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Utilize AI

AI-Driven Practical Application Strategies in Manufacturing: Automotive/Parts Industry

The automotive and parts manufacturing industry is a field where innovation is essential amid fierce global competition and complex supply chains. This course focuses on how AI technology can be applied in the field, explaining actual cases including production efficiency improvement, quality inspection automation, equipment predictive maintenance, and supply chain optimization. From the foundational understanding needed for AI implementation to strategies and risk management considerations for field application, this course covers topics step by step, providing knowledge and insights that practitioners and managers can immediately utilize.

5 learners are taking this course

  • 88888
AI 활용법
사묌읞터넷
제조ai
품질ai
데읎터분석
Machine Learning(ML)
Deep Learning(DL)
IoT
AI
Generative AI

What you will learn!

  • AI Necessity and Background

  • Production/Quality Optimization

  • Forecasting and Supply Chain

  • Future Technology Integration

  • Performance & Case Analysis Flow

"Practical Strategies for Innovating Automotive Manufacturing with AI"

  • Principles of AI Application in Manufacturing

    • Understand the trends and necessity of AI adoption in the automotive/parts industry.

    • Learn how key technologies such as machine learning, deep learning, and computer vision are used to solve real-world problems.

  • Production Process Optimization

    • Learn how to analyze production flow and improve bottleneck sections by utilizing sensor and IoT data.

    • Understand strategies to improve production efficiency through AI-based simulation.

  • Quality Inspection Automation

    • Learn about defect detection using computer vision technology and the differences from traditional inspection methods.

    • Learn cases of reducing defect rates and increasing inspection speed.

  • Equipment Predictive Maintenance

    • Learn how to predict failure signs based on equipment sensor data.

    • Understand strategies to reduce unnecessary downtime and cut maintenance costs.

  • Supply Chain and Operations Management

    • Learn how AI is utilized in demand forecasting, inventory management, and parts supply optimization.

    • Learn AI adoption strategies that consider return on investment (ROI) and risk management.

I recommend this for people like this

Automotive and Parts Manufacturing Workers

Those who want to apply AI to production, facilities, etc. in the field

Manufacturing Digital Transformation (DX) Manager

Managers interested in AI-based automation and data analysis

B2B/Corporate Training Target Audience

Manufacturing employees, executives, leaders

After taking the course

  1. Understanding the Application of AI Technology in Manufacturing

    • You can specifically understand how AI is utilized in automotive and parts manufacturing sites, including key technologies and cases.

  2. Acquiring Strategies for Productivity and Quality Improvement

    • You will gain practical knowledge that can be directly applied to your work, including AI-based innovation solutions such as process optimization, defect detection automation, and predictive maintenance of equipment.

  3. Strengthening Digital Transformation Capabilities

    • You can learn about investment effectiveness (ROI), risk management, and organizational change response strategies that should be considered in the AI adoption process, contributing to corporate DX initiatives.

  4. Improving field-oriented problem-solving skills

    • You can develop the ability to independently diagnose field problems and propose improvement solutions through data analysis and AI utilization.

Features of this course

Please introduce the key features and differentiating factors.

13찚시_AI륌 활용한 제조업 분알의 싀묎 적용 전략-자동찚부품 제조업 분알_강의

Curriculum specialized in automotive/parts manufacturing

Explanation of methods for applying AI technology in the field at automotive/parts manufacturing

Data-driven design utilizing AI technology

Explanation of data-driven design using AI technology in a systematic and efficient manner

You'll learn this content

  • Smart Process Management

    • Methods for Process Optimization Using Sensors and Data

    • Case Studies of Defect Rate Reduction and Production Efficiency Improvement

  • Quality Inspection and Predictive Maintenance

    • Image Recognition-Based Automatic Defect Detection

    • Equipment Data Analysis for Failure Prediction and Maintenance

  • Production Planning and Demand Forecasting

    • AI-Based Market Demand Analysis

    • Production Schedule Automation and Cost Reduction Strategy

  • Supply Chain Management and Traceability

    • Data-driven tracking from raw materials to finished products

    • Ensuring Reliability through AI and Blockchain

  • Industry-Wide Application Strategy

    • Automotive, electronics, machinery, food, chemical and other various manufacturing industries common utilization model

    • Step-by-step Implementation Strategy from SMEs to Large Enterprises

The person who created this course

  • I have been conducting lectures and consulting related to building smart factories using AI and big data and industrial digital transformation (DX).

Do you have any questions?

Q1. Can AI be applied to actual manufacturing sites?
A1. Yes, it can be immediately utilized in various processes such as quality inspection, equipment predictive maintenance, and production planning.

Q2. Can small and medium-sized enterprises like our company also adopt AI?
A2. Yes, it's possible. By utilizing cloud-based solutions or lightweight AI tools, you can reduce cost burdens.

Q3. Can defect detection or equipment predictive maintenance be applied to our industry as well?
A3. While there are differences by industry, data-based quality management and equipment management can be applied to most industries.

Q4. How do existing workforce and work methods change when AI is introduced?
A4. Simple repetitive tasks become automated, and employees transition to roles focused on data analysis and problem-solving.

Q5. How do you ensure data security and reliability?
A5. We can secure safety by implementing AI alongside security solutions and blockchain-based tracking technology.

Pre-enrollment Reference Information

Practice Environment

  • Operating system and version (OS): OS types and versions such as Windows, macOS, Linux, Ubuntu, Android, iOS, etc.

  • Tools Used: Software/hardware versions and billing plans required for practice, virtual machine usage, etc.

  • PC Specifications: CPU, memory, disk, graphics card, and other recommended specifications for program operation

Learning Materials

  • Learning materials format provided (PPT, cloud links, text, source code, assets, programs, example problems, etc.)

  • Volume and capacity, characteristics and precautions for other learning materials, etc.

Prerequisites and Important Notes

  • Basic Manufacturing Flow
    (e.g., overall concepts such as production processes, quality control, equipment management, etc.)

  • Basic concepts of data
    (What sensor data and production data are - a level where you can handle them with Excel is sufficient)

  • Basic Understanding of AI
    (You don't need to understand the principles of machine learning or deep learning - it's sufficient to know the concept that AI 'learns from data to find patterns')

In other words, this is a lecture designed to be understood by field practitioners, managers, or planners looking to integrate AI into manufacturing without requiring specialized IT knowledge.

Recommended for
these people

Who is this course right for?

  • Automotive and Parts Manufacturing Workers

  • Manufacturing Digital Transformation (DX) Manager

  • B2B/Corporate Training Target Audience

  • Learners interested in AI and manufacturing convergence fields

Need to know before starting?

  • Understanding Artificial Intelligence

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Curriculum

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18 lectures ∙ (3hr 42min)

Course Materials:

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