MGM 350: Quantitative Analysis Models

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Plattsburgh State

Institute of Operations Research and the Management Sciences

International Federation of Operational Research Societies

Instructor:	Jim Owens
Office:	Redcay 139
Phone:	564-2775
Time and place:	M-T-W-Th 10:30-12:50 Hawkins 229
Email:	jim.owens@plattsburgh.edu
Office hours:	Mon - Thur 1:00 - 2:00 pm

Prerequisites

MAT 161 Intro Statistics or ECO 260 Economic Statistics I
MAT 221 Calc: Life, Mgmt, Soc Sci I
MGM 280 Principles of Management

Course Description

This course deals with the use of quantitative techniques for management decision making with applications in business. An examination of representative techniques is intended to develop an acquaintance with such an approach to decision making.

Learning Objectives

LO1:Decision Making:

Students will be able to successfully apply Minimax, Maximin, Maximax, and Minimin approaches to solve problems.

LO2:Multi-criteria Models:

Students will demonstrate a mastery level understanding of multi-criteria decision models, to include criterion development, weight assessment, and model scoring.

LO3:Linear-Programming Software Tools:

Students will be able to successfully apply spreadsheet and other, specialized software tools to develop linear programming solutions.

LO4:Analytic Understanding:

Students will demonstrate a competency level capability to analyze and interpret model solutions and then subject these solutions to sensitivity analysis and modification.

LO5:Network and Logistics Modeling:

Students will demonstrate a competency level understanding of the critical factors in assessing network and logistics modeling problems, including shortest route, maximal flow, transshipment, and assignment problems.

LO6:Application of Appropriate Analysis Model:

Given an unstructured case, students will be able to successfully analyze the situational dimensions, apply the appropriate analysis methodology, and competently interpret the modeling results.

Course Perspectives

The topics detailed below are expected to be a part of every core course outline in the School of Business and Economics at Plattsburgh State.

There exist “perspectives” that form the context for business activities and study. Knowledge relating to such perspectives receives, to a greater or lesser extent, emphasis in all business and economics courses. To the extent specified on the matrix below, faculty in the department teaching this course cover these perspectives through examples, case studies, assignments or reading material. Similarly, a particular course may provide, in one way or another, opportunities for students to enhance their performance or understanding with respect to specified skills. For this particular course, the emphasis given to enhancing knowledge of selected perspectives and developing particular skills, ranging from none to high is as follows:

Skills Enhancement	Mod	High
Written communication	X
Oral communication	X
Mathematical analysis		X
Statistical analysis		X
Computer literacy		X
Team building	X
Research methods		X
Analytical & integrative processes	X

Knowledge Enhancement	Low	Mod
Ethical	X
Global	X
Political, Social & Legal		X
Regulatory	X
Environmental	X
Technological		X
Demographic diversity	X

Course Overview

In today’s complex business environment, more and more managers use quantitative techniques and analysis to increase their decision process effectiveness. Quantitative management uses mathematical models, statistics and information aids to support managerial decision-making. For example, Operations managers, who are responsible for planning and coordinating the use of the organizations’ resources in order to convert inputs into goods or services; make extensive use of quantitative analysis/methods. Management Science, also known as Operations Research, techniques are widely used by a very large number of organizations. These include banks and other financial institutions, national and local government, the Health service, oil, steel and engineering industries, airlines, railways and many others. The course starts with an overview of the field of Operations Research/Management Science and a presentation of a variety of real world applications. Then, commonly used quantitative methods and techniques are presented. The topics covered include decision making process, decision tree analysis, transportation and assignment models, linear programming, inventory management, project management, network models and multicriteria decision models.

Course Learning Goals

The overall goal of this course is for the student to become an effective user of operations research technology. This includes:

developing the insight to notice when Management Science tools are appropriate for solving a decision problem
developing an understanding of the use of quantitative techniques in decision making
developing the expertise to build mathematical models for decision problems
selecting the suitable approach for solving the problem at hand
applying optimization software to solve the mathematical models
analyzing and interpreting the solutions
modifying the models and performing sensitivity analysis

Required Text

An Introduction To Management Science: Quantitative Approaches To Decision Making. Anderson, Sweeney, and Williams; Thomson South-Western Publishing, Eleventh Edition, 2005. ISBN: 0-324-20231-8.

Required Software

The Management Scientist, Version 6.0 by Anderson, Sweeney, and Williams; Thomson South-Western Publishing, 2005. (Included with textbook.)

Evaluation Schedule

Evaluation	Approx. date	Time Allowed	Weight
Exam #1	6/13/06	1 hour	20%
Exam #2	6/20/06	1 hour	20%
Case problem (take-home)	6/29/06	n/a	20%
Final exam	7/6/06	2 hours	30%
Homework/Participation/Attendance	n/a	n/a	10%

Note: Exam dates subject to change.

Teaching Method

We will rely on the text to provide detailed descriptions of the topics, together with some supplemental readings. Students are expected to read the assigned material, solve the assigned problems and cases, come well prepared to each class, and actively participate in class discussions. The class time will be devoted to:

clarifying difficult points/items and illustrating the mathematical methods
discussing and solving the problems and cases assigned
using the computer to solve application problems
analyzing the solutions (computer output) and performing sensitivity analysis

Course Rules

There will be no make-up exams.
Assignments and their due dates will be given in class. Assignments are due at the beginning of the class period on the due date. Late assignments will not be accepted.
Assignments will be word-processed and/or printed from the Management Scientist or electronic spreadsheet. Handwritten assignments will not be accepted.
Class attendance is mandatory. More than two absences will result in a 0 participation mark; more than four absences will result in a final grade of "E".
Students are responsible for making up any work they miss due to absences.
It is expected that all assigned reading will be done prior to the class meeting for which it is assigned.
Cheating (including plagiarism) is not tolerated and will lead to expulsion from the course

Grading Scale

Final Average	Final Grade
94 - 100	A
90 - 93	A-
85 - 89	B+
80 - 84	B
76 - 79	B-
70 - 75	C+
65 - 69	C
61 - 64	C-
56 - 60	D+
51 - 55	D
<=50	E

Course Outline

Note: This schedule is an approximation and is subject to change.

Session	Topic	Learning Goal(s)	Chapter/ Section	Problems & Cases
1	Intro to Management Science Quantitative Analysis Management Science techniques	1 - 2	Chap 1	Video and Handouts
2 - 3	Linear Programming: Formulation Applications	1 - 4	2.1, 2.5, 4.1 - 4.4	#21(a) p74 #23(a) p75 #26(a), #28(a) p76
4	Simplex method/ Computer solution	4 - 6	2.3, 2.4, App. 2.1	Solve applications problems in 4.1 - 4.4 using MS software
5	Sensitivity analysis	7	3.3 - 3.4	#11-#13, p132 and case problem handout
6	Decision Making Risk, uncertainty and certainty Expected value Minimax/Maximin/ Maximax/Minimin Expected value of perfect information Decision tree analysis	2, 4	14.1 - 14.3	#2, #3 p687 #5 p688 #6 p689 #14 p693
7	Data Envelopment Analysis (DEA)	1 - 6	4.5	#26, #27 p217 #29 p218 Case problem handout
8 - 9	Transportation Problems Assignment Problems Transshipment Problems	2 - 6	7.1 - 7.3	#4 p356, #5 p357, #13 p361, #15, #18 p362 and case problem handout
10 - 11	Project management Critical path method PERT technique Known activity times Uncertain activity times	2 - 7	10.1 - 10.2	#5 p484, #7 p485, #10 p486, #12 p487
12 - 13	Multi-criteria Decision Models Scoring models The AHP model	2 - 6	15.3 - 15.6	#10 p749, #12 p750, #18 p 752, #24, #25 p753
14 - 15	Network models Shortest route problem Minimal spanning tree problem Maximal flow problem	2 - 6	9.1 - 9.3	#1, #2, #3 p446, #6 p448, #11, #12 p450, #13 p451, #15 p452, #16 p453, #18 p454
16 - 17	Integer Linear Programming	2 - 6	8.1 - 8.3	#4 p410, #8 p412, #10 p 413, #15 p415

Class materials