• What are the Big Data sources in Supply Chain and Logistics and their utilization,
• Methods for machine learning and its use for forecasting,
• Using analysis results for a dynamic simulation basis,
• Cost reduction trough predictive analytics,
• Decision making in real time,
• Forecasting the events based on complex behavior analysis and pattern recognition.

By the end of this course the delegates will:

• Learn how to utilize Machine Learning in Supply Chain Management.
• Learn new concepts from industry proven examples,
• Gain a foundational understanding of a subject and the use of AI within Supply Chain,
• Develop job-relevant skills with hands-on projects and examples.
• Create virtual models of Supply Chains.

This training course is suitable to a wide range of professionals but will greatly benefit:

• Operation Managers
• Project Managers
• Supply Chain Managers
• Risk Managers
• Plant Managers
• Production Planners
• HR Managers
• Logistics Managers
• Plant Managers
• Bossiness Improvement Specialists

The delegates will be walked through the examples of using the software for Supply Chain design, market planning, inventory optimization, warehouse practices improvement, risk management and customer satisfaction measurement.

Delegates will go through the step-by-step process of creating the supply chain process, performing the risk assessments, acquiring and analyzing the results and preparing the recovery measures, and using Machine Learning algorithms best utilized within the Supply Chain.

The delegates will create simulation models based on the real life data, and will be encouraged to simulate the processes they are already working on. The focus is on the actual work of the delegates and their effective use of software

Supply Chain and Machine Learning Introduction

• Use cases of machine learning in the supply chain
• General artificial intelligence concepts
• Vision of the Supply Chain 4.0 and the future of Logistics
• Exercise: machine learning model for Supply Chain
• Exercise: Supply Chain modelling trough AI

Concepts relating to the ML paradigm

• How to pick the right model for Supply Chain
• Data Driven Supply Chain Optimization
• k-means,
• Apriori,
• Aykin and Babu algorithms
• Exercise: Use of Big Data within AI software

Machine learning on images and text

• Framework oriented on customer requirements
• Exercise: Creating a model of customer behavior
• Optimization of distribution
• Natural language processing
• Exercise: Creating and evaluating distribution network models
• Optimization of inventory management
• Exercise: Modeling inventory management policies

Optimization of manufacturing process and AI for detecting anomalies

• Optimizing product design and innovation
• Optimizing the production process
• Exercise: Creating a Supply Chain network
• Using created models as agents for future models creation

CDGA attendance certificate will be issued to all attendees completing minimum of 80% of the total course duration