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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.

3 learners are taking this course

Level Basic

Course period Unlimited

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What you will gain after the course

  • ① Standardized OEE Loss Analysis Sheet • Content: A data sheet that records the causes of equipment downtime based on **standardized error codes**, excluding subjective manual entries by operators. • Result: Instead of vague records like 'motor failure,' it completes the logic for mapping the six big losses into a database-compatible format, such as M-01 (Motor Failure - Overheat).

  • ② 90-Day Execution Roadmap (90-Day Execution Plan) • Content: This is a concrete action plan that moves beyond vague innovation, following a sequence of **[Day 30: Foundation Building] → [Day 60: Pilot Application] → [Day 61~90: Expansion]**. • Result: You will obtain a step-by-step execution schedule ranging from establishing Gemba Walk routines to identifying bottlenecks and implementing TPM pilots.

  • ③ ROI and Financial Impact Report • Content: Financial evidence to persuade management. It numerically demonstrates how a 10% improvement in OEE leads to cost avoidance in capital expenditure (CapEx). • Result: Instead of a vague slogan like "Let's increase productivity," you can produce a quantitative report stating, **"By reducing setup time by 50%, we will lower inventory carrying costs and increase production volume by 40% without additional equipment investment."**

  • ④ ISA-95 Based Data Integration Architecture Diagram • Content: A system blueprint showing the flow of data from field sensors (Level 1) to MES (Level 3) and ERP (Level 4). • Result: Enables the elimination of data silos and the design of an interface model that synchronizes ERP production plans with real-time field performance data.

  • Students will acquire the following problem-solving skills to ask **"Why?"** and answer with **"Data."** ① **Diagnostic Capability to uncover the 'Hidden Factory'** • Beyond simply observing equipment breakdowns, students will use data to identify how invisible **small stops (under 10 minutes) and reduced speeds** erode overall efficiency. • They will be able to distinguish between chronic losses and sporadic losses, prescribing the correct resolution tools (TPM vs. SMED) accordingly. ② **Systemic Thinking and Standardization & Governance** • Understand global standards such as ISO/IEC 42001 (AI Management System) and ISO 23247 (Digital Twin), and evaluate whether factory systems comply with these international requirements. • Instead of relying on individual experience, students will develop SPC (Statistical Process Control) skills to control process variability through Standard Operating Procedures (SOP) and Control Charts. ③ **Digital & AI Literacy** • Move beyond the 'descriptive' stage of analyzing past data to gain a technical vision that predicts failures through **AI Predictive Maintenance (PdM)** and prescribes solutions for root causes through **Causal AI**. • Go beyond traditional physical setups (SMED) to understand SMED v2 capabilities, which derive optimal work movements through simulations on a Digital Twin. ④ **Gemba Leadership** • To bridge the gap between office KPI charts and the physical reality of the shop floor, students will exercise leadership by performing the 8-step Gemba Walk protocol—identifying process defects rather than blaming workers. In conclusion, students will become **"Architects who transform uncertain manufacturing sites into predictable, data-driven factories."**

"Control uncertain manufacturing sites with 'data' and 'standards,' and design a 'predictable future' with AI."

This course is not merely a theoretical class on how to calculate Overall Equipment Effectiveness (OEE). It is a practical engineering course covering ‘A to Z of Manufacturing Operational Excellence’, ranging from the core methodologies of traditional manufacturing engineering such as TPM, SMED, and Lean, to essential elements of modern manufacturing like ISA-95 system integration and ISO standards, and AI-based Predictive Maintenance (PdM).

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1. Background of Course Planning: Why this course now?

Even in the era of the Fourth Industrial Revolution, many factories still rely on ‘Gut Feeling’ or remain trapped in ‘Data Silos.’ While management looks at financial metrics on the ERP, field engineers manually record data in Excel, leading them to speak different languages.

This course is designed to overcome these disconnects on the shop floor. Centered on the global standard metric of OEE, we uncover waste within the 'Hidden Factory', present a specific Engineering Toolbox to address it, and ultimately provide a roadmap toward Autonomous Operations through AI and Digital Twins.

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2. Detailed Curriculum by Section (Deep Dive)

This course consists of six core modules logically connected from basic to advanced levels.

SECTION 1. Mastering Manufacturing Efficiency (Foundation of OEE)

Redefining OEE: Learn how to use OEE (Overall Equipment Effectiveness) not just as a simple productivity metric, but as a "stethoscope" to diagnose manufacturing competitiveness. Quantify the gap between your current level and Perfect Production through the three key elements: Availability, Performance, and Quality.

Global Standard Goals: Understand why global manufacturing companies aim for 85% World Class OEE and establish the fundamental principles for achieving this in a Discrete Manufacturing environment.

SECTION 2. In-depth Analysis of the Six Big Losses

Visualization of Hidden Waste: Uncover the specific enemies hindering OEE, namely the "Six Big Losses." We identify how not only equipment breakdowns but also small stops (under 10 minutes) and reduced speed, which are difficult to capture with data, erode overall efficiency.

Root Cause Analysis (RCA): Develop analytical skills to eliminate the root of a problem rather than just surface-level symptoms by utilizing Fishbone diagrams and the 5 Whys technique from the 4M (Man, Machine, Material, Method) perspective.

SECTION 3. Productivity Innovation Tools (Operational Excellence Toolbox)

TPM (Total Productive Maintenance): Shift from reactive maintenance to preventive/predictive maintenance. Establish an Autonomous Maintenance system where operators manage their own equipment to prevent breakdowns in advance.

SMED (Single-Minute Exchange of Die): We teach techniques to reduce setup times to under 10 minutes by converting "internal setup" time (when equipment is stopped) into "external setup" tasks that can be performed while the machine is running. This ensures agility for high-mix, low-volume production.

Gemba Walk: Learn the 8-step field observation protocol to bridge the gap between desk-bound data and the Ground Truth of the shop floor.

SECTION 4. Data-Driven Execution Strategy

Reliability Engineering Metrics: Establish specific strategies to increase MTBF (Mean Time Between Failures) and decrease MTTR (Mean Time To Repair). In particular, we reveal the know-how for reducing 'Diagnose' time, which accounts for 50% of repair time.

SPC & Process Capability: Establish a quality assurance system that controls process variation and prevents defects before products are made by utilizing Statistical Process Control (SPC) and Cp/Cpk indices.

Digital Data Architecture: Moving beyond the limitations of manual records (Level 1), you will learn architecture for automated data collection from sensors and PLCs (Level 3) and standard data sheet design methods.

SECTION 5. Manufacturing Standardization Competitiveness (Standardization & ISA-95)

ISA-95 System Integration: Learn the ISA-95 (IEC 62264) standard, which acts as a 'data interpreter' between ERP (Management) and MES (Manufacturing). This enables the elimination of data silos and the design of an enterprise-wide data integration model (B2MML).

MES Advancement: Establish strategies for building next-generation MES that enables real-time control and predictive analysis, moving beyond simple record-keeping MES.

SECTION 6. AI, Digital Twin, and Governance (Future Readiness)

AI & Digital Twin: We cover cutting-edge technologies that virtualize physical assets through ISO 23247 standard-based digital twins and utilize Causal AI to provide prescriptive analysis for the causes of failures.

OEEE (Integrated Energy Efficiency): We present a sustainable manufacturing strategy that simultaneously achieves carbon neutrality and cost reduction through the OEEE metric, which combines productivity (OEE) with energy efficiency.

AI Governance: Establish a management system to manage risks and ensure the reliability of AI systems in response to ISO/IEC 42001 and the EU AI Act.

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3. Who this course is for (Target Audience)

Production and Manufacturing Engineers: Practitioners who want to improve equipment efficiency but feel frustrated by the lack of clear data analysis methods and improvement tools.

Equipment Maintenance Personnel: Those who want to move beyond the role of a "firefighter" who only repairs equipment after a breakdown and grow into a data-driven Predictive Maintenance (PdM) expert.

DX/Smart Factory Planner: Leaders who want to integrate ERP, MES, and IoT systems but are struggling with global standards (ISA-95) and architecture design.

Quality Control (QA/QC) Professional: A manager who wants to prevent defects at the source through Statistical Process Control (SPC) rather than relying on post-inspections.

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4. Outcomes After Taking the Course

1. Unified Language: From machine data on the shop floor to financial reports for management, you can communicate and make decisions using standardized indicators (KPIs).

2. Cost Avoidance: Achieve financial results by increasing production capacity (CAPA) and reducing inventory costs solely through OEE improvement, without additional capital expenditure.

3. Predictable Factory: Achieve a fundamental transformation from a "factory where you never know when a breakdown will occur" to a "factory that predicts and controls breakdowns."

Are you ready to elevate your career and your factory to a ‘World Class’ level? This course will serve as your definitive guide.

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Who is this course right for?

  • 1. "The report says 90% efficiency, so why are we missing the deadlines?" (The Paradox of the Hidden Factory) The most frustrated person is the production manager suffering from the gap between the OEE (Overall Equipment Effectiveness) figures reported to management and the actual output on the shop floor. • Situation: While the manual logs written by workers only record 'equipment breakdowns,' in reality, frequent 'minor stops' of less than 10 minutes and 'reduced speed' are eating away at productivity. • Pain Point: Because data collection is not automated, the waste of the so-called **'Hidden Factory'** remains invisible. Consequently, they are stuck in a vicious cycle of relying on overtime and holiday shifts to make up for production shortfalls without ever knowing the root cause.

  • 2. "Fix it when it breaks? I'll just end up getting called out again at 2 AM." (The Trap of Reactive Maintenance) This is the maintenance manager who has to rush in every time a machine stops because there is no systematic maintenance strategy in place. • Situation: Without a Predictive Maintenance (PdM) system to identify early signs of failure, they rely entirely on 'Reactive Maintenance'—repairing equipment only after it has already broken down. • Pain Point: Unexpected **Unplanned Downtime** drops productivity to zero and wastes the budget on emergency repair costs and part replacements. For them, the factory is like an 'unpredictable time bomb.'

  • 3. "We bought an expensive ERP, but you're telling us to enter floor data into Excel?" (Data Silo) I am a DX (Digital Transformation) engineer suffering from the disconnect between management systems (ERP) and the manufacturing floor (MES). • Situation: Headquarters looks at ERP (Level 4) screens, while the shop floor looks at data directly from the machines (Level 1). Without a standard (ISA-95) to bridge the two, we are trapped in **'Data Silos'** where data is mutually incompatible. • Pain Point: Despite spending hundreds of millions of won on smart factory solutions, people are still manually transferring data into Excel. Data reliability is plummeting, and decision-making is being delayed.

  • 4. "We introduced AI and robots, but we aren't seeing any ROI." (Investment without direction) This refers to innovation leaders who are failing to prove results after adopting technology without a clear definition of the problem. • Situation: They attempted automation without fundamental waste elimination (Lean) within a 'Closed MITT' culture (a closed culture that hides problems). • Pain Point: By trying to apply advanced technology without first establishing basic equipment stabilization (TPM) and Single-Minute Exchange of Die (SMED), costs have increased while actual productivity remains stagnant. Furthermore, they feel anxious about regulatory compliance because they are unprepared for AI risk management (ISO 42001). In conclusion, without this lecture, they will continue to operate factories based on 'gut feeling' instead of 'data,' 'repair' instead of 'prevention,' and 'improvisation' instead of 'standards,' never escaping the endless cycle of 'firefighting.'

Need to know before starting?

  • 1. Basic Statistical Knowledge (Mean, Standard Deviation, Distribution): Statistical thinking is required to digest the Quality Management and Data Analysis sections. • Basic statistical knowledge of normal distribution, mean, and variation is essential for SPC (Statistical Process Control), interpreting X-bar R Charts, and calculating Process Capability Indices (Cp, Cpk) covered in the lectures. • It is also helpful for understanding the difference between Correlation and Causation discussed in the AI section.

  • 2. Basic Understanding of Manufacturing Site Operation Processes

  • 3. Knowledge of Factory Automation (OT) hierarchy

  • 4. Production Information Systems (IT) and Database Fundamentals

  • 5. Excel Proficiency and Digital Mindset

Hello
This is

40여 년간의 국내 대기업 및 중견기업 근무 경력의(1984.1~2024.5) 은퇴자입니다. 

재직기간 40년 중 18년은 중역으로 근무한 파워트레인 및 동력 추진계 기술자이면서, 마지막 5년은 중견기업에서 부사장과 대표이사를 역임하였습니다. 

현대자동차 그룹에서는 해외 기술 이전 수익을(약 1,300억 상당, 중형 가솔린 엔진, 

터보차져, AWD 등)달성하였습니다. 다수의 정부투자 R&D 과제를 수행한 

이력이 있습니다. 현재는 경력 기간 중의 확보 된 지식과 경험을 공유를 목적으로 저술 활동을  시작하였습니다. 독자 여러분의 많은 관심과 격려를 부탁드립니다. 

  • 네이버 블로그 명 : 지식 공유 Hub : 기업 혁신경영의 본질과 R&D 핵심과제  

                                 (http://blog.naver.com/khjyhy100)

  • 교육 훈련 : KAIST 인공지능 경영자 과정 수료(25.2~25.6)

  • 경력 : 현대차 그룹 R&D (현대자동차(주), 현대위아(주) : 1984~2018   

          인지컨트롤스(주): 2019~2024 

  • 수상 경력 : 한국의 100대 기술과 주역 (2010.12.) (한국공학한림원, 산업자원부)

                  장영실상의 대통령상 수상 (중형 가솔린엔진 개발,산업자원부, 2005년)

  • 자동차 공학 분야의 파워트레인 및 동력추진계의 국내외 전문 기술학회 논문 13편

  • 직무발명 특허 다수 출원 및 공개

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7 lectures ∙ (54min)

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