Digitale Transformation

Digital Manufacturing

by Education4Industry GmbH
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Preview image for training Digital Manufacturing

Course Features

  • Certified by TÜV Rheinland
  • Lifetime Access
  • Course Level: Beginner
  • Learning Extent: 1 hrs. 30 min.
  • Language: english
Target Audiences:
All Target Audiences

Course Overview

About this online Course

In this Skill, you will learn how Machine Learning and Advanced Analytics can help in a production environment to raise productivity.

In this Skill, you will learn how Machine Learning and Advanced Analytics can help in a production environment to raise productivity.

You will see how bottom-line economic improvements can be created by using Machine Learning and Advanced Analytics. We also cover what needs to be implemented to make improvements lasting. This Skill is less about the method itself and more about the implementation so that tangible performance improvements are created. We discuss an example of simultaneous improvement of yield, energy consumption and throughput enhancement in the cement production.

This Skill provides a basic understanding of digital manufacturing and how it is changing the world.

Target group

This Skill is targeted to Management of manufacturing companies, management of production sites; anyone who works in manufacturing/production.

Learning objectives


  • about use cases of Advanced Analytics
  • about advanced analytics in manufacturing
  • how advanced analytics can be applied in manufacturing
  • about the requirements for a digital transformation using advanced analytics
  • why starting with the impact is so important
  • how to think about capturing and storing huge amounts of data
  • how to think about structuring data in discrete and continuous processes
  • which steps are needed to clean a data set
  • what advanced analytics models are
  • that determining the real impact is only possible after considering the people who are part of the implementation
  • how advanced analytics can be used to optimize yield, energy, and throughput
  • which steps are involved in implementing an advanced analytics based yield, energy, and throughput project
  • about a practical case example for implementing yield, energy and throughput
  • what a target function is
  • about the steps in the data preparation phase
  • which steps are involved in developing advanced analytics models
  • about the various types of advanced analytics models that can be used
  • how to validate a model
  • how an Optimizer for our production process can be developed
  • how to implement and automate advanced analytics

Learning content

Introduction: Advanced Analytics in Manufacturing

  • What are use cases of Advanced Analytics?
  • How is Advanced Analytics already being used in manufacturing?
  • How can Advanced Analytics be applied in manufacturing?

    How to Implement Advanced Analytics

    • What are the requirements for a digital transformation using Advanced Analytics?
    • What are the specific steps for implementing an Advanced Analytics driven transformation?
    • What are Advanced Analytics models?
    • What roles are important for successfully implementing Advanced Analytics?

    Advanced Analytics Use Case: Yield, Energy, and Throughput

    • How can Advanced Analytics be used to optimize Yield, Energy, and Throughput?
    • What steps are involved in implementing an Advanced Analytics based Yield, Energy, and Throughput project?
    • What is a target function?
    • What are the different types of Advanced Analytics models and how can they be created/validated?
    • How can an Optimizer for the production process be developed, implemented, and automated?

    Test: Yield, Energy, and Throughput

    • Test your knowledge of predictive modelling.

    Learning Contents

    Summary of Learning Contents

    Digital Manufacturing 

    timelapse 1 hrs. 30 min.
    • Digital Manufacturing 
      extension interactive content
      timelapse 1 hrs. 30 min.


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