Big Data in Smart Manufacturing

About This Course

In the Industry 4.0 era, machines, devices, sensors, and people are connected via IoT and this has resulted in an enormous daily exchange of data. Such revolution necessitates the systematic analytics on data to transform them into information that enables ‘informed’ decisions.  

Big data analytics is a relatively new phenomenon and its potential applications on manufacturing activities are wide-reaching and diverse. Organisations must be able to adapt to big data technologies to meet the expectations of smart manufacturing. Having vast quantities of data at hand doesn’t mean one can extract the needed insights. Therefore, the key deliverables of this program, which is the analytical methodologies to turn big data into useful information, is the key to sustainable innovation in a smart manufacturing environment.  

In this 2-day program, participants will be exposed to multiple big data analytics examples in the manufacturing industry via hands-on exercises, which incorporates state-of-the-art analytics techniques. This program will also address production challenges (Eg yield ramp-up, waste reduction, throughput optimisation) and analytical challenges (Eg equipment and process complexity, process dynamics, and data quality). Upon completion of the training, participants will have the ability to perform all common data preparations, build sophisticated predictive models, evaluate model quality with respect to different criteria, and deploy analytical predictive models. 

Learning Objectives



Participants must have basic knowledge of computer programs and mathematics

Target Audience

Production/Process Managers, Production/Process Analysts, Engineers, and Researchers who want to understand the potential in using big data analytics for manufacturing process improvement

Training Outline

  1. Overview
  2. Basic Usage
  3. Data Cleansing and Preparation
  4. EDA: Exploratory Data Analysis
  5. Building Better Processes 
  6. Predictive Models
  7. Model Evaluation
  8. Model Application
  9. Sharing and Collaboration