AI for Manufacturing Industry Leaders

Module Overview

This module equips manufacturing industry leaders with practical, hands-on knowledge of Artificial Intelligence and Machine Learning applications in manufacturing. It empowers leaders to understand, evaluate, and deploy AI for process optimization, predictive maintenance, quality control, supply chain forecasting, and Industry 4.0 transformation.

Learning Outcomes

By the end of this module, participants will be able to:

  • Understand key AI/ML concepts relevant to manufacturing.
  • Evaluate AI use cases such as predictive maintenance, process optimization, defect detection, and energy management.
  • Apply AI tools for data-driven decision-making in operations, quality, and supply chains.
  • Lead and manage AI deployment projects within manufacturing settings.
  • Understand ethical, cybersecurity, and workforce implications of AI integration.

Module Structure

Duration: 4 weekends (16 contact hours) + 20 hours self-study, projects, and assessments.

Syllabus Breakdown

Week 1: Foundations of AI and Machine Learning in Manufacturing (4 hours)

  • Introduction to AI, ML, and Industry 4.0 in manufacturing.
  • Key concepts: supervised, unsupervised, reinforcement learning.
  • Understanding data pipelines in manufacturing environments.
  • Case Study: AI-led defect detection on production lines.

Week 2: AI Applications in Manufacturing Operations (4 hours)

  • Predictive Maintenance using ML.
  • Process optimization using AI.
  • Quality control using computer vision.
  • Case Study: Predictive maintenance in chemical manufacturing plants.

Practical:

  • Exploring sample datasets and building a simple predictive maintenance model using IBM Watson/AutoML tools.

Week 3: AI for Supply Chain and Energy Management (4 hours)

  • Demand forecasting using AI.
  • AI in inventory optimization and logistics.
  • Energy efficiency and sustainability through AI.
  • Case Study: AI-powered supply chain optimization in a mid-sized manufacturing unit.

Practical:

  • Simple supply chain forecasting using AI on a shared demo environment.

Week 4: Managing AI Projects and Ethical Considerations (4 hours)

  • Building an AI transformation roadmap for manufacturing.
  • Understanding AI project lifecycles.
  • Managing change and workforce readiness for AI deployment.
  • Ethical, cybersecurity, and compliance considerations in manufacturing AI.

Capstone Exercise:

  • Prepare a one-page AI deployment roadmap for your organization’s manufacturing unit.

Assessment Structure

Component Weightage
Participation & Discussions 20%
Practical Exercises 30%
Capstone Project 50%

Participation & Discussions (20%)
Active participation in case discussions and weekend sessions.

Practical Exercises (30%)
Submission of predictive maintenance and supply chain forecasting exercises.

Capstone Project (50%)
A practical AI deployment roadmap specific to the participant’s manufacturing context, evaluated on feasibility, clarity, and alignment with business objectives.

Pedagogy

  • Interactive live weekend sessions (case discussions, group activities).
  • IBM-supported AI Labs (hands-on demos).
  • Real-world case studies from global chemical, pharmaceutical, and engineering manufacturing sectors.
  • Peer-to-peer learning and structured feedback from mentors.

Faculty

Delivered by:

  • Industry mentors from IBM and global manufacturing firms.
  • Faculty with AI and manufacturing expertise from IIT Bombay, MIT, and NTU Singapore.

Resources Provided

  • Access to curated AI in manufacturing resources.
  • Sample datasets for practical exercises.
  • Optional IBM certification in AI Applications in Manufacturing (at additional cost).

Certification

On successful completion, learners will receive:
“AI for Manufacturing Industry Leaders” module completion certificate with 3 ECTS credits under Mentogram’s Global TEMBA program, accredited under European Standards.

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