Course : Decision Mathematics for Marketing

Course Code : BMKT 21233

Credit Value : 3

Status : Compulsory

Level : Level 2

Semester : Semester I

Overall Learning Outcomes : At the end of the session students should be able to;

  1. evaluate marketing related problems based on the techniques available under discrete mathematics
  2. recommend the best possible solution for a given scenario by interpreting the outcome of the problem analyzed using decision mathematical techniques
  3. convince how far the decisional mathematical techniques could be applied in marketing related decision making
  4. integrate the classroom learning to solve a real world business scenario given as a case study
  5. construct the best possible solution with the given information related to a real world business scenario accurately
  6. practice the techniques learned under the subject into decision making related to the project carried out as a group work
  7. practice the techniques under discrete mathematics in order to make best possible solution for business based problems

Course Content         :

Topic No

Topic

Learning Outcomes

Teaching

and

Learning Method

Method of Assessment

1

Introduction to decision making using discrete mathematics

At the end of the session students should be able to;

  1. discuss the importance of using discrete mathematics for decision making under marketing context
  2. appraise the techniques available under discrete mathematics for marketing related decision making
  3. examine the difference between discrete mathematic techniques and the basic business mathematics
  4. produce answers for classroom questions independently after the completion of the lessons
  5. decide the relevant discrete mathematical technique that matches with a given situation accurately

Lectures

Class room discussions

End Semester Examination

2

Linear Programming in marketing decisions

At the end of the session students should be able to;

  1. identify the types of problems that can be solved using linear programming
  2. formulate liner programming problems with different objectives based on business scenarios
  3. determine constraints that have unique structures based on the objective identified for the problem
  4. solve linear programming problems graphically and interpret the answers
  5. solve a variety of linear programming problems using MS Excel and interpret the excel output of the problem
  6. integrate the interpretations into managerial decision making for a given scenario

Lectures

Problem based learning

Tutorials

End Semester Examination

Answers for the questions given at tutorial sessions

3

Decision making for transportation and assignment problems

At the end of the session students should be able to;

  1. formulate transportation problems and assignment problems accurately with given cost information
  2. solve transportation problems to get the basic solution by using North West Corner (NWC) rule or Least Cost Method (LCM)
  3. ascertain the Optimal solution for a transportation problem using Modified Distribution Method (MODI) with MS Excel
  4. solve assignment problems using MS Excel
  5. integration of the classroom learning to make the optimal solution for a scenario based transportation and an assignment problem
  6. practice the techniques learned in order to make the most feasible managerial/management decisions for a business scenario

Lectures

Practical sessions using MS excel

Buzz groups

End Semester Examination

4

Project Management for marketing

At the end of the session students should be able to;

  1. appraise the importance of using network models in decision making
  2. differentiate the Critical Path Method (CPM) and Program Evaluation and Review Technique (PERT) with examples
  3. construct a network diagram accurately by considering given cost and time constraints
  4. decide the duration of the project with given probabilities in order to recommend the strategy to manage the project within the given time frame
  5. practice the classroom learning with the application of techniques into a small project carried out as a group work

Lectures

Problem based learning

Implementation of a project as a group work

End Semester Examination

Written report based on group work

Presentations based on group work

5

Decision theory

At the end of the session students should be able to;

  1. differentiate decision making under certainty, risk and complete uncertainty with examples
  2. solve decision making problems under risk with expected value criterion using MS Excel
  3. construct decision trees that consist of decision alternatives and events to arrive at a final decision
  4. integrate the classroom learning to arrive at the optimal decision for a given marketing scenario
  5. practice the decision making theory by applying the correct tools based on the given scenario

Lectures

Problem based learning

Tutorials

End Semester Examination

6

Waiting line models for service systems

At the end of the session students should be able to;

  1. appraise the importance of learning waiting line models for decision making under marketing
  2. evaluate problems using waiting line models with the identification of appropriate model accurately
  3. produce recommendations for a given business scenario in service marketing through the application of waiting line models
  4. practice classroom learning to arrive at decision related to service systems

Lectures

Problem based learning

Case study

End Semester Examination Written report based on case study analysis

7

Game theory and marketing in action

At the end of the session students should be able to;

  1. appraise in which circumstances game theory can be applied in decision making in the marketing context
  2. evaluate the different types of strategies available under game theory for a marketer
  3. ascertain the best strategy for a player in an industry by using principles of game theory on a given scenario
  4. integration of the classroom learning into decision making under given circumstances in a selected industry

Lectures

Problem based learning

End Semester Examination

Recommended Reading       :

  1. Anderson, D.R. and Sweeney, D.J. (2011), “An Introduction to Management Science: Quantitative Approaches to Decision Making”, 13th edition, Cengage Learning.
  2. Hillier, F.S., Lieberman, G.J., Nag, B. and Basu, P. (2010), “Introduction to Operations Research”, McGraw-Hill Higher Education.
  3. Pai,  P. P. (2012), “Operations Research”,1st edition,  Oxford University Press India.
  4. Pomerai,  S. D. and Berry, J.  (2001) , “Decision Mathematics :Discovering Advanced Mathematics”, 1st  edition , Heinemann.
  5. Roueche, N. R., Michae, V. G. and Tuttle, I. D. (2005), "Business Mathematics: A Collegiate Approach", 9th edition, Prentice Hall.
  6. Stevenson, W.J. and Ozgur, C. (2012) , “Introduction to Management Science with Spreadsheets” , 9th edition, Tata McGraw – hill.

 

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