary

define a problem, develop a model
test data, develop solution
analyze results

Lecture 2
Decision Making / Ch. 3, core / i.) six steps in decision theory
ii.) DM environments
iii.) DM under risk
iv.) Marginal analysis
v.) EMV calculation / pp. 67 – 114

end of chapter self-test
end of chapter glossary
end of chapter equations
representative questions

/ 1. Understand the six steps in DM.
2. Be able to describe the types of DM environments.
3. Understand & Calculate EMV
Lecture 3
Decision Making:
Bayes Theory applied to DM
Utility Theory
Regression Theory Introduction / Ch. 3, core
Ch. 4, core: powerpoint slides only!
Also see any introductory statistics text for more details on linear regression. / i.) steps to decision making
ii.) Criterion of Realism
iii.) Bayes' Theorem
iv.) Utility analysis / pp. 67 – 114

end of chapter self-test
end of chapter glossary
end of chapter equations
representative questions

/ 1. Understand the steps in DM.
2. Be able to draw a decision tree.
3. Calculate expected values with probabilities.

Understand Utility Theory.
Understand what is regression theory

Lecture 4
Forecasting: Regression Analysis Models / Ch. 5, core
. / i.) using regression models for forecasting
ii.) determining which model to use
iii.) computing forecasts
iv.) interpreting results for DM / pp. 149-188

end of chapter self-test
end of chapter glossary
end of chapter equations
representative questions

/ 1. Eight steps to Forecasting
2. Types of forecasting models
3. Able to calculate regression line
4. Able to analyze results for DM
5. Moving Average, Time Series, Multiple Regression, Correlation Coefficient
6. Calculate and interpret MAD
Lecture 5
Inventory Control Models / Ch. 6, core / i.) inventory control with ABC analysis
ii.) economic order quantity, reorder point, quantity discounts, safety stock
iii.) sensitivity analysis / pp. 189-240

end of chapter self-test
end of chapter glossary
end of chapter equations
representative questions

/ 1. Understand inventory planning and forecasting
2. Understand ordering costs, stock out costs, over-stock costs, reorder point, safety stock
3. Able to calculate reorder point and costs
4. Able to use ABC analysis for DM
5. Able to compute sensitivity analysis
Lecture 6
Project Management:
PERT & CPM / Ch. 13, core / i.) plan, monitor and control projects with PERT
ii.) determine timing, slack time, completion time
iii.) crashing to reduce project time, CPM
iv.) PERT diagram and flowchart / pp. 525-566

end of chapter self-test
end of chapter glossary
end of chapter equations
representative questions

/ 1. Understand how to develop a PERT analysis
2. Be able to draw a PERT diagram
3. Identifying project times and costs
4. Able to “crash” a project to reduce time using CPM
Lecture 7
Project Management: PERT and CPM / Ch. 13, core / pp. 525-566

end of chapter self-test
end of chapter glossary
end of chapter equations
representative questions

what is PERT
how to make a PERT chart
what is Critical Path Method (CPM)

Lecture 8
Waiting Lines and Queuing Theory / Ch. 14, core / pp. 567-606

end of chapter self-test
end of chapter glossary
end of chapter equations
representative questions

what is a queue
what techniques can be used to analyze queues
different types of queues
calculations of queue characteristics

Lecture 9
Waiting Lines and Queuing Theory / Ch. 14, core / pp. 567-606

end of chapter self-test
end of chapter glossary
end of chapter equations
representative questions

what is a queue
what techniques can be used to analyze queues
different types of queues
calculations of queue characteristics

Lecture 10
Linear Programming
Introduction / Ch. 7, core / i.) Requirements for LP problems
ii.) formulating and solving a solution set.
iii.) graphical solutions to LP problems. / pp. 241-292

end of chapter self-test
end of chapter glossary
end of chapter equations
representative questions

/ 1. Understand the requirements for a LP problem.
2. Assumptions of LP models.
3. How to graph a LP solution (2 variables).
4. Infeasibility, Unboundedness, Redundancy.
5. Sensitivity Analysis.
Lecture 11
Linear Programming: Multiple Variables / Ch. 7, core / i.) modeling LP problems with multiple variables
ii.) application of LP models to marketing, manufacturing, finance, transportation, ingredient blending
iii.) using the computer to solve LP problems / pp. 241-292

end of chapter self-test
end of chapter glossary
end of chapter equations
representative questions

/ 1. Understand major applications areas of LP models
2. Able to formulate a multi-variable LP model
3. Understand the results of a LP solution set.
Lecture 12
Class Summary / Ch. 3, 5, 6, 13, 14, 7 /

Decision analysis summary
Forecasting summary
Inventory Control Summary
Project Management Summary
Queueing Theory Summary
LP Summary

/ Ch. 3,5,6,13,14,7:

end of chapter self-test
end of chapter glossary
end of chapter equations
representative questions

/ 1. Use DM models to assist in decision analysis
2. Understand how to take real-world data and analyze it for DM.
3. Understanding that these are only tools used in DM, it is Critical Thinking that solves problems.

NOTE: depending on how much progress is made, the last topic of Linear Programming (LP) may or may not be covered. For example, if Forecasting takes up 2 lectures, and if Inventory Control takes up 2 lectures (rather than 1 as in the above table), then the LP lectures will not occur.