Quantitative Methods in Management

This course provides a comprehensive exploration of Quantitative Methods in Management, emphasizing the development and application of analytical tools to support effective decision-making in complex business and organizational settings. The course begins with an overview of Management Science, laying the groundwork for understanding the role of quantitative analysis in solving managerial problems. It progresses through foundational and advanced topics in Linear Programming, including model formulation, graphical solutions, computer-based optimization, sensitivity analysis, and diverse modeling applications. Students are introduced to specialized optimization techniques such as Integer Programming, Transportation, Transshipment, and Assignment Problems, followed by Network Flow Models that support efficient resource allocation and logistics planning. The course also covers Project Management methodologies, Multi-Criteria Decision Making, and Nonlinear Programming for addressing real-world complexities beyond linear assumptions. Further, students will gain essential skills in Probability and Statistics, Decision Analysis, and Queueing Analysis, enabling them to model uncertainty and variability in systems. The course concludes with advanced decision-support tools, including Simulation, Forecasting, and Inventory Management, which are vital for operational planning and control. With a strong emphasis on practical application and critical thinking, this course aims to develop students’ abilities to formulate, analyze, and solve quantitative models for managerial decision-making. By the end of the course, students will be equipped to use data-driven methods and optimization techniques to enhance strategic, tactical, and operational decisions in various management contexts.

• Demonstrate an understanding of the role of quantitative methods in management science and their application in solving complex decision problems.

• Formulate and solve linear, integer, and nonlinear programming models using graphical, analytical, and computer-based techniques.

• Apply optimization techniques to transportation, transshipment, assignment, and network flow models for effective resource and logistics management.

• Use probability, statistics, and decision analysis tools to address uncertainty and support risk-informed managerial decisions.

• Develop forecasting models, conduct simulation studies, and implement inventory control methods for operational planning and control.

• Interpret and present quantitative analysis results clearly to support managerial decision-making and stakeholder engagement.

1. Lecture 1: Management Science

2. Lecture 2: Linear Programming - Model Formulation and Graphical Solution

3. Lecture 3: Linear Programming - Computer Solution and Sensitivity Analysis

4. Lecture 4: Linear Programming - Modeling Examples

5. Lecture 5: Integer Programming

6. Lecture 6: Transportation, Transshipment, and Assignment Problems

7. Lecture 7: Network Flow Models

8. Lecture 8: Project Management

9. Lecture 9: Multi-Criteria Decision Making

10. Lecture 10: Nonlinear Programming

11. Lecture 11: Probability and Statistics

12. Lecture 12: Decision Analysis

13. Lecture 13: Queueing Analysis

14. Lecture 14: Simulation

15. Lecture 15: Forecasting

16. Lecture 16: Inventory Management

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1. Quantitative techniques for manager by Akash Gupta

2. Quantitative Methods for Business Decision Making

3. Intro to Linear Programming by Joshua Emmanuel

4. Linear Programming Problem by Gajendra Purohit

5. Optimization in Chemical Engineering by Prof. Debasis Sarkar

6. Optimization Techniques/Operation Research by MathPod

7. Optimization Techniques by Sukanta Nayak

8. Optimization Methods in Engineering Design by Saroj Kumar Patel

9. Optimization Techniques by Harish Garg