Comprehensive Course Structure

The Operations Management program is structured over 8 semesters, offering a balanced blend of theoretical knowledge and practical application. The curriculum includes core courses, departmental electives, science electives, and hands-on laboratory sessions designed to build comprehensive operational literacy.

Semester	Course Code	Course Title	Credit (L-T-P-C)	Pre-requisites
I	MATH101	Calculus and Differential Equations	3-1-0-4	None
	MATH102	Linear Algebra and Probability	3-1-0-4	MATH101
	PHYS101	Physics for Engineers	3-1-0-4	None
	CHEM101	Chemistry for Engineers	3-1-0-4	None
	ENG101	English Communication Skills	2-0-0-2	None
	ECE101	Introduction to Electrical Engineering	3-1-0-4	PHYS101
	CSE101	Introduction to Computer Science	3-1-0-4	None
	MECH101	Engineering Mechanics	3-1-0-4	MATH101, PHYS101
	MECH102	Manufacturing Processes	3-1-0-4	MECH101
	MGT101	Introduction to Management	2-0-0-2	None
II	MATH201	Statistics and Numerical Methods	3-1-0-4	MATH102
	MATH202	Operations Research	3-1-0-4	MATH102
	PHYS201	Modern Physics and Applications	3-1-0-4	PHYS101
	CHEM201	Organic Chemistry	3-1-0-4	CHEM101
	ECE201	Electronic Devices and Circuits	3-1-0-4	ECE101
	CSE201	Data Structures and Algorithms	3-1-0-4	CSE101
	MECH201	Thermodynamics and Heat Transfer	3-1-0-4	MECH101, MATH102
	MECH202	Mechanics of Materials	3-1-0-4	MECH101
	MGT201	Business Economics	2-0-0-2	MATH102
	MGT202	Organizational Behavior	2-0-0-2	MGT101
III	OPM301	Introduction to Operations Management	3-1-0-4	MGT201
	OPM302	Production Planning and Control	3-1-0-4	OPM301
	OPM303	Inventory Management	3-1-0-4	OPM301
	OPM304	Quality Management	3-1-0-4	OPM301
	OPM305	Supply Chain Management	3-1-0-4	OPM301
	OPM306	Logistics and Distribution Systems	3-1-0-4	OPM301
	OPM307	Operations Research II	3-1-0-4	MATH202
	OPM308	Project Management	3-1-0-4	OPM301
	OPM309	Lean Manufacturing	3-1-0-4	OPM301
	OPM310	Service Operations Management	3-1-0-4	OPM301
IV	OPM401	Data Analytics in Operations	3-1-0-4	MATH201, CSE201
	OPM402	Digital Transformation in Operations	3-1-0-4	OPM301
	OPM403	Sustainable Operations Management	3-1-0-4	OPM301
	OPM404	Risk and Compliance Management	3-1-0-4	OPM301
	OPM405	Advanced Supply Chain Analytics	3-1-0-4	OPM305
	OPM406	Operations Strategy and Planning	3-1-0-4	OPM301
	OPM407	Process Optimization Techniques	3-1-0-4	OPM301
	OPM408	Decision Support Systems	3-1-0-4	OPM401
	OPM409	Operations Research III	3-1-0-4	MATH202
	OPM410	Capstone Project	6-0-0-6	All OPM courses

Detailed Departmental Elective Courses

The department offers several advanced elective courses that allow students to specialize in areas aligned with current industry trends and research interests:

1. Machine Learning for Operations (OPM401): This course focuses on applying machine learning algorithms to solve operational challenges. Students learn how to build predictive models for demand forecasting, optimize inventory levels, and automate decision-making processes using Python and TensorFlow.
2. Digital Twin Technology in Manufacturing (OPM402): Explores the concept of digital twins—virtual replicas of physical systems used for simulation, monitoring, and optimization. The course covers modeling techniques, real-time data integration, and applications in smart factories.
3. Green Supply Chain Management (OPM403): Addresses environmental sustainability within supply chains. Topics include carbon footprint reduction, green procurement, reverse logistics, and circular economy principles applied to manufacturing and distribution.
4. Operational Risk Analysis (OPM404): Teaches students to identify, assess, and mitigate operational risks using quantitative methods and scenario planning. Case studies from industries such as aviation, pharmaceuticals, and finance are analyzed to understand risk management strategies.
5. Customer-Centric Operations (OPM405): Examines how customer feedback and experience influence operational decisions. Students explore service design frameworks, customer journey mapping, and the role of operations in enhancing customer satisfaction.
6. Smart Logistics Systems (OPM406): Focuses on integrating IoT sensors, GPS tracking, and automation into logistics networks. The course includes hands-on lab sessions using simulation software to model warehouse operations and transportation routes.
7. Process Simulation and Modeling (OPM407): Introduces students to simulation tools like Arena, Flexsim, and AnyLogic for modeling complex operational systems. Emphasis is placed on validating models and interpreting results for strategic decision-making.
8. Sustainable Manufacturing (OPM408): Covers eco-design principles, waste minimization techniques, energy efficiency improvements, and compliance with environmental regulations in manufacturing environments.
9. Enterprise Resource Planning (ERP) Systems (OPM409): Provides an overview of ERP concepts and implementation strategies. Students learn to configure systems like SAP, Oracle, and Microsoft Dynamics for operational efficiency.
10. Data Visualization and Reporting (OPM410): Teaches effective data visualization techniques using tools such as Tableau and Power BI. The course emphasizes the importance of clear reporting in driving informed operational decisions.

Project-Based Learning Framework

The program emphasizes project-based learning as a cornerstone of student development. From the second year onwards, students engage in mini-projects under faculty supervision that simulate real-world challenges. These projects often involve collaboration with industry partners and provide valuable exposure to practical problem-solving.

Mini-projects are typically completed in groups of 3-5 students and span approximately 6 weeks. Each project includes a detailed proposal, progress reports, peer evaluations, and a final presentation before a panel of faculty and industry experts.

The final-year capstone project is a significant component of the program, requiring students to work on an original research or applied problem in operations management. The project is supervised by a faculty mentor and culminates in a written thesis and a public defense. Students are encouraged to publish their findings in academic journals or present at conferences.

Project selection involves a competitive process where students propose topics aligned with faculty expertise or industry needs. Mentorship is provided throughout the project lifecycle, ensuring that students receive guidance on methodology, data analysis, and presentation skills.