Big Data Analytics with Manufacturing Focus: Driving OEE Improvement with Abnormality Detection and Predictive Maintenance

9 – 11 July 2019 | Penang
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Course Objective

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 initiative sparks the revolutionary idea of Industry 4.0.
The main concept of the Industry 4.0 is to allow technologies and machines to communicate with human and business by exchanging data to make informed decisions. Coupling the data from machines, subject-matter-expertise and technologies, companies could leverage them to achieve business objectives more effectively and ergonomically.
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 decision making. Therefore, organizations must be able to adapt to big data phenomenon to meet the expectations of Smart Manufacturing. However, big data analytics is a relatively new phenomenon and its potential applications on manufacturing activities are wide-reaching and diverse.
In this 3-days course incorporating a mixture of theories and hands-on, we will guide you through the methodology to carry out an analytical project to improve machine availability, via exercises 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.

What Will You Learn?

Address production challenges:

  • Improve Overall Resource Efficiency
  • Increase Machine Availability

Address analytical challenges:

  • Equipment and process complexity
  • Data Quality

After the training, students will have the ability to:

  • Articulate equipment abnormally and predictive maintenance scenarios
  • Identify relevant data sources and perform common data preparations
  • Build sophisticated prediction models
  • Evaluate model quality to relate back to business requirements
  • Deliver results to enhance availability of equipments

Target Audience

This course is suitable for Operations, Production, Supply Chain, Business user, individuals and etc.

Training Outline

Overview
  • What is Industry 4.0?
  • How Big Data Analytics play a role in Industry 4.0 and Smart Manufacturing?
  • What could be done with Big Data Analytics to solve business problems?
Business Use Case
  • The concepts of data science
  • Improve machine availability with predictive analytics (1): Anomaly Detection
  • Improve machine availability with predictive analytics (2): Predictive Maintenance
Identifying and Getting Your Data Ready for Machine Availability Improvement
  • Where to obtain the relevant datasets?
  • How to cleanse and prepare your datasets?
  • The importance of incorporating multiple data sources
  • Transforming data to insights
EDA: Exploratory Data Analysis
  • Understanding and exploring the information in your datasets
  • Descriptive statistics
  • Data visualization as part of data mining
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
Evaluation of Models and Results
  • Validation and prediction performance indicators
  • Mapping the results back to business objectives
Delivering and Operationalizing Analysis Outcomes
  • Identifying and flagging abnormal equipments using an anomaly detection model
  • Predicting future equipment breakdowns using a trained model
  • Integration of the machine behavior prediction workflows into the smart factory ecosystem
  • Consuming equipment availability prediction results via dashboards for non-technical users

Prerequisite

Basic knowledge of computer programs and mathematics.