Comprehensive Course Structure
The Operations program at Gurukul Kangri Vishwavidyalaya Faculty Of Management Studies is structured to provide a comprehensive academic journey that spans eight semesters, combining foundational knowledge with specialized expertise. Each semester builds upon the previous one, ensuring that students develop both depth and breadth in their understanding of operational principles.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | MATH101 | Engineering Mathematics I | 3-0-0-3 | None |
| 1 | ENG101 | Engineering Graphics and Design | 2-0-0-2 | None |
| 1 | CS101 | Introduction to Computer Programming | 2-0-0-2 | None |
| 1 | BME101 | Basic Mechanical Engineering | 3-0-0-3 | None |
| 1 | ENG102 | Introduction to Operations Management | 3-0-0-3 | None |
| 1 | PHYS101 | Physics for Engineers | 3-0-0-3 | None |
| 2 | MATH102 | Engineering Mathematics II | 3-0-0-3 | MATH101 |
| 2 | ELEC101 | Basic Electrical Engineering | 3-0-0-3 | None |
| 2 | CS102 | Data Structures and Algorithms | 3-0-0-3 | CS101 |
| 2 | BME102 | Thermodynamics and Heat Transfer | 3-0-0-3 | BME101 |
| 2 | ENG103 | Systems Analysis and Design | 3-0-0-3 | ENG102 |
| 2 | PHYS102 | Modern Physics and Applications | 3-0-0-3 | PHYS101 |
| 3 | MATH201 | Probability and Statistics for Operations | 3-0-0-3 | MATH102 |
| 3 | CHEM101 | Chemistry for Engineers | 3-0-0-3 | None |
| 3 | CS201 | Database Management Systems | 3-0-0-3 | CS102 |
| 3 | BME201 | Materials Science and Engineering | 3-0-0-3 | BME102 |
| 3 | ENG201 | Supply Chain Fundamentals | 3-0-0-3 | ENG103 |
| 3 | ECON101 | Introduction to Economics | 3-0-0-3 | None |
| 4 | MATH202 | Linear Algebra and Numerical Methods | 3-0-0-3 | MATH201 |
| 4 | CS202 | Software Engineering | 3-0-0-3 | CS201 |
| 4 | BME202 | Mechanical Systems and Dynamics | 3-0-0-3 | BME201 |
| 4 | ENG202 | Operations Research | 3-0-0-3 | ENG201 |
| 4 | ENG301 | Quality Management Systems | 3-0-0-3 | ENG201 |
| 5 | MATH301 | Advanced Mathematics for Operations | 3-0-0-3 | MATH202 |
| 5 | CS301 | Machine Learning for Business Applications | 3-0-0-3 | CS202 |
| 5 | BME301 | Automation and Control Systems | 3-0-0-3 | BME202 |
| 5 | ENG302 | Lean Manufacturing | 3-0-0-3 | ENG301 |
| 5 | ENG401 | Project Planning and Control | 3-0-0-3 | ENG202 |
| 6 | MATH302 | Statistical Inference and Modeling | 3-0-0-3 | MATH301 |
| 6 | CS302 | Advanced Data Analytics | 3-0-0-3 | CS301 |
| 6 | BME302 | Industrial Robotics and Automation | 3-0-0-3 | BME301 |
| 6 | ENG402 | Sustainable Operations | 3-0-0-3 | ENG302 |
| 6 | ENG501 | Risk Management in Operations | 3-0-0-3 | ENG401 |
| 7 | CS401 | Advanced Simulation Techniques | 3-0-0-3 | CS302 |
| 7 | BME401 | Human Factors in Operations | 3-0-0-3 | BME302 |
| 7 | ENG502 | Strategic Decision Making in Operations | 3-0-0-3 | ENG501 |
| 7 | ENG601 | Operations Innovation and Entrepreneurship | 3-0-0-3 | ENG502 |
| 8 | CS402 | Capstone Project in Operations | 3-0-0-3 | ENG601 |
| 8 | BME402 | Systems Engineering Principles | 3-0-0-3 | BME401 |
| 8 | ENG602 | Internship and Industry Collaboration | 3-0-0-3 | ENG601 |
Detailed Departmental Elective Courses
Departmental electives in the Operations program are designed to deepen students' understanding of specialized areas within the field. These courses allow students to tailor their education according to their interests and career goals.
1. Machine Learning for Business Applications
This course introduces students to the fundamental concepts of machine learning and their practical applications in business settings. It covers supervised and unsupervised learning techniques, neural networks, and deep learning architectures. Students learn how to apply these methods to solve real-world operational challenges such as demand forecasting, customer segmentation, and predictive maintenance.
2. Advanced Data Analytics
This course focuses on advanced statistical and computational methods used in data analytics for operations management. Topics include regression analysis, time series modeling, clustering techniques, and big data processing using tools like Apache Spark and Hadoop. Students gain hands-on experience through projects involving large datasets from real-world industries.
3. Lean Manufacturing
This course explores the principles of lean manufacturing and their application in various industrial settings. Students study value stream mapping, 5S methodologies, Kaizen events, and continuous improvement processes. The course emphasizes practical implementation through case studies and simulations.
4. Sustainable Operations
This course examines the integration of environmental sustainability into operational practices. It covers topics such as green supply chains, circular economy principles, carbon footprint reduction, and sustainable manufacturing techniques. Students analyze real-world examples and develop strategies for implementing eco-efficient operations.
5. Service Operations Management
This course focuses on the unique challenges of managing service delivery systems. It covers service quality measurement, customer experience design, service process modeling, and service innovation strategies. Students engage with case studies from sectors such as healthcare, finance, and hospitality.
6. Risk Management in Operations
This course provides students with tools and techniques for identifying, assessing, and mitigating operational risks. It covers financial risk modeling, operational risk frameworks, business continuity planning, and crisis management strategies. The course includes practical exercises involving risk scenario analysis.
7. Strategic Decision Making in Operations
This course explores how strategic decisions impact operational performance. It covers topics such as competitive advantage through operations, innovation management, portfolio analysis, and long-term planning frameworks. Students develop skills in evaluating strategic options using quantitative and qualitative methods.
8. Operations Innovation and Entrepreneurship
This course encourages students to think creatively about operational challenges and opportunities. It covers entrepreneurship fundamentals, innovation processes, idea generation techniques, and business model development. Students work on developing innovative solutions to operational problems through team projects.
9. Systems Engineering Principles
This course introduces systems thinking and engineering approaches to complex operations management problems. It covers system modeling, simulation techniques, optimization methods, and integration of multiple subsystems. Students learn to design and evaluate large-scale operational systems using engineering principles.
10. Industrial Robotics and Automation
This course explores the role of robotics and automation in modern manufacturing operations. It covers robotic control systems, industrial automation technologies, machine vision, and smart factory concepts. Students gain practical experience through laboratory experiments and project work involving robotic applications.
Project-Based Learning Philosophy
The department's philosophy on project-based learning is centered around experiential education that bridges theoretical knowledge with real-world application. Projects are designed to simulate actual industry challenges, providing students with authentic learning experiences that enhance their problem-solving and analytical skills.
Mini-projects are assigned in the second and third years of the program, allowing students to explore specific operational concepts under guided supervision. These projects typically last 2-3 months and involve small teams working on defined problems with clear deliverables.
The final-year capstone project is a comprehensive endeavor that spans the entire academic year. Students select projects in consultation with faculty mentors and industry partners, ensuring relevance and impact. The project includes research, analysis, design, implementation, and presentation phases.
Project Selection and Mentorship
Students begin selecting their final-year projects during the sixth semester, with guidance from faculty advisors who match student interests with available research opportunities. Projects can be industry-sponsored, research-oriented, or innovation-driven, ensuring diversity in scope and application.
Faculty mentors are selected based on expertise aligned with the project topic and availability of resources. Each student is paired with a primary advisor and may have additional support from visiting industry experts or alumni professionals.
Evaluation Criteria
Projects are evaluated using multiple criteria including technical depth, innovation, feasibility, presentation quality, and impact analysis. Students must submit progress reports at regular intervals and defend their work in front of a panel of faculty members and industry experts.