Semester-wise syllabus for an M.Tech in Industrial Engineering

Semester 1: Core Foundations 

Courses: 

1. Advanced Operations Research 

   - Linear/non-linear programming, queuing theory, simulation, and decision analysis. 

2. Production and Operations Management

   - Lean manufacturing, JIT, TPM, capacity planning, and facility layout design. 

3. Quality Engineering and Management 

   - Six Sigma, SPC (Statistical Process Control), Taguchi methods, and ISO 9001 standards. 

4. Supply Chain Management 

   - Inventory optimization, logistics, demand forecasting, and blockchain in SCM. 

5. Research Methodology 

   - Technical writing, statistical tools (Minitab, R), and experimental design. 

Labs: 

- Simulation Lab (Arena, Simul8, FlexSim) 

- Quality Control Lab (Minitab, control charts, DOE) 

Semester 2: Specialization & Electives 

Core Courses: 

1. Systems Engineering and Analysis

   - System dynamics, reliability engineering, and failure mode analysis (FMEA). 

2. Human Factors and Ergonomics 

   - Workstation design, cognitive ergonomics, and safety management (OSHA standards). 

Electives (Examples): 

- Lean Six Sigma (DMAIC, Kaizen, value stream mapping) 

- Advanced Manufacturing Systems (CIM, robotics, additive manufacturing) 

- Project Management (PERT/CPM, Agile, MS Project) 

- Sustainability in Industry (life-cycle assessment, circular economy) 

- Data Analytics for Industrial Engineering (Python, SQL, Power BI) 

Labs: 

- Ergonomics Lab (Motion capture, RULA/REBA analysis) 

- Supply Chain Simulation Lab (AnyLogic, SAP ERP) 

Semester 3: Advanced Electives & Project Work

Electives (Examples):

- AI/ML in Industrial Engineering (predictive maintenance, digital twins) 

- Industry 4.0 (IoT, smart factories, cyber-physical systems) 

- Logistics and Transportation Engineering (route optimization, VRP) 

- Healthcare Systems Engineering (hospital workflow optimization) 

- Financial Engineering (cost-benefit analysis, risk modeling) 

Project/Dissertation: 

- Phase 1: Topic selection (e.g., process optimization, Industry 4.0 implementation, sustainable SCM), literature review, and proposal. 

- Seminars: Presentations on trends like AI-driven automation, cobots, or green manufacturing. 

Semester 4: Thesis/Project Completion 

Thesis/Project: 

- Full-time focus on industrial case studies, simulations, or field implementation (e.g., optimizing a production line, reducing waste in supply chains). 

- Final documentation, viva voce defense, and industry collaboration (if applicable). 

Additional Components: 

- Industrial Internship (optional, with manufacturing firms, consultancies, or logistics companies). 

- Workshops: Training in Digital Twin tools (ANSYS Twin Builder), ERP systems (SAP, Oracle), or Lean Six Sigma certification. 

Elective Tracks (Specializations): 

1. Operations Research & Analytics 

   - Optimization algorithms, simulation modeling, big data analytics. 

2. Supply Chain & Logistics 

   - Global SCM, warehouse automation, last-mile delivery. 

3. Manufacturing Systems 

   - Smart manufacturing, robotics, Industry 4.0 integration. 

4. Sustainability & Green Engineering

   - Carbon footprint reduction, circular economy, renewable energy systems.