Smart Manufacturing – Improving OEE 

Who Should Attend?

Smart Manufacturing Professionals

Duration: 3 Days

Technologies had allowed machines, devices, sensors, and people to be interconnected and this results in an enormous amount of data generated and exchanged. Such reform necessitates systematic analytics of data to transform them into information that could be used for decision-making. Therefore, organizations must be able to adapt to the big data phenomenon to meet the expectations of Smart Manufacturing.

Incorporating a mixture of theories and hands-on, this course will guide you through the methodology to carry out an analytical project to improve machine availability. The essence of this course – the analytical methodologies to turn data into foresight will be the key to sustainable innovation in a smart manufacturing environment.

After this training, participants will be able to:
  Explain basic data science concepts
  Prepare data for analytics
  Prepare low-volume data for prediction
  Apply predictive analytics to detect abnormal data and predict machine breakdowns
  Evaluate prediction model for production use

1. Overview
▪ What is Industry 4.0?
▪ How do 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?
▪ The concepts of data science

2. EDA: Exploratory Data Analysis
▪ Clean and prepare data for analysis
▪ Understanding and exploring the information in your datasets
▪ Descriptive statistics
▪ Data visualization as part of data mining
▪ Handling low-volume data

3. 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

4. Evaluation of Models and Results
▪ Validation and prediction of performance indicators
▪ Mapping the results back to business objectives

5. Delivering and Operationalizing Analysis Outcomes
▪ Identifying and flagging abnormal equipment using an anomaly detection model
▪ Predicting future equipment breakdowns using a trained model

Case Study 💡

1. Predictive Maintenance
▪ Predict machine breakdowns

2. Anomaly Detection
▪ Detect abnormal sensor reading
▪ Interpret the result and find insights

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Private Class Available

Contact us to find out more information if you are interesting in organizing private group class for your corporate.