How to Improve Overall Equipment Effectiveness (OEE)

[Course Introduction] Breaking the 60% OEE Barrier: Designing an AI-Powered 'Predictable Factory' "Is your factory stalled, or is it predicting?" Many manufacturing sites are working 'hard,' yet they fail to cross the 85% threshold of World Class OEE, the global standard. Equipment breaks down without warning (Breakdowns), data is disconnected somewhere between the ERP and the shop floor (MES) (Data Silo), and the grand slogans of the 4th Industrial Revolution feel detached from the actual problems on the ground. This course is not a theoretical class for simply memorizing OEE formulas. It is a practical engineering masterclass that finds answers to the question, "Why isn't our factory's efficiency improving?" through data and standards. 1. The Pain Point - Invisible Waste: Frequent 'Minor Stops' of less than 10 minutes and 'Reduced Speed' are eating away at productivity, but the causes cannot be found through manual records. - Reactive Response: Because of a 'Reactive' maintenance approach—repairing equipment only after it breaks—emergency shutdowns repeat and costs snowball. - Data Disconnection: Management looks at financial metrics in the ERP while the shop floor looks at machine sensor data, but the lack of a connecting standard (ISA-95) delays decision-making. 2. The Solution I provide you with a clear roadmap to transform 'Uncertainty' into 'Predictability.' - Step 1. Visualizing the 6 Big Losses: We will dissect the 16 loss structures that hinder OEE to reveal the 'Hidden Factory' through data. - Step 2. Powerful Toolbox: I will share protocols for TPM to achieve zero breakdowns, SMED to drastically reduce changeover time, and Gemba Walks to find the truth on the shop floor. - Step 3. Combining AI with Global Standards: Learn how to work with data rather than 'gut feelings.' We will apply future technologies to prevent failures in advance by integrating systems via the ISA-95 standard and utilizing AI-based Predictive Maintenance (PdM) and Digital Twins (ISO 23247). 3. Why Me? (Expertise and Experience) I have experienced both the sweat of traditional manufacturing sites and the data flows of cutting-edge smart factories. - Field-Oriented Improvement: I am not just a theorist. I cover only field-proven methodologies, ranging from Autonomous Maintenance (tightening bolts on equipment) to SPC (Statistical Process Control) to maximize process capability. - Global Standards Leadership: I interpret ISA-95 system integration and ISO/IEC 42001 (AI Management System) standards—required by global automakers and advanced manufacturing companies—so they can be applied immediately to your work. - Future-Oriented Insight: I guide sustainable manufacturing strategies that go beyond simple productivity to consider energy efficiency (OEEE) and carbon neutrality. 4. The Outcome After completing this course, you will no longer feel anxious about when your equipment might stop. - Data-Driven Decision Making: Instead of saying "The machine seems strange," you will say, "Based on vibration data pattern analysis, a bearing failure is predicted within three days." - Financial Results: You will demonstrate 'Cost Avoidance' effects by increasing production capacity (CAPA) through waste elimination without additional facility investment. Re-engineer your factory now. I will be with you on that journey.