POST GRADUATE PROGRAMME IN MANAGEMENT
AY 2015-16 TERM: II
TITLE OF THE COURSE: QUANTITATIVE TECHNIQUES- II
CREDITS: 3
Name of the Faculty
Prof. Bhavin J Shah
Prof. Harshal Lowalekar
Prof. Bhuvanesh Pareek
Prof. Nagarajan Krishnamurthy
Prof. T Radha Ramanan
Faculty Block/
Room No.
C 206
C 204
A 212
C 210
C 106
Email
bhavinj@iimidr.ac.in
harshal@iimidr.ac.in
bhuvaneshp@iimidr.ac.in
nagarajan@iimidr.ac.in
ramanan@iimidr.ac.in
Telephone
Number
+91-731-2439563
+91-731-2439578
+91-731-2439569
+91-731-2439581
+91-731-2439475
COURSE DESCRIPTION
Decision making is the most important job for any manager. The possibilities in a real life scenario
and the associated consequences make the decision maker’s job difficult. So it is necessary to do
formal and systematic analysis of decision problems. This course aims at sharpening analytical
skills for decision-making under certainty as well as uncertainty.
COURSE OBJECTIVES
1.
2.
3.
Enhance participants’ ability to structure managerial problems and analyse them with a
rational perspective
Strengthen participants’ ability to incorporate relevant contextual information in the
decision making process
To demonstrate the use of analytical / formal methods in Operation s Research for
managerial decision making under certainty as well as uncertainty.
PEDAGOGY/TEACHING METHOD
Mix of lectures, cases, discussions and exercises.
EVALUATION
Class Participation
Quizzes (Two)
End Term Exam
Total
Weightage
20%
40%
40%
100%
LEARNING OUTCOMES
At the end of the course student is expected to accomplish the following learning outcomes (CLO).
Alignment of CLO with the Programme Level Goals & Objectives and Assessment of the learning
outcomes of the co urse is presented below.
Course Learning Outcome
1. Enhance participants’
ability to structure
managerial problems and
analyse them with a rational
perspective
2. Strengthen participants’
ability to incorporate relevant
contextual information in the
decision making process
Program Level Goals/ Outcome
3.2 Uses available information
and suggested sources
Assessment Tool(s)
Embedded Question(s)in
Mid-Term
3.2 Uses available information
and suggested sources
Embedded Question(s)in
Mid-Term
7.1 Identifies the right set of
data with co rrect calculations to
facilitate decision making in
business
3. To demonstrate the use of 3.3 Identifies and presents
analytical / formal methods in appropriate evidence
supporting the analysis of
Operations Research for
alternatives
managerial decision making
7.1 Identifies the right set of
under certainty as well as
data with correct calculations to
uncertainty
facilitate decision making in
business
Embedded Question(s)in
Mid Term and End-Term
SCHEDULE OF SESSIONS
Module I
LINEAR PROGRAMMING
Module Objective:
To introduce participants to problem structuring, linear programming and
various solution methods
Session 1, 2:
Objective :
Quantitative Approach to Decision Making
To introduce participants to problem structuring and quantitative
approaches to decision making
Note on Linear Programming, Harvard Business School (1992), Product #:
191085-PDF-ENG, 12 pages
Class room Exercises and Problems
Reading:
Case :
Session 3:
Objective :
Reading:
Case :
Session 4:
Objective :
Case :
Linear Programming: Graphical Solution and Sensitivity Analysis
To introduce participants to linear programming and graphical solution
method and sensitivity analysis
Note on Linear Programming, Harvard Business School (1992), Product #:
191085-PDF-ENG, 12 pages
Merton Truck Company, Harvard Business School (1990), Product #:
189163-PDF-ENG, 4 pages
Linear Programming: Duality
To teach concepts of duality and shadow price in LP
Merton Truck Company, Harvard Business School (1990), Product #:
189163-PDF-ENG, 4 pages
Module II
APPLICATIONS OF LINEAR PROGRAMMING
Module Objective:
To apply LP in solving complex business decision problems
Sessions 5, 6:
Objective :
Case :
Relevant Costs and Application of Linear Programming
To apply LP to real life situation
Red Brand Canners, Stanford Graduate School of Business (1965), Product #:
OSA1-PDF-ENG, 5 pages
Sessions 7, 8:
Objective :
Case :
Linear Programming: Analysis of Complex Problems
To apply LP to structure complex business decision problems
Chandpur Enterprises Limited (CEL), Steel Division, Darden School of
Business (2011), Product #: UV6307, 4 pages
Module III
EXTENSIONS OF LINEAR PROGRAMMING: IPP AND MILP
Module Objective:
To introduce concepts of Integer Programming and Mixed Integer Linear
Programming
Sessions 9, 10:
Objective :
Case:
Mixed Inter Linear Programming
To introduce structuring of problems using integer programming
Class room Exercises and Problems
Session 11:
Objective :
Case :
Application of Mixed Inter Linear Programming
To demonstrate the application of MILP in complex problems
JCG Global Air Services, Darden School of Business (2014), Product #:
UV1317-PDF-ENG, 4 pages
Module IV
DECISION TREES
Module Objective: To teach decision making under uncertainty. This module covers problem
structuring and decision trees.
Session 12:
Objective :
Reading:
Case:
Session 13:
Objectives:
Reading:
Case:
Structuring Problems with Decision Trees
To introduce the concept of decision trees to assess alternatives indecision
situations involving uncertainty
Decision Trees, Harvard Business School (2006), Product #: 9205060
Canonical Decision Problems, Harvard Business School (2008), Product #:
396308- PDF-ENG, 14 pages
Application of Decision Trees
To teach the applications of decision trees
Decision Trees for Decision Making, Harvard Business Review (1964) by
John Magee, 12 pages
Canonical Decision Problems, Harvard Business School (2008), Product #:
396308-PDF-ENG, 14 pages
Session 14:
Objective:
Reading:
Case:
Session 15:
Objective:
Case:
Value of Information
To teach the concept of Value of Information and its use in decision making
Value of Information, Harvard Business School (1994), Product #: 9191138PDF-ENG, 5 pages
Freemark Abbey Winery (Abridged), Harvard Business School (2005),
Product #: 606004-PDF-ENG, 2 pages
Application of Decision Trees
To discuss decision situations that can be modeled using decision trees
Merck & Company - Evaluating A Drug Licensing Opportunity, Harvard
Business School (2003), Product #: 201023-PDF-ENG, 6 pages
REFERENCES
1.
2.
3.
David R. Anderson, Dennis J. Sweeney and Thomas A. Williams. Introduction To
Management Science: Quantitative Approaches To Decision Making, Cengage Learning
John A. Lawrence and Barry A. Pasternack. Applied Management Science: Modeling,
Spreadsheet Analysis, And Communication For Decision Making, Wiley India Edition
Frederick S. Hiller and Greald J. Lieberman. Introductio n to Operations Research, Tata
McGraw Hill, Inc.
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