- Home
- Academics
- School of Advanced Engineering
- B.Tech. (Engineering)
- Smart Manufacturing and Industrial Intelligence
B.Tech in Smart Manufacturing & Industrial Intelligence
Program details
The B.Tech in Smart Manufacturing & Industrial Intelligence is built on the philosophy of ‘Atoms to Bits’—where every physical manufacturing activity (machines, materials, shopfloors and supply chains) is paired with a digital intelligence layer that can sense, analyse and improve performance continuously. Over 4 years (8 semesters) and 160 credits, the program develops digitally native manufacturing engineers who don’t just operate automated systems but design the “brain” behind modern factories—using data, AI and simulation to make production smarter, safer and more sustainable.
Students build a strong base in manufacturing systems and processes, then progress into automation, robotics and controls, supported by Industrial AI and analytics for real-time decision-making. A defining emphasis is on digital twins and simulation, enabling production lines to be modelled and optimised virtually before changes are made on the shopfloor. The curriculum also covers IIoT, edge and cloud manufacturing for connected operations and predictive maintenance, along with operations strategy, supply chain thinking and sustainability. By graduation, learners are ready for Industry 4.0/5.0 roles where manufacturing becomes intelligent, adaptive and human-centric.
Program Highlights
- The ₹25 crore Bajaj Engineering Skills Training (BEST) hub provides 6-months immersion and hands-on work with AMRs (Autonomous Moving Robots), Cobots, and MES (Manufacturing Execution Systems) platforms, supported by industry-recognised certifications that strengthen employability.
- 50/50 Rule - 50% of the contact hours are dedicated to studio/lab-based learning.
- Students build Digital Twins before working on physical production lines.
- Students use NVIDIA Omniverse and Siemens Tecnomatix to build a virtual replica of a factory before working on a real factory floor.
- AI-Native manufacturing curriculum enabling real-world applications.
- Students are evaluated through a digital hardware portfolio (CAD designs, simulations, code) and a factory hackathon, where they restore a broken production line using AI and mechatronics in a real-world challenge.
Industry Trends & Career Opportunities
This program aligns with the future of manufacturing: smart and autonomous factories, AI-driven inspection and predictive maintenance, digital twins as day-to-day operational tools, IIoT with edge AI, sustainable/net-zero manufacturing, manufacturing analytics platforms, and industrial cybersecurity. Graduates won’t just 'run machines'—they’ll architect the future factory by combining manufacturing understanding with AI-led decision systems.
Typical roles include:
- Smart Manufacturing / Industrial Intelligence Engineer (smart factory systems, process optimisation, digital operations)
- Industrial AI / Manufacturing Data Engineer (AI models for quality, predictive maintenance, production analytics)
- Automation & Controls Engineer (controls, robotics integration, cobots, intelligent automation)
- Digital Twin & Simulation Engineer (virtual commissioning, scenario modelling, throughput and reliability improvement)
- Operations / Supply Chain Analytics Specialist (planning, optimisation, sustainability-linked operations)
Placements
Hiring opportunities typically come from manufacturing and automotive majors, industrial automation leaders, and Industry 4.0/5.0 startups—alongside consulting and analytics firms supporting smart factory transformation. Depending on roles and hiring cycles, this can include names such as AMNS, Tata, Maruti, Tata Motors, Mercedes-Benz, Micron, Siemens, GE Aerospace, Rockwell Automation, Schneider Electric, Amazon and other Industry 4.0 recruiters.
Students stand out through factory-ready portfolios, real industrial projects, simulation and digital twin capability, and exposure to industrial-grade automation and analytics systems via the BEST ecosystem. There is also a clear build-and-launch track—so final projects can evolve into practical outcomes such as manufacturing analytics tools, predictive maintenance solutions, energy optimisation systems, or robotics integration ventures.
Fee Structure
Click here for detailed Fee Structure.
Curriculum
Year 1: The Digital-Physical Foundation
| Sem | Course Title | L | T | P | Credits |
|---|---|---|---|---|---|
| I | Linear Algebra & Calculus for Engineers | 3 | 1 | 0 | 4 |
| Engineering Physics (Semiconductor & Sensors) | 3 | 0 | 2 | 4 | |
| Problem Solving using Python (Industrial Logic) | 2 | 0 | 4 | 4 | |
| Engineering Visualisation & 3D Modeling (CAD) | 1 | 0 | 4 | 3 | |
| English for Professional Life | 2 | 0 | 0 | 2 | |
| II | Differential Equations & Probability | 3 | 1 | 0 | 4 |
| Engineering Chemistry (Materials Science) | 3 | 0 | 2 | 4 | |
| Basic Electrical & Electronics Engineering | 3 | 0 | 2 | 4 | |
| Data Structures & Algorithms | 3 | 0 | 2 | 4 | |
| Design Thinking & Innovation | 1 | 0 | 2 | 2 |
Year 2: Mechanics & Connectivity
| Sem | Course Title | L | T | P | Credits |
|---|---|---|---|---|---|
| III | Kinematics & Dynamics of Smart Mechanisms | 3 | 1 | 0 | 4 |
| Manufacturing Processes – I (Subtractive) | 3 | 0 | 2 | 4 | |
| Industrial IoT & Sensor Integration | 2 | 0 | 2 | 3 | |
| Microcontrollers & PLC Programming | 2 | 0 | 4 | 4 | |
| Economics for Engineers | 3 | 0 | 0 | 3 | |
| IV | Manufacturing Processes – II (Additive / 3D) | 3 | 0 | 2 | 4 |
| Mechatronics & Control Systems | 3 | 0 | 2 | 4 | |
| Introduction to Industrial AI & ML | 3 | 0 | 2 | 4 | |
| Database Management & Cloud for Manufacturing | 2 | 0 | 2 | 3 | |
| Universal Human Values (AICTE Mandatory) | 3 | 0 | 0 | 3 |
Year 3: Industrial Intelligence & Autonomy .
| Sem | Course Title | L | T | P | Credits |
|---|---|---|---|---|---|
| V | Robotics & Autonomous Systems | 3 | 0 | 2 | 4 |
| Digital Twins & Cyber-Physical Systems | 3 | 0 | 2 | 4 | |
| Computer Integrated Manufacturing (CIM) | 3 | 0 | 0 | 3 | |
| Professional Elective – I (Basket A/B/C) | 3 | 0 | 0 | 3 | |
| Open Elective – I | 3 | 0 | 0 | 3 | |
| Industrial Summer Internship (4–6 weeks) | – | – | – | 2 | |
| VI | Industrial Big Data Analytics | 3 | 0 | 2 | 4 |
| Industrial Cybersecurity | 3 | 0 | 0 | 3 | |
| Professional Elective – II (Basket A/B/C) | 3 | 0 | 0 | 3 | |
| Professional Elective – III (Basket A/B/C) | 3 | 0 | 0 | 3 | |
| Open Elective – II | 3 | 0 | 0 | 3 | |
| Minor Project (Smart System Prototype) | 0 | 0 | 4 | 2 |
Year 4: Specialisation & Deployment
| Sem | Course Title | L | T | P | Credits |
|---|---|---|---|---|---|
| VII | Generative Design & Optimisation | 3 | 0 | 2 | 4 |
| Lean-Agile Project Management | 3 | 0 | 0 | 3 | |
| Professional Elective – IV | 3 | 0 | 0 | 3 | |
| Professional Elective – V | 3 | 0 | 0 | 3 | |
| Major Project Phase – I (Capstone) | 0 | 0 | 8 | 4 | |
| VIII | Industry Immersion / Major Project Phase-II | – | – | – | 12 |
Elective Baskets (AICTE-Compliant)
Students can specialize in one of the following tracks from Semester V onwards.
Basket A: Robotics & Autonomous Systems
Focused on the "Motion" of the factory.
- Collaborative Robotics (Cobotics): Programming robots to work alongside humans.
- Computer Vision for Robotics: SLAM and object recognition for factory navigation.
- Swarm Intelligence in Logistics: Managing fleets of AMRs (Autonomous Mobile Robots).
Basket B: Industrial Data & AI
Focused on the "Brain" of the factory.
- Deep Learning for Quality Control: Automated optical inspection (AOI).
- Predictive Maintenance & PHM: Prognostics and Health Management using time-series data.
- Industrial Edge Computing: Deploying AI models directly on factory-floor hardware.
Basket C: Digital Manufacturing & Enterprise
Focused on the "Business & Systems" of the factory.
- Blockchain in Supply Chain: Transparent tracking of parts and logistics.
- Augmented/Virtual Reality (XR): For remote maintenance and worker training.
- Sustainable & Green Manufacturing: Energy-aware scheduling and circular economy models.
Eligibility
The minimum eligibility criteria for this program includes - Minimum 60–70% aggregate in Class XII with Physics and Mathematics, along with one additional subject from Chemistry / Computer Science / Electronics, and a strong interest in engineering and industry.
Selection Criteria
The selection is based on the candidate’s performance in JEE Main / CUET (UG) / UPESEAT or academic merit, along with an aptitude assessment focused on systems thinking and problem-solving, followed by an optional personal interview for high-potential candidates.
