SBM-NMIMS: COURSE TEACHING PLAN
Assurance of Learning AOL Specific
Course
Code
Course
Title
Modelling and Optimization for Business Decisions
Mumbai Campus:Dr. Abhinav Sharma (Course Anchor), Dr. T. Kachwala, Dr. Ashu
Sharma, Dr. Akshay Khanzode, Prof. Arti Deo
Bengaluru Campus: Prof. Satish Kumar S
Course
Hyderabad Campus: Dr Vengala Rao Pachava
Instructor/s
Indore Campus:
Dr. Shubhangi Jore
Navi Mumbai Campus: Prof. Prashant Barsing
Credit
3
Value
Programme
FT MBA; Trim II
&
Trimester
Pre-requisite
CLO 1: Develop an understanding of various mathematical formulation techniques and
models commonly used to resolve business problems. (PLO 3b)
Learning
Objectives
Learning
Outcomes
(Must be
connected to
Learning
Objectives)
Course
Description
Evaluation
Pattern
CLO 2: Apply analytical techniques to solve the mathematical model of a business problem
and derive solution(s). (PLO 4d)
CLO 3: Analyze a business problem based on given information and data, and decide best
course of action. (PLO 2c)
By the end of this course:
1. Students will be able to develop a process view of business situations and come up with
mathematical models. (CLO 1)
2. Students will be able to apply analytical skills to derive the best possible solutions to
different types of quantitative problems. (CLO 2)
3. Students will be able to evaluate alternatives and choose the best one based on various
evaluation criteria. (CLO 3)
This course provides students with essential skills to address the complex decision-making
challenges that managers encounter in the business world. The course focuses on a structured
approach to identify, model, and solve different types of business problems, leading to
effective resource utilization and competitive advantage. Students will learn how to apply
various analytical tools and techniques, including forecasting, linear programming, and
decision analysis, to enhance their understanding of business problems and generate optimal
solutions. By the end of this course, students will have a deeper understanding of the
problem-solving process and will be well-equipped to make data-driven decisions in realworld business situations.
Specific
assessment
3
Credit
1.5
Credit
AOL
Instruments
CLO 1
CLO 2
CLO 3
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methods
Test
25
Project Work
Class
participation
Final Exam
25
Total
100
(*)
Embedded
questions
Rubric
25
10
10
5
15
10
15
25
45
20
10
Embedded
questions
40
50
2
*AOL Assessment Instruments:
 Embedded Questions: Quiz, Class Test, Midterm Examination, Final Examination
 Rubrics: Case & Article Discussion, Individual Assignment
Group Projects & Viva’s, Case Problem analysis, Oral and written
communication presentations, Role Play,
Group Presentation, Group Project etc.
Pedagogy adopted for class
engagement
Learning Outcomes
session wise
Introduction
to Pre-read: Chapter 15, pp. Class discussion, lecture, and
modelling
and 654 – 671 [Textbook]
numerical problems.
optimization for business
problems.
Learning outcome:
Topics / Sub -topics
Sessions
1
Forecasting: Introduction
to time series analysis,
importance of forecasting,
common
time
series
patterns, model evaluation
metrics (MFE, MAE,
MSE, MAPE), smoothing
methods
–
moving
average, weighted moving
average
Chapter detail
/ Article Reference / Case
Studies
1. Students will be able to
develop forecasting models
with time series smoothing
methods.
2. Students will demonstrate
their ability to apply smoothing
methods to make accurate
forecasts for time series data
with random variations only.
3. Students will be able to
critically evaluate and compare
the effectiveness of different
forecasting models using a
variety of metrics, such as
Mean Forecast Error (MFE),
Mean Absolute Error (MAE),
Mean Squared Error (MSE),
and Mean Absolute Percentage
Error (MAPE)
Forecasting: Exponential Pre-read: Chapter 15, pp. Class discussion, lecture, and
smoothing method.
672 – 684 [Textbook]
numerical problems.
2
Forecasting in presence Case: Roychowdhury, S., Learning outcome:
of trend and seasonality: Shrivastava, A., and Dinesh
Forecasting
with
1. Students will demonstrate
2|Page
seasonality without trend, Kumar,
U.
(2014).
Forecasting
with Forecasting Demand for
seasonality and trend using Food at Apollo Hospitals.
additive model
Indian
Institute
of
Management Bangalore.
Forecasting in presence
of trend and seasonality:
Forecasting in presence of
trend using Holt’s method,
Time series decomposition
for multiplicative model
3
their capacity to construct and
analyze forecasting models for
time series data exhibiting
trends and/or seasonality, using
Ordinary Least Squares (OLS)
regression.
Pre-read: Chapter 13, pp. Class discussion, lecture, and
378 [Reference book-1]
numerical problems.
Chapter 6, pp. 306 – 311 Case discussion questions:
[Reference book-2]
1. Students will be able to use
Case: Roychowdhury, S., Holt's method of forecasting to
Shrivastava, A., and Dinesh generate accurate predictions
Kumar,
U.
(2014). for relevant scenarios.
Forecasting Demand for
Food at Apollo Hospitals. 2. Students will exhibit the
capability to methodically deIndian
Institute
of
compose given time series data,
Management Bangalore.
isolating and identifying its
random, trend, and seasonal
components.
Auto-regressive moving
average (ARMA) and
auto-regressive
integrated
moving
average
(ARIMA)
processes: stationary vs
non-stationary time series,
AR process, MA process,
ARMA process
4
Pre-read:
Chapter
13,
pp.388 – 398 [Reference
book-1]
Case: Roychowdhury, S.,
Shrivastava, A., and Dinesh
Kumar,
U.
(2014).
Forecasting Demand for
Food at Apollo Hospitals.
Indian
Institute
of
Management Bangalore.
Class discussion, lecture, and
numerical problems.
Class discussion:
1. What is need for ARMA and
ARIMA models?
Learning outcome:
1. Students will comprehend
and understand the differences
between stationary and nonstationary time series.
2. Students will demonstrate
the capacity to construct and
analyze (ARMA) models for
specified time series data.
5
Pre-read:
Chapter
13,
pp.398 – 405 [Reference
book-1]
Case: Roychowdhury, S.,
Shrivastava, A., and Dinesh
Kumar,
U.
(2014).
Forecasting Demand for
Power of forecasting Food at Apollo Hospitals.
Institute
of
model: Theil’s coefficient Indian
Auto-regressive moving
average (ARMA) and
auto-regressive
integrated
moving
average
(ARIMA)
processes: ARIMA model
building
Class discussion, lecture, and
numerical problems.
Learning outcome:
1. Students will be able to
determine the stationarity of the
series using Augmented Dickey
Fuller test (whichever is
applicable).
3|Page
Management Bangalore.
2. Students will demonstrate
the ability to construct and
analyze (ARIMA) models for a
given time series, employing
their understanding of ARIMA
properties
to
effectively
interpret the results.
3. Students will be able to
estimate power of forecasting
model using Theil’s coefficient.
Pre-read: Chapter 2, pp. 28 Class discussion, lecture, and
Linear programming:
– 32 [Textbook]
numerical problems
Introduction to linear
programming, formulation Case: Dhebar A. (1989).
Merton Truck Company. Class discussion:
of a linear program
Harvard
Business
1. The 4 stages in formulation
Publishing Education
and the need for these 4 stages.
2. The assumption of linearity
and how this is useful. Is it
wrong
to
make
these
assumptions in practice?
6
3. Assumption of continuity
and how this is not a limitation.
Learning Outcome:
1. Students will be capable of
formulating a linear program
for a given problem, utilizing
and synthesizing the provided
information to establish an
effective mathematical model.
Linear
programming:
Graphical
solution,
feasible area, optimal
solution, infeasibility,
unboundedness,
redundancy,
multiple optima.
7
Pre-read: Chapter 2, pp. 33 Class discussion, lecture, and
– 46 [Textbook]
numerical problems
Case discussion questions:
Case: Dhebar A. (1989).
Q.1 Develop a linear program
Merton Truck Company.
to determine optimal productHarvard
Business
mix for Merton.
Publishing Education
Q.2 Determine using graphical
method what should be the
optimal
product-mix
for
Merton.
Learning Outcome:
1. Students will demonstrate
the ability to derive the optimal
solution to a linear program,
employing
the
graphical
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procedure to visually analyze
and solve the problem.
2.
Students
will
gain
knowledge and understanding
of the key concepts in linear
programming
such
as
infeasibility, unboundedness,
redundancy,
and
multiple
optima, and will be able to
interpret
the
practical
implications of these concepts
when encountered in different
scenarios.
Linear
programming: Pre-read:
Chapter
17 Class discussion, lecture, and
Limitations of graphical [Textbook, available online numerical problems
method, Simplex method on book website]
Case discussion questions:
to solve linear programs
Q.1 Using simplex method
Case: Dhebar A. (1989).
determine what will be the
Merton Truck Company.
optimal
product-mix
for
Harvard
Business
Merton?
Publishing Education
8
Learning Outcome:
Linear programming:
Sensitivity analysis with
single and multiple
coefficient changes
9
1. Students will demonstrate
the ability to derive the optimal
solution to a linear program by
proficiently
applying
the
Simplex method, utilizing their
understanding of this key
technique
in
linear
programming.
Pre-read: Chapter 3, pp. 94 Class discussion, lecture, and
– 110 [Textbook]
numerical problems
Chapter 15, pp. 456 – 457 Case discussion questions:
[Reference book 1]
Q.1 What would be the best
product
mix
if
enginer
Case: Dhebar A. (1989).
assembly capacity were raised
Merton Truck Company.
by one unit, from 4000 to 4001
Harvard
Business
machine-hours? What is the
Publishing Education
extra unit of capacity worth?
Q.2 Assume that a second unit
of engine assembly capacity is
worth the same as the first.
Verify that if the capacity were
increased to 4100 machine
hours,
then
increase
in
contribution would be 100
times that in Q.1.
Q.3 How many units of engine
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assembly capacity can be added
before there is a change in the
value of an additional unit of
capacity?
Learning Outcome:
Linear programming:
Managerial implications of
linear programming in
decision making
10
1. Students will be able to
critically investigate the impact
of changes in the objective
function coefficient and the
right-hand side of constraints
on the optimal solution of a
linear program, demonstrating
their
understanding
of
sensitivity analysis in linear
programming
Case: Dhebar A. (1989). Class discussion, lecture, and
Merton Truck Company. numerical problems
Harvard
Business Case discussion questions:
Publishing Education
Q.1
Merton's
production
manager suggests purchasing
Model 101 or Model 102
engines from an outside
supplier in order to relieve the
capacity problem in the engine
assembly
department.
If
Merton decides to pursue this
alternative, it will be effectively
“renting” capacity: furnishing
the necessary materials and
engine
components
and
reimbursing
the
outside
supplier for labor and overhead.
Should the company adopt this
alternative? If so, what is the
maximum rent it should be
willing to pay for a machinehour of engine assembly
capacity?
What
is
the
maximum number of machinehours it should rent?
Q.2 Merton is considering the
introduction of a new truck, to
be called Model 103. Each
Model 103 truck would give a
contribution of $2,000. The
total engine assembly capacity
would be sufficient to produce
5,000 Model 103s per month,
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and the total metal stamping
capacity would be sufficient to
produce 4,000 Model 103s. The
new truck would be assembled
in the Model 101 assembly
department, each Model 103
truck requiring only half as
much time as a Model 101
truck
(a) Should Merton produce
Model 103 trucks?
(b) How high would the
contribution on each Model
103 truck have to be before it
became worthwhile to produce
the new model?
Q.3 Engines can be assembled
on overtime in the engine
assembly department. Suppose
production efficiencies do not
change and 2,000 machinehours of engine assembly
overtime capacity are available.
Direct labor costs are higher by
50% for overtime
production. While variable
overhead would remain the
same, monthly fixed overhead
in the engine assembly
department would increase by
$0.75 million. Should Merton
assemble engines on overtime?
Q.4 Merton's president, in
arguing that maximizing shortrun contribution was not
necessarily good for the
company in the long run,
wanted to produce as many
Model 101s as possible. After
some discussion, it was agreed
to maximize the monthly
contribution as long as the
number of Model 101 trucks
produced was at least three
times the number of Model
102s. What is the resulting
"optimal" product mix?
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Learning Outcome:
1. Students will demonstrate
their ability to apply linear
programming
to
propose
solutions to a variety of
managerial problems, utilizing
their understanding of both
linear
programming
and
managerial
decision-making
processes
Transportation
and Pre-read:
Chapter
17 Class discussion, lecture, and
Assignment
Problems: [Textbook, available online numerical problems
Formulation and solution on book website]
Case discussion questions:
methodology
MSIA:
Optimizing 1. How will you convert this
production, inventory and problem into a analytical
distribution at The Kellogg problem?
company
2. What will be the optimal
plan so as to minimize the
transportation costs?
11
3. What managerial insights
can you derive from the
optimal solution?
Learning Outcome:
Integer and Binary
programming problems:
Formulation and Solution
methodology (branch and
bound method)
1. Students will be capable of
formulating a transportation
problem
as
a
linear
programming (LP) model, and
deriving the optimal solution.
Pre-read: Chapter 7, pp. Class discussion, lecture, and
292 – 316 [Textbook]
numerical problems
Class discussion:
1. How to formulate integer
program?
12
2. What is the need for
additional integer constraint?
3. Can we not just round off the
non-integer solution?
Learning Outcome:
Students will be able to
formulate
integer
linear
programming problem and
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derive optimal solution.
Case discussion questions:
1.How would this problem
translate
to
optimization
problem?
2. How should CEO allot the
linac capacity over five years?
Case discussion
Case: Wang, J., Zaric, G.S.
(2015). Radiation Treatment
Machine Capacity Planning
at Cancer Care Ontario. Ivey
Publishing.
Goal Programming
Pre-read:
Chapter
14 Class Discussion, lecture and
some typical scenarios where
[Textbook pp., 614 – 626]
goal programming technique
Chapter 15, pp 475 – 478
can be utilized.
[Reference book-1]
Learning Outcomes:
13
Development: Pre-emptive
GP
14
Weighted Goal
Programming through a
Case Study
Case:
Chatterjee,
D.,
Dhaigude,
A.
(2017).
Apoorva:
A
Facility
Location Dilemma. Ivey
Publication.
1. Students will comprehend
the process of resolving
business
problems
with
multiple goals, and derive best
trade-off
solution using
technique
of
goal
programming.
Class Discussion, lecture and
some typical scenarios where
goal programming technique
can be utilized.
Case Discussion Questions:
1. What challenges is Rao
facing?
2. What are the feasible
locations for the new locations?
15
3. How should Rao evaluate the
locations
for
his
new
restaurant?
4. Can Rao use the goal
programming technique to
select the best location?
5. Will the location change if
Rao allocates weights to
criteria based on his intuition
about
the
weights
recommended in the survey
report? Analyze the different
scenarios.
6. What should Rao do, and
why?
9|Page
Learning Outcomes:
16
Students will comprehend the
concepts and techniques of
weighted goal programming
and will be equipped to make
informed decisions.
Decision Analysis: payoff Pre-read: Chapter 13, pp. Class discussion, lecture, and
matrix,
numerical, 544 – 550 [Textbook]
numerical problems
environments of decision
Learning Outcome:
making, decision making
without
probabilities:
1. Students will be able to
Maximax
(optimistic)
apply varied decision-making
criterion,
maximin
techniques to a given payoff
(pessimistic
or
matrix and evaluate the
conservative
criterion),
potential outcomes in order to
Hurwicz criterion, laplace
criterion, minimax regret
suggest the best alternative
criterion (savage criterion)
solution.
Decision
Analysis: Pre-read: Chapter 13, pp.
Introduction to decision 550 – 558 [Textbook]
tree, decision making
under
risk
(With
probability),
EVPI,
EVwPI, EVwoPI, risk and
sensitivity
analysis,
expected opportunity loss.
17
18
Class discussion, lecture, and
numerical problems
Class discussion:
1. How is decision making
under risk different?
Learning Outcome:
1. Students will demonstrate
the ability to suggest the best
course
of
action
under
conditions
of
uncertainty,
applying their understanding of
probability theory when the
likelihood of each state of
nature is provided.
2. Students will be capable of
utilizing Expected Value of
Perfect Information (EVPI),
Expected Value with Perfect
Information (EVwPI), and
Expected Value without Perfect
Information
(EVwoPI)
to
determine the most suitable
operational decision,
Decision
Analysis: Pre-read: Chapter 13, pp. Class discussion, lecture, and
Decision analysis using 559 – 572 [Textbook]
numerical problems
sample
information
(Bayesian
analysis),
Class discussion:
expected value of sample
1. Concept of Decision node
information, risk profile,
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computing
branch
probabilities with Bayes’
theorem, EVSI, EVwSI,
EVwoSI, efficiency of
sample information
and chance / outcome node.
2. What is the benefit of
Bayesian approach?
Learning Outcome:
1. Students will demonstrate
the ability to suggest the best
course
of
action
under
conditions of uncertainty.
2. Students will be capable of
utilizing Expected Value of
Sample Information (EVSI),
Expected Value with Sample
Information (EVwSI), and
Expected
Value
without
Sample Information (EVwoSI)
to determine the most suitable
decision strategy, applying
these concepts to analyze and
compare potential outcomes.
3. Students will be able to
determine the efficiency of
sample information, analyzing
its impact on decision-making
and outcome predictions.
19
20
Reading List
and
References
Group
Presentations
Group
Presentations
Project
Project
Textbook: Anderson et al. (2019). An introduction to management science. 15th ed.
Cengage Learning
(must be
comprehensive Reference books:
and complete
1. Kumar, U. D. (2022). Business analytics. 2nd ed. Wiley.
with all
2. Kreating et al. (2022). Forecasting and predictive analytics. McGraw Hill.
details.)
Prepared by Faculty Team
Area
&
Program chairpersons
Dr. Abhinav Sharma (Course Anchor)
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Approved by Associate Deans
Approved by Dean SBM
Sticker for date of receipt and attachments rubric and project guidelines
MOBD Project Guidelines (AY 2024 – 2025)
As a part of MOBD coursework, students are required to do a group project which carries 25
marks towards final assessment. Each group must identify a business domain and apply the
techniques discussed in class to solve problem You can take primary / secondary literature
based projects also. For e.g. refer to literature and understand how a particular organization/
industry has benefited from using optimization? Further, you should refer to research papers,
web articles, cases for project work. The final project should cover Identification of Issues,
Selection of Methodology, Analysis, Strategic Recommendations and Performance
Implications. Project proposals must be submitted by 10th session EOD. You are advised to
remain in touch with faculty for feedback and guidance. Report submission is due on 18th
session of the course and presentations + viva will be conducted in 19th and 20th session.
Project Report Guidelines
1. The report must be written in a word processing software.
2. First page should consist of tile of project, name, and roll number of group members.
3. Use a spacing of 1.15 and text must be justified across margins.
4. Use a font size of 12 (headings + paragraphs).
5. Headings and sub-headings must be numbered, bold and left aligned.
6. Use multi-level list to number headings and sub-headings.
7. Table title should appear on top of table along with table number.
8. Figure caption should appear on bottom along with figure number.
9. Your project report should contain (not limited to):
(a) Introduction and identification of issues
(b) Methodology
(c) Analysis
(d) Results
(e) Strategic recommendations and performance implications
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(f)Conclusions
10. References should be cited in APA 7th edition format.
The project rubric is given on next page.
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Course: Modelling and Optimization for Business Decisions
Trimester II Assessment: Group Project Evaluation
Faculty :
Division :
Group No. :
Criteria
Performance
CLO 1: Process
View and
Mathematical
Modelling
Exemplary [9-10]
The project demonstrates a deep
understanding of the underlying
processes in the business
situation and uses mathematical
models to analyze them.
Good [6-8]
The project demonstrates a solid
understanding of the underlying
processes in the business
situation and uses appropriate
mathematical models to analyze
them.
Average [4-5]
The project demonstrates some
understanding of the underlying
processes in the business
situation and uses basic
mathematical models to analyze
them.
Poor [<4]
The project does not demonstrate a
clear understanding of the
underlying processes in the
business situation or does not use
mathematical models appropriately
to analyze them.
CLO 2: Analytical
Skills
Exemplary [9-10]
The project uses appropriate
tools and techniques to solve
complex business problems,
demonstrating a high level of
proficiency in quantitative
analysis.
Good [6-8]
The project uses appropriate
analytical tools and techniques to
solve business problems,
demonstrating a solid level of
proficiency in quantitative
analysis.
Average [4-5]
The project uses some analytical
tools and techniques to solve
business problems, but may lack
depth or sophistication in
quantitative analysis.
Poor [<4]
The project does not use
appropriate analytical tools and
techniques to solve business
problems or shows a lack of
understanding of quantitative
analysis.
Exemplary [5]
The project evaluates a range of
alternatives and uses objective
criteria to choose the best option,
demonstrating a high level of
proficiency in decision-making.
Good [4]
The project evaluates alternatives
and uses appropriate criteria to
choose the best option,
demonstrating a solid level of
proficiency in decision-making.
Average [2]
The project evaluates some
alternatives but may not use
appropriate criteria to choose
the best option, or the decisionmaking process may lack
objectivity.
Poor [1]
The project does not evaluate
alternatives or does not use
appropriate criteria to choose the
best option, or the decision-making
process lacks objectivity.
CLO 3:
Evaluation and
Decision-making
Marks
Obtained
Total
Signature of the Faculty
Date
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