Course Structure Overview
The Operations program at Prayaga Institute of Management Studies is structured over 8 semesters, with a balance of core courses, departmental electives, science electives, and practical labs. Each semester spans approximately 16 weeks, allowing for in-depth exploration of topics and meaningful engagement with faculty and peers.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| 1 | ENG101 | English Communication Skills | 2-0-0-2 | - |
| 1 | MAT101 | Calculus and Differential Equations | 3-0-0-3 | - |
| 1 | MAT102 | Linear Algebra and Vector Calculus | 3-0-0-3 | - |
| 1 | PHY101 | Physics for Engineers | 3-0-0-3 | - |
| 1 | CHE101 | Chemistry for Engineers | 3-0-0-3 | - |
| 1 | CS101 | Introduction to Programming | 2-0-2-3 | - |
| 1 | ENG102 | Engineering Graphics | 2-0-2-3 | - |
| 2 | MAT201 | Probability and Statistics | 3-0-0-3 | MAT101 |
| 2 | PHY201 | Electromagnetic Waves and Optics | 3-0-0-3 | PHY101 |
| 2 | CHE201 | Physical Chemistry | 3-0-0-3 | CHE101 |
| 2 | CS201 | Data Structures and Algorithms | 2-0-2-3 | CS101 |
| 2 | ENG201 | Engineering Mechanics | 3-0-0-3 | - |
| 2 | MAT202 | Transform Calculus and Partial Differential Equations | 3-0-0-3 | MAT101 |
| 3 | ME301 | Thermodynamics and Heat Transfer | 3-0-0-3 | MAT202 |
| 3 | CSE301 | Database Management Systems | 2-0-2-3 | CS201 |
| 3 | MAT301 | Numerical Methods and Optimization Techniques | 3-0-0-3 | MAT201 |
| 3 | ENG301 | Mechanics of Materials | 3-0-0-3 | ENG201 |
| 3 | ECE301 | Electrical Circuits and Electronics | 3-0-0-3 | - |
| 3 | ENG302 | Introduction to Operations Management | 3-0-0-3 | - |
| 4 | CSE401 | Software Engineering and Project Management | 2-0-2-3 | CSE301 |
| 4 | MAT401 | Operations Research | 3-0-0-3 | MAT301 |
| 4 | ENG401 | Industrial Engineering Principles | 3-0-0-3 | ENG302 |
| 4 | ECE401 | Control Systems | 3-0-0-3 | ECE301 |
| 4 | CSE402 | Artificial Intelligence and Machine Learning | 2-0-2-3 | CS201 |
| 5 | ENG501 | Supply Chain Management | 3-0-0-3 | ENG401 |
| 5 | MAT501 | Advanced Statistics and Data Analysis | 3-0-0-3 | MAT201 |
| 5 | CSE501 | Big Data Analytics | 2-0-2-3 | CSE401 |
| 5 | ENG502 | Quality Control and Assurance | 3-0-0-3 | - |
| 5 | CSE502 | Database Design and Optimization | 2-0-2-3 | CSE301 |
| 6 | ENG601 | Lean Manufacturing and Process Improvement | 3-0-0-3 | ENG501 |
| 6 | CSE601 | Cloud Computing and DevOps | 2-0-2-3 | CSE401 |
| 6 | MAT601 | Decision Making Under Uncertainty | 3-0-0-3 | MAT501 |
| 6 | ENG602 | Operations in Healthcare | 3-0-0-3 | ENG502 |
| 6 | CSE602 | Internet of Things (IoT) and Smart Systems | 2-0-2-3 | CSE402 |
| 7 | ENG701 | Sustainable Operations and Green Logistics | 3-0-0-3 | ENG601 |
| 7 | MAT701 | Stochastic Processes and Simulation | 3-0-0-3 | MAT601 |
| 7 | CSE701 | Blockchain Technologies for Business | 2-0-2-3 | CSE601 |
| 7 | ENG702 | Risk Management in Operations | 3-0-0-3 | - |
| 7 | CSE702 | Mobile Application Development | 2-0-2-3 | CSE402 |
| 8 | ENG801 | Final Year Project / Thesis | 0-0-6-6 | - |
| 8 | CSE801 | Advanced Topics in Data Science | 2-0-2-3 | CSE701 |
| 8 | MAT801 | Capstone Research Project | 0-0-6-6 | MAT701 |
Advanced Departmental Electives Overview
Departmental electives play a crucial role in allowing students to specialize and explore advanced topics within operations management. These courses are designed to complement core subjects and offer deeper insights into specific areas of interest.
Supply Chain Management
This course delves into the complexities of global supply chains, focusing on procurement strategies, supplier relationship management, logistics coordination, and demand forecasting techniques. Students gain exposure to industry tools such as SAP SCM and Oracle Supply Chain Planning, enabling them to design efficient and resilient supply chain architectures.
Lean Manufacturing and Process Improvement
Students learn the principles of lean manufacturing, including waste identification, value stream mapping, and continuous improvement methodologies. Through hands-on simulations and real-world case studies, they develop skills in process optimization and operational excellence.
Data Analytics for Operations
This elective introduces students to advanced data analysis techniques tailored for operational decision-making. Topics include predictive modeling, machine learning algorithms, data visualization tools (Tableau, Power BI), and statistical inference methods used in performance measurement and forecasting.
Quality Control and Assurance
The course explores quality assurance systems, Six Sigma methodologies, statistical process control, and total quality management principles. Students learn to implement quality improvement initiatives using tools such as control charts, process capability analysis, and design of experiments (DOE).
Operations in Healthcare
This specialized course addresses the unique operational challenges in healthcare environments. It covers topics such as patient flow optimization, resource allocation, service design, and performance metrics specific to hospitals and clinics. Real-world projects with healthcare partners provide practical experience.
Sustainable Operations and Green Logistics
Focused on environmental sustainability in operations, this course examines green supply chain practices, carbon footprint reduction, renewable energy integration, and circular economy models. Students engage in case studies of companies implementing sustainable practices and develop strategies for reducing operational impact.
Risk Management in Operations
Students are introduced to risk identification, assessment, and mitigation techniques in complex operational environments. The course covers topics such as scenario planning, business continuity management, cybersecurity risks, and regulatory compliance frameworks affecting modern operations.
Project Management
This elective provides a comprehensive overview of project management methodologies, including Agile, Scrum, Waterfall, and PRINCE2. Students learn to plan, execute, monitor, and close projects effectively, ensuring alignment with organizational objectives and stakeholder expectations.
Artificial Intelligence in Operations
Exploring the intersection of AI and operations, this course covers applications such as AI-driven forecasting, robotic process automation (RPA), chatbots for customer service, and intelligent scheduling systems. Students build models using Python libraries like scikit-learn and TensorFlow.
Digital Transformation
This advanced elective examines how digital technologies are reshaping operational landscapes. Topics include cloud computing, IoT integration, blockchain applications, and automation trends in manufacturing and services. Students work on real-world transformation projects with industry partners.
Project-Based Learning Philosophy
Project-based learning is a cornerstone of the Operations program at Prayaga Institute of Management Studies. The approach emphasizes active learning, collaboration, and problem-solving in realistic contexts.
Mini-Projects (First to Third Year)
Mini-projects are integrated into the curriculum from early semesters, allowing students to apply theoretical knowledge to practical situations. These projects typically last 4-6 weeks and involve small teams working on predefined challenges under faculty supervision. Examples include process mapping exercises, simulation modeling tasks, or simple data analysis assignments.
Final-Year Thesis/Capstone Project
The capstone project is a significant component of the final year, requiring students to undertake an independent research or applied project under the guidance of a faculty mentor. Projects can be theoretical (e.g., developing a new algorithm for scheduling problems) or practical (e.g., implementing an operational improvement in a local organization).
Project Selection Process
Students select their final-year projects based on personal interests, career goals, and faculty availability. The selection process involves submitting a proposal that outlines the problem statement, methodology, expected outcomes, and timeline. Faculty mentors are assigned based on expertise alignment and project relevance.
Evaluation Criteria
Projects are evaluated based on multiple criteria including conceptual depth, methodological rigor, technical execution, presentation quality, and impact potential. Peer reviews, faculty feedback, and industry input contribute to a holistic assessment.