Machine Learning and Predictive Analytics in Industry 4.0

Who Should Attend?


Duration: 4 Days

In the 20th century, manufacturing companies are striving to be more competitive and be more relevant to the industry to ensure sustainability of business while staying ahead of their peers. At the moment, manufacturing philosophy such as lean manufacturing is adopted quite extensively to ensure sustainability, however, there is still a need to further improve the current climate of the industry. This sparks the revolutionary idea of Industry 4.0.

Technologies has allowed machines, devices, sensors and people to be interconnected and this results in enormous amount of data generated and exchanged. Such reform necessitates the systematic analytics on data to transform them into information could be used for better decision making. However, data analytics is a relatively new phenomenon and its potential applications on manufacturing activities are wide-reaching and diverse.

In this course which incorporates a mixture of advanced analytics concepts and hands-on exercises, we will guide you through the methodology to implement data analytics projects to improve machine availability and production planning. The exercises will be carried out using state-of-the-art analytics tools. The essence of this course – the analytical methodologies to turn data into foresights will be the key to sustainable innovation in a smart manufacturing environment.

  • Articulate equipment anomaly, predictive maintenance, and production forecast scenarios
  • Understand the concepts of machine learning and build sophisticated prediction models
  • Identify relevant data sources and perform common data preparations
  • Evaluate model quality to relate back to business requirements
  1. Overview
    • What is Industry 4.0?
    • How Data Analytics play a role in Industry 4.0 and Smart Manufacturing?
    • What could be done with Data Analytics to solve business problems?
    • The concepts of machines learning and predictive analytics
  2. Smart Manufacturing Use Cases
    • Improve machine availability with predictive analytics: Anomaly Detection and Predictive Maintenance
    • Improve production planning with predictive analytics: Demand Forecasting
  3. Identifying and Getting Your Data Ready for the Use Cases
    • Where to obtain the relevant datasets?
    • How to cleanse and prepare your datasets?
    • The importance of incorporating multiple data sources
    • Transforming data to insights
  4. EDA: Exploratory Data Analysis 
    • Understanding and exploring the information in your datasets
    • Descriptive statistics
    • Data visualization as part of data mining
  5. Predictive Analytics
    • Engineering and selecting the right features to model equipment breakdown characteristics
    • Apply anomaly detection techniques to identify abnormal conditions in the production line
    • Use machine learning to predict future equipment breakdowns
    • Engineer the right features to model demand characteristics
    • Use machine learning to predict future demands
    • Time series analysis
  6. Evaluation of Models and Results
    • Validation and prediction performance indicators
    • Mapping the results back to business objectives

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