[Logistics Management AX Agent] Data Analysis and SCM Decision-Making Automation

We reduce manual, simple analytical tasks and transfer an AI-based SCM decision-making system that directly leads to performance in actual logistics and production sites. We provide a practical framework that allows logistics and supply chain managers to immediately shorten decision-making time and reduce operational costs in the field.

1 learners are taking this course

Level Beginner

Course period Unlimited

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What you will gain after the course

  • Understand the paradigm shifts in SCM and perform logistics data cleaning and Exploratory Data Analysis (EDA) using generative AI tools.

  • By applying hybrid AI techniques, it is possible to perform precise demand forecasting and establish an intelligent inventory management system.

  • One can verify AI-based prediction results from multiple perspectives and execute strategic communication to persuade stakeholders based on these findings.

  • You can design a logistics process automation roadmap through AI agent orchestration concepts and practical logistics application cases.

Inaccurate demand forecasting and piling inventory—the AI solution changing the game for SCM!


Are you worried about missing sales opportunities due to stockouts, or conversely, facing skyrocketing management costs due to excessive inventory? In an era of increasing supply chain uncertainty, relying solely on a manager's experience or past Excel data to forecast demand and manage inventory poses a significant risk to business operations.

This course reduces manual, simple analytical tasks and imparts an AI-based SCM decision-making system that directly leads to performance in actual logistics and production sites. It provides a practical framework that allows logistics and supply chain managers to immediately shorten decision-making time and reduce operational costs in the field.


Field-oriented Education

  • Automate SCM tasks with AI agent-based data cleaning (EDA), hybrid demand forecasting, and intelligent inventory management.

  • We provide practical solutions that can be immediately applied to logistics and production sites, ranging from AI analysis result verification to process automation roadmaps.



Presenting core points centered on practical application

  • Topic-specific learning is possible through micro-learning centered on core keywords that can be applied immediately in practice.

Course Curriculum


  1. Understanding the SCM Paradigm Shift and AI Agents

  2. Data Cleaning Agent & EDA Copilot

  3. Hybrid AI Demand Forecasting

  4. AI Intelligent Inventory Management

  5. AI prediction result verification and persuasive communication

  6. Orchestration and Practical Cases of Logistics AI


Presenting differentiated content through the recruitment of tax and accounting expert instructors

We provide lectures by selecting only the core skills required in industrial fields through the recruitment of tax and accounting expert instructors. We have presented professional and differentiated content by recruiting instructors who are highly acclaimed for their in-depth lectures at corporations, public institutions, and universities.

Instructor Dong-cheon Choi

Current) Lead Consultant at TSP Consulting

Current) Logistics Instructor at the Korea Productivity Center

Former) Lead Consultant at Logistics Revolution Korea Co., Ltd.

Former) Senior Consultant at Korea Management Association Consulting (KMAC)



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Who is this course right for?

  • Logistics and Supply Chain Management (SCM) practitioners and managers

  • Business Innovation and Strategic Planning Manager

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Courses

As an affiliate of the Korea Productivity Center established in 1987, we provide essential job training for employees of corporations and public institutions.

We have developed job-duty-task-based educational content to strengthen practical skills, based on the actual 'work' that occurs in real corporate business environments.

Experience job training on a whole new level!

Homepage : https://www.kpcice.or.kr

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Curriculum

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21 lectures ∙ (3hr 26min)

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