In partnership with NUS

Academy of GxP Excellence since 2009


This course provides an overview of the concept , tools, infrastructure and techniques which can be grouped together under the parent term of ‘Big Data”

The objective of this course is to guide the attendees through the following concepts;

·         Firstly , what is Big Data and the Fourth Industrial Revolution ?

·         How can large volumes of data be handled effectively in a Pharmaceutical Manufacturing Environment?

·         How can big data techniques be applied effectively and successfully in a Pharmaceutical Environment?

          o    Specifically, how can areas like manufacturing process quality and yield and equipment maintenance be improved , optimized through         the use of big  data analytics techniques

          o    What are the challenges?

          o    What are the benefits?



Over the past 20 years, with the advent of six sigma methodologies; pharma manufacturers have been able to reduce waste and variability in their production processes, leading to increased product quality and yield. However, due to the number and complexity of production activities in pharma and specifically biopharma manufacturing  (which can contain up to 200 production variables) , extreme swings in the yield and quality of product are the reality, even after the application of lean process improvement techniques. Additionally, with the increased FDA focus on continuous process verification, the need for manufacturers to use a more detailed, more granular approach to diagnosing and correcting process flaws is obvious.  Big Data and advanced digital analytics can provide such an approach.


This course will give the attendees a practical introduction to the world of Big Data and Digital Analytics and will present the many different technologies and techniques which can be used to optimise process characterisation and understanding to extract not only maximum product yield and quality but also business insights from large volumes of available data.

To reinforce the learnings,  course attendees will be led through three practical use cases:

1.    Process Understanding through Data Visualisation tools

2.    Predictive Modelling for Process Equipment Maintenance

3.    SPC using Data Streaming

Course Outline

1.       Introduction to Big Data (data formats, technologies and handling techniques) and the fourth industrial revolution.

·         Data explosion  - worldwide {3 v’s of big data ;Variety, Velocity, Volume}

·         Data Science v Data Mining, Data handling, Data lake, Big Data Ecosystems

·         How to maximise the value of data.

2.       Big Data Analytics techniques as the basis for Continued Process Verification -  Introduction and demonstration;

·         Statistical Process Control using process data streaming.

·         Using data viz to identify patterns in the data (scatter plots, paretos, histograms, box plots, capability plots etc)

·         Using statistical techniques to understand the data (confidence intervals, correlations,  

·         Use of Machine Learning techniques to identify the optimal parameter ranges.

3.       Pharma Manufacturing  Equipment Maintenance -  Introduction and demonstration;

·         How to drive efficiencies in predictive maintenance programme through modelling techniques.

·         Understand and pinpoint machine breakdowns using Machine Learning.

·         Quantify reliability on machine and even part level.

4.       3 x Case Studies

·         Manufacturing equipment preventative maintenance using predictive modelling techniques.

·         Manufacturing SPC using shop floor data streaming.

·         Manufacturing Data Viz exercise.

5.       Quiz and group participation

·         Course Handouts – comprehensive templates and examples to help you with implementation.


Learning Outcome


Upon completion of this course the attendees will be able to:

·         Gain an understanding of a practical approach to harnessing the power of Big Data in pharma manufacturing.

·         Gain an understanding of the application and benefits of Predictive Modelling in Engineering maintenance

·         Learn the benefits of performing Lean Process improvement using digital analytic techniques.