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Post Graduate Program in Smart Manufacturing with Digital Excellence

Duration: 6 Months / 24 Weeks / 110 hrs

Industry Acceptance

Smart Manufacturing are revolutionizing manufacturing, ushering in an era of unprecedented efficiency and customer responsiveness. Imagine: advanced automation, predictive maintenance, and self-optimizing processes seamlessly working together. This is the reality of smart factories, powered by the fourth industrial revolution.

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Registration Fees

₹ 1500

non-refundable

Course Fees

₹ 85,000

Flexible EMI options available.

Batch Start

Aug, 2025

Duration

6 Months / 24 Weeks / 110 hrs

Program Structure

Eligibility: A Bachelor's degree or Diploma with a focus on IT or Engineering from a recognized institution.

Duration: 6 Months / 24 Weeks / 110 hrs

Batch Start Date: Aug, 2025

Batch Size: 25-30

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Program Structure:

Module 1: Smart Manufacturing
Course Content +

This session provides an overview of:

  • Economic Benefits with Real-Time Monitoring and Insight: Leverage real-time data to optimize production, reduce waste, and increase operational efficiency, ultimately driving cost savings and profitability.
  • Use Cases of Computer Vision and Augmented Reality: Implement advanced technologies like computer vision for quality control and augmented reality for remote assistance, training, and maintenance in industrial settings.
  • Relevance of Machine Learning: Apply machine learning to predict outcomes, enhance product quality, and automate complex decision-making processes, leading to smarter and more adaptive manufacturing systems.
  • Application of Multipurpose Robots & Stages of Implementation: Deploy versatile robots across various stages of production—from assembly and inspection to packaging—while following structured implementation stages for smooth integration.
Module 2: Industrial Internet of Things
Course Content +

This session provides an overview of:

  • Underpinning Technologies that Make IIoT Possible: Core technologies like sensors, edge devices, cloud computing, 5G connectivity, and advanced data analytics that form the backbone of Industrial IoT systems.
  • Technical Issues Required to Build an IIoT Network: Considerations including latency, data security, device interoperability, network architecture, and real-time communication protocols.
  • Innovative Business Models from Smart Factory Connectivity: New opportunities such as predictive maintenance, equipment-as-a-service (EaaS), and data-driven decision-making enabled by interconnected systems.
  • Protocols, Software Patterns, and Middleware in IIoT: The use of MQTT, CoAP, OPC-UA, REST APIs, and middleware platforms to ensure smooth data communication and integration between heterogeneous systems.
  • Deployment and Implementation of Use Cases: Practical applications of IIoT in sectors like manufacturing, logistics, energy, and agriculture — including steps for successful planning, deployment, and scaling.
Module 3: Machine Learning
Course Content +

Manufacturers and Machine Builders are including Machine Learning in key areas of the business operations, including production and post-production.

This session will cover:

  • Fundamentals of Machine Learning and Data Analytics: Understand core concepts such as supervised and unsupervised learning, model training, evaluation, data preprocessing, and analytics pipelines.
  • Value from Machine Learning Implementation: Learn how machine learning drives value by improving decision-making, optimizing processes, reducing waste, and enabling automation in manufacturing environments.
  • Use Cases of Machine Learning in Manufacturing: Explore real-world applications such as demand forecasting, quality inspection, defect detection, process optimization, and supply chain analytics.
  • Predictive Maintenance and Predictive Quality Analytics: Discover how data from sensors and systems can be used to predict equipment failures and maintain consistent product quality, reducing downtime and enhancing efficiency.
Module 4: Augmented Reality
Course Content +

Augmented reality (AR) has emerged as a powerful technology to bridge the gap between the digital and real worlds for assemblers, operators, and technicians.

This session provides an overview of:

  • Basics of Implementation of Augmented Reality: Understand the fundamental concepts, tools, and platforms used to develop AR applications in manufacturing environments.
  • Use Cases of Augmented Reality in Manufacturing: Explore real-world examples such as remote assistance, assembly guidance, training simulations, and equipment maintenance through AR.
  • Integrating Augmented Reality with Industrial IoT: Learn how AR can be combined with IIoT to visualize real-time data from machines and sensors, enhancing operational insight and decision-making.
  • Step-by-Step Demo of Creating an Augmented Reality Experience: Follow a guided demonstration to build a basic AR experience using industry-standard tools and platforms.
Module 5: Digital Twin
Course Content +

Modelling plays a key role in managing the increasing complexity of technological systems. Digital twin provides the possibility to interact with the real representation of the machine. The possibility to combine the real data with the simulation models provides a new range of applications with clear benefits. Digital twins enable you to optimize, improve efficiency, automate, and evaluate future performance.

This session will cover:

  • Role of Digital Twin in Business Systems: Understand how Digital Twins enable real-time monitoring, simulation, and optimization of business operations for increased efficiency and informed decision-making.
  • How Digital Twin Works: Learn the core components of a Digital Twin system, including data acquisition, simulation models, and feedback mechanisms that mirror physical assets in the digital world.
  • Digital Twin Implementation Framework: Explore the step-by-step process to design, develop, and deploy a Digital Twin—from integration with sensors and IoT devices to software platforms and analytics.
  • Data-Driven and Physics-Based Modeling: Discover the two main approaches to building Digital Twins—using historical and real-time data (data-driven) or applying engineering principles (physics-based).
  • Use Case: Machine Tool Digital Twin: Analyze a real-world example of implementing a Digital Twin for machine tools to improve predictive maintenance, reduce downtime, and enhance performance.

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