AI for Manufacturing

AI for Manufacturing

Introduction

Understanding, Applying, and Strategically Adopting AI in Manufacturing Operations

Artificial Intelligence (AI) is becoming a core enabler of smart manufacturing, Industry 4.0, and digital transformation. Manufacturing organisations are increasingly using AI to improve production efficiency, quality control, predictive maintenance, supply chain visibility, energy optimisation, and decision-making.

The rise of Large Language Models (LLMs) and Generative AI has expanded AI adoption beyond engineers and data scientists. Today, production managers, quality teams, planners, and executives can directly use AI tools to analyse data, interpret reports, summarise machine logs, support root-cause analysis, and automate documentation.

However, AI adoption in manufacturing requires a clear understanding of AI capabilities and limitations, especially when AI is used to support safety-critical, operational, and compliance-related decisions.

This course provides a practical, business-oriented introduction to AI for manufacturing professionals. Participants will learn AI fundamentals, understand how AI technologies interconnect, and gain hands-on experience with leading AI tools such as ChatGPT, Gemini, Copilot, Perplexity, Grok, Claude, Manus, DeepSeek, Qwen, and NotebookLM. The focus is on real manufacturing use cases, responsible AI usage, and measurable productivity and operational improvement, without requiring programming skills.

Who will Benefit - This course is designed for working professionals in manufacturing and industrial environments, including:

Manufacturing managers and supervisors
Production, operations, and plant managers
Quality assurance and quality control professionals
Maintenance and reliability engineers
Supply chain and planning teams
Process engineers and industrial engineers
Digital transformation and Industry 4.0 teams
Manufacturing executives and decision-makers

Level & Delivery

Beginner to Intermediate | No prior AI or technical background is required | No coding required.
Duration - 2 days | Mode In-house | Methodology - Instructor-led | Hands-on AI tools | Laptop or Tablet required.
Min 5 pax - Max 16 | Hit Get Proposal for an in-house quote.

Outcome

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

Understand core AI concepts and terminology relevant to manufacturing
Distinguish between AI, Machine Learning, Deep Learning, LLMs, and Generative AI
Evaluate and select AI tools suitable for manufacturing workflows
Apply AI tools to production analysis, quality reporting, maintenance planning, and operations documentation
Use AI responsibly within safety, operational, and organisational constraints
Identify high-impact AI use cases within their own manufacturing environments
Contribute to AI adoption and digital transformation initiatives

Select to design your own content and request for a customized quotation

No	Topic	Topic Description
1	Understanding AI in Manufacturing	What Artificial Intelligence is and is not Relationship between: Artificial Intelligence Machine Learning Deep Learning Large Language Models Generative AI How AI systems learn from production data Current AI applications in manufacturing: predictive maintenance quality inspection and defect detection production forecasting and scheduling process optimisation and yield improvement Strengths and limitations of AI in operational environments
2	AI Tools Landscape for Manufacturing Professionals	Overview of modern AI tools for manufacturing: ChatGPT, Gemini, Copilot, Claude Perplexity, Grok, Manus, DeepSeek, Qwen, NotebookLM Understanding differences in: capabilities and reliability real-time vs historical analysis data privacy and intellectual property risks Selecting the right AI tool for: production reporting and analysis SOP creation and revision root-cause analysis support internal communication and documentation Prompt engineering fundamentals for manufacturing use cases Hands-On Activities Writing prompts for production summaries and shift reports Analysing downtime logs and defect descriptions using AI Comparing outputs across multiple AI tools
3	AI for Production Analysis and Operational Reporting	Using AI to analyse production KPIs: OEE, downtime, scrap, yield Identifying trends, bottlenecks, and anomalies AI-assisted daily, weekly, and monthly production reporting Using AI with spreadsheets: Excel with Copilot Google Sheets with Gemini Translating data into actionable insights for management Hands-On Activities Generating AI-assisted production insights Drafting executive-ready operations reports
4	Responsible AI, Safety, and Governance in Manufacturing	AI risks in manufacturing environments: incorrect recommendations affecting safety over-reliance on AI outputs data quality and sensor reliability Safety and compliance considerations Human-in-the-loop decision-making for operations Best practices for responsible AI usage in manufacturing
5	AI for Predictive Maintenance and Asset Reliability	AI concepts in predictive maintenance Analysing equipment logs and maintenance history with AI Supporting maintenance planning and spare parts decisions Limitations of AI in equipment-critical decisions Hands-On Activities Analysing maintenance scenarios using AI tools Drafting maintenance insights and recommendations
6	AI for Quality Control and Process Improvement	AI in quality inspection and defect analysis Supporting root-cause analysis with AI AI-assisted continuous improvement (Kaizen, Six Sigma support) Using AI to document quality findings and corrective actions Hands-On Activities Analysing defect reports using AI Generating corrective action summaries
7	AI-Enabled Workflow Automation in Manufacturing	AI-supported workflows: production data → analysis → reports Integrating AI into daily manufacturing operations Improving coordination between production, quality, and maintenance Measuring productivity, quality, and cost impact Exercise Designing an AI-supported manufacturing workflow
8	AI Strategy for Manufacturing Leaders and Decision-Makers	Identifying high-impact AI opportunities in manufacturing Build vs buy vs partner considerations Developing an AI adoption roadmap for manufacturing Establishing AI governance, roles, and responsibilities Preparing manufacturing teams for AI-enabled operations Workshop Drafting a high-level AI strategy for a manufacturing organisation or plant

Expert

Dr. Mohammed Muayad Abdulrazzaq

Dr. Mohammed Muayad Abdulrazzaq, fondly known as Dr. Obay is an accomplished AI Trainer, Academic Leader, TEDx Speaker, Industry Advisor, and Entrepreneur, internationally recognised for his contributions to Artificial Intelligence education, research, and innovation. Holding a Ph.D. in Artificial Intelligence and bringing over 20 years of experience across the information technology landscape, Dr. Obay’s expertise spans AI, Big Data Science, Machine Learning, Deep Learning, Generative AI, Large Language Models (LLMs), and Immersive Technologies. As a certified and HRD Corp-accredited trainer, Dr. Obay has trained more than 5,000 professionals, students, and business leaders through highly practical and results- driven programs in Data Science, AI Engineering, Generative AI, and LLMs. His workshops are widely praised for their clarity, relevance, and ability to translate complex AI concepts into real-world business impact. Recently, Dr. Obay was honoured to begin a new chapter as an Industry Advisor at ICMS, contributing to advanced programs and professional certifications in Generative AI, Large Language Models, Google AI & Machine Learning, and Microsoft Azure AI. His advisory role bridges academia and industry by empowering future-ready talent and organisations to thrive in the era of intelligent automation. Recognised for his excellence in AI education and leadership, Dr. Obay serves as an ELITE Fellow at the University of Malaya (UM) — Malaysia’s top-ranked university — and as an Adjunct Associate Professor at Taylor’s University, where he supervises Ph.D. and Master’s students at Taylor’s University and the International Islamic University Malaysia (IIUM). His academic work focuses on applying AI to solve real- world problems while shaping the next generation of innovators. An entrepreneur at heart, he is the Founder of Artificial Intelligence & Coding for All (AIC4ALL) — a global learning platform that equips learners of all ages with essential skills in AI, coding, and data science for IR 4.0 & IR 5.0. He also leads INNOEYETIVE, a digital innovation agency leveraging AI, Big Data, and Immersive Tech to accelerate digital transformation. As a TEDx and international speaker, Dr. Obay has delivered impactful talks and keynotes that demystify AI, inspire innovation, and promote ethical adoption of emerging technologies. His lifelong mission is simple yet powerful — to empower professionals, educators, and leaders worldwide to harness the transformative power of AI for a better, smarter future.