AI for Lean Manufacturing

Dr. Alfred Ang came to artificial intelligence from the industrial side of technology. Before founding Tertiary Courses, he worked in the defence, electronics and semiconductor industries, published more than 20 peer-reviewed papers and co-invented more than 20 US patents, grounding his teaching in how real production environments run. He holds a PhD in Electrical Engineering from NUS, an MSc in Electrical and Electronic Engineering from NTU, a B.Sc (Hons) in Physics from NUS and a Graduate Diploma in Intelligent Robotics Systems from NUS-ISS. Today he is a full-stack AI developer and Agentic AI solution architect, and serves as Managing Director, CIO, CTO and Chief Instructional Designer of Tertiary Infotech Academy Pte Ltd. He is a Microsoft Certified Data Scientist (DP-100), AI Engineer Associate (AI-102) and Power BI Data Analyst (PL-300), as well as a certified PMP. His public GitHub portfolio of more than 300 repositories includes projects that map closely to Lean practice: sipoc, a guided SIPOC and process map builder for Lean Six Sigma Yellow Belt that generates swimlanes and handoff tables; n8n-inventorycontrol, an AI inventory audit and forecasting agent in which a local LLM flags items under reorder thresholds and computes replenishment; kacificlogistics, a warehouse inventory dashboard with derived stock status; and an ERP prototype with human-in-the-loop purchase-order approvals and low-stock replenishment. He also founded Tertiary Robotics in 2015, reflecting a long interest in hardware, sensors and automation, and as an ACTA-certified trainer and DACE-certified curriculum developer he turns technical ideas into practical workplace learning.

In AI for Lean Manufacturing, Dr. Ang connects those builds to the course's four topics. Learners start with AI-assisted Lean fundamentals, using AI to support value stream analysis and to identify the classic forms of waste in a process. They then apply AI to inventory classification and Kanban optimisation, for example grouping items by value and usage and tuning reorder points, before moving to AI-enabled control of production, inventory and material flow. The final topic covers Lean implementation, performance tracking and preventive maintenance, where learners see how operational data and AI analysis can flag issues before they cause downtime. His style is practical rather than theoretical: he demonstrates how an AI agent can read stock data, raise exceptions and draft reports, then has learners apply the same thinking to their own shop-floor problems using accessible tools. Drawing on his data science and Power BI background, he stresses clean data, sensible metrics and keeping people in the improvement loop, so that AI supports continuous improvement rather than replacing the judgement of operators and engineers. Learners leave with a clear view of where AI adds value in their own Lean journey and a short list of improvements to try first. Discussions draw on examples from electronics and semiconductor environments, where small process gains matter, and learners are encouraged to bring their own process data or scenarios to the exercises. To explore his process improvement and automation projects, learners can view his GitHub portfolio at https://github.com/alfredang and connect with him on LinkedIn at https://www.linkedin.com/in/angchewhoe/