AI Course for Supply Chain Leaders

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Transforming Supply Chain Management with AI

4 Online Sessions | Tuesdays, 11:00 am EST

About the Course

AI Transforms Supply Chain

The AI revolution unlocks the potential to transform supply chain planning and management. Employing innovative technologies like machine learning and deep learning delivers precise forecasting, advanced data insights, and optimized inventory. This leads to cost savings, performance improvements, and increased profitability.

Course Agenda

In this course, we will delve into the theoretical and practical foundations of integrating artificial intelligence (AI) into supply chain management. We will explore key concepts such as machine learning (ML) and deep learning (DL), and examine the role of Big Data in AI-driven decision-making across various stages of supply chain planning and management.

Comprehensive Training Program

This course is part of Intelichain’s exclusive training program, attended by leading companies across industries. In addition to online courses on a range of topics—such as Excellence in Demand Planning, Excellence in Inventory Planning, Supply Chain Secrets for Sales Teams, Production Planning, and the S&OP Bootcamp. tailored training sessions can be arranged for individual companies as needed.

Let’s discover what your future looks like, together.

Course Teachers

Taught by top supply chain experts with real-world and academic experience

Israel Israeli

CEO & Founder

roeiaviram@gmail.com

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Jeremy Rothman-Shore

CTO

jeremy@inteli-chain.com

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Key Takeaways

Introduction to AI in Supply Chain

What is AI
Key concepts: AI, Machine Learning (ML), Deep Learning (DL)
Differences between AI, ML, and DL
What is GenAI and practical applications of GenAI in supply chain
How AI is transforming supply chain management

Big Data & AI in Supply Chain

Understanding Big Data: Data layers, attributes, and sources
Role of Big Data in AI-driven decision-making
Supply chain applications: demand forecasting, inventory optimization, logistics efficiency
Challenges in data collection, cleansing, and integration

Machine Learning Fundamentals

ML concepts: how machines learn
Types of learning: supervised, unsupervised, reinforcement learning
Optimization models: training, validation, and test phases
Key supply chain applications: demand forecasting, inventory classification, fraud detection

ML Models for Supply Chain

Overview of essential ML models and supply chain applications: K-Nearest Neighbors (KNN), Neural Networks, Logistic Regression, Linear Regression, K-Means Clustering, Decision Trees & Random Forests
Real-world uses in supply chain
Hands-on: Implementing ML models in Excel for demand forecasting and inventory planning

AI-Powered Demand & Inventory Planning