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Applied AI

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AI for Work

Starting Manufacturing AI Implementation Without Coding

"We need to do DX/AX." It's always serious in the meeting room, but when you actually get down to the field, don't you feel lost about what to do first and how to do it? You've done a few PoCs, but they never transition to regular operations You bought solutions and equipment, but they're treated as cumbersome tools on-site There's a lot of talk about data and systems, but you can't figure out how to apply them to your current line or process There's no dedicated TFT or team, so you're a practitioner who has to handle both your main job and DX/AX... This course was created specifically for people like you.

(4.3) 3 reviews

18 learners

Level Beginner

Course period Unlimited

  • fleem826937
Generative AI
Generative AI
Generative AI
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What you will gain after the course

  • The fog lifts on 'what needs to be done' for AI adoption.

  • I can see actionable steps on 'how to get started' with AI.

  • You'll gain the ability to persuade others on how to introduce AI within your organization.

Manufacturing DX/AX and smart factory stories are everywhere,
but when it comes to actually applying them to your own factory or production line, it often remains unclear.

  • "We have all the data," they say,
    but from an AI perspective, there's hardly any usable data

  • I've done PoC several times,
    but there are almost no cases that have transitioned to regular operations

  • AI companies, headquarters/planning teams, and field teams
    often speak different languages, causing projects to start off on the wrong foot.


This course starts from that reality.
It's not about AI theory or coding education, but rather a course that enables field and production technology practitioners to 'evaluate and design AI projects'.


The content covered in this course is as follows.

  • Chapter 1
    How to organize "where and why to use AI"
    in practical terms, not technical jargon

  • Chapter 2
    Reinterpreting the statement
    "Our factory isn't structured for AI"
    from a data and structural perspective
    – We'll examine how human-dependent processes, unexpected issues, supplier problems, and post-event statistical data
    hinder AI implementation.

  • Chapter 3
    Revisiting the phrase "We have all the data"
    from the C·O·L (Condition·Outcome·Link) perspective
    – We specifically examine the difference between
    data that AI can actually use and data that only looks good to human eyes.

  • Chapter 4
    A section that checks "Is our factory ready for AI?"
    with five checklists
    – We objectively examine whether problem definition, data structure,
    action design, operational ownership, and pilot scope are ready.

  • Chapter 5
    Not just 'one-and-done PoCs,' but
    how to build a repeatable AI experimentation system within the factory
    – Covering candidate problem selection, hypothesis definition, KILL/GO rules,
    capabilities to build internally vs. capabilities to outsource,
    and how to define success based on on-site behavioral metrics rather than technical indicators.


When the course ends, students will be able to answer at least the following questions.

  • "In our factory, where are the areas we shouldn't implement AI right away,
    and where are the areas we need to prepare for first?"

  • "What are the candidate problems where we can try AI with the data we currently have?"

  • "When receiving an AI project proposal,
    what criteria can help determine what's realistic versus what's exaggerated?"


Even if you don't write code yourself,
this course is built on the premise that people who understand the problems, data, and field should be at the center of AI projects.
The goal is for those in charge of field operations, production technology, quality, and smart factories
to gain "the criteria to make their own judgments, rather than being led by AI/AX discussions."

Recommended for
these people

Who is this course right for?

  • A practitioner who is "concurrently handling" DX/AX-related work in the factory/production technology/quality/facilities area

  • Those who need to implement automation and smart factory solutions but can't visualize what would fit their current processes and production lines

  • For those frustrated that while the company talks about "AI and data," the actual workplace is still based on Excel and paper

  • DX/AX for "those who want to properly establish a solid structure at least once from the start"

Hello
This is

22

Learners

3

Reviews

4.3

Rating

2

Courses

With over 15 years of experience in production technology and equipment engineering within the manufacturing sector, I have specialized in solving on-site challenges through data and systems. Starting with PC-based equipment control, I have built my expertise in systemic improvement by understanding process and equipment structures and analyzing manufacturing data flows and operational frameworks.
Currently, I design and implement practical solutions in the field of Manufacturing AX (AI & Digital Transformation), connecting data, processes, systems, and automation.

www.linkedin.com/in/기호-이-3015a317b

Curriculum

All

5 lectures ∙ (51min)

Course Materials:

Lecture resources
Published: 
Last updated: 

Reviews

All

3 reviews

4.3

3 reviews

  • soykms님의 프로필 이미지
    soykms

    Reviews 4

    Average Rating 5.0

    5

    100% enrolled

    • calculator님의 프로필 이미지
      calculator

      Reviews 113

      Average Rating 4.9

      5

      100% enrolled

      This is a great lecture that helps you think about the considerations when implementing AI.

      • fleem826937
        Instructor

        I will continue to bring you content that is helpful for practical work. Thank you!

    • stonless0684님의 프로필 이미지
      stonless0684

      Reviews 2

      Average Rating 4.0

      3

      100% enrolled

      $26.40

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