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Master Syllabus

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Administrative Unit: Business Administration Department
Course Prefix and Number: MGMT 430
Course Title: Management Science
Number of:
Credit Hours 3
Lecture Hours 3
Lab Hours 0
Catalog Description:

Management Science is a discipline that integrates mathematical modeling and quantitative analysis into the managerial decision-making process. A variety of models and approaches are introduced including: linear programming and optimization models (e.g., maximize profit or minimize cost problems, resource-allocation problems), network and transportation models (e.g., shortest route problems, critical path problems), forecasting models, PERT/CPM models (e.g., a model to determine the optimal schedule for a project), simulation models and the use of Crystal Ball, and simple/multiple regression models. Students learn to model problems mathematically and to use spreadsheet packages to solve management science problems. The goal of the course is to provide students with a background in mathematical modeling to augment their problem-solving skills. Prerequisites: MATH 150 or MATH 170; MATH 250 or PSYC 324.

Prerequisite(s) / Corequisite(s):

MATH 150 or MATH 170; MATH 250 or PSYC 324.

Course Rotation for Day Program:

Offered Spring.

Text(s): Most current editions of the following:

Most current editions of the following:

An Introduction to Management Science
By Anderson, Sweeney, Williams, and Martin (Thomson-Southwestern)
Practical Management Science
By Winston and Albright (South-Western Cengage)
Introduction to Management Science: A Modeling and Case Studies Approach with Spreadsheets
By Hillier and Hillier (McGraw-Hill Irwin Publishers)
Spreadsheet Modeling for Business Decisions
By Kros (McGraw-Hill Irwin Publishers)
Course Learning Outcomes
  1. Describe the theory behind several widely-accepted data-driven quantitative approaches currently in use by real-world managers to make better and/or more timely decisions and predictions, including the basic mathematical theory underlying these quantitative approaches.
  2. Create valid data-driven predictive business models combining these new concepts with business concepts learned in earlier course work.
  3. Implement these models using current analytical tools such as Excel and Risk Solver.
  4. Evaluate the performance and usefulness of these models in business management terms.
Major Topics/Skills to be Covered:
  • Introduction to modeling and management science
  • Optimization and linear programming
  • Solving linear programming problems in a spreadsheet
  • Sensitivity analysis
  • Network modeling
  • Integer linear programming
  • Transportation problems
  • Queuing models
  • Simulation and forecasting
  • Simple and multiple regression
  • Crystal Ball
  • Project scheduling: PERT/CPM
Culminating Experience Statement:

Material from this course may be tested on the Major Field Test (MFT) administered during the Culminating Experience course for the degree. 
During this course the ETS Proficiency Profile may be administered.  This 40-minute standardized test measures learning in general education courses.  The results of the tests are used by faculty to improve the general education curriculum at the College.


Recommended maximum class size for this course: 20

Library Resources:

Online databases are available at You may access them from off-campus using your CougarTrack login and password when prompted.

Prepared by: Patrick Feehan Date: April 8, 2015
NOTE: The intention of this master course syllabus is to provide an outline of the contents of this course, as specified by the faculty of Columbia College, regardless of who teaches the course, when it is taught, or where it is taught. Faculty members teaching this course for Columbia College are expected to facilitate learning pursuant to the course learning outcomes and cover the subjects listed in the Major Topics/Skills to be Covered section. However, instructors are also encouraged to cover additional topics of interest so long as those topics are relevant to the course's subject. The master syllabus is, therefore, prescriptive in nature but also allows for a diversity of individual approaches to course material.

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