BIO09095 2021 Bio-Industry 4.0 Theory and Practice

General Details

Full Title
Bio-Industry 4.0 Theory and Practice
Transcript Title
Bio-Industry 4.0 Theory and Pr
N/A %
Subject Area
BIO - Bio Tech/Eng/Chem
LIFE - Life Sciences
09 - NFQ Level 9
10 - 10 Credits
Start Term
2021 - Full Academic Year 2021-22
End Term
9999 - The End of Time
Mary Butler
Programme Membership
SG_SBIOI_E09 202100 Postgraduate Certificate in BioIndustry 4.0 SG_SVALI_O09 202300 Postgraduate Diploma in Science in Validation and Digitalisation Technologies SG_SVALI_M09 202300 Master of Science in Validation and Digitalisation Technologies SG_SBIOI_M09 202300 Master of Science in BioIndustry 4.0 SG_SVALD_O09 202300 Postgraduate Diploma in Science in BioPharmaceutical Validation and Digitalisation

This module aims to provide students with an understanding of the key concepts of Industry 4.0 and outlines the application of the latest digital trends and technologies in the Bio-Industry 4.0 field, and how they can be practically applied.

Learning Outcomes

On completion of this module the learner will/should be able to;


Critically assess the smart factory concept in the biopharma industry, considering digital supply network, real-time data, optimised and predictive production.


Evaluate the role of PAT in the implementation of the smart factory concept, focusing on efficient use of data from different sources old and new, to bring about measured efficiencies and controls.


Evaluate the current applications of automation and the future potential applications and benefits of automation and data integration in the implementation of the smart factory including the identification of challenges.


Demonstrate an understanding of the ISA-95 model, the international standard for the integration of enterprise and control systems, along with other automation standards such as NAMUR and Profinet.


Covering the entire bioproduction supply-chain from design to delivery of the product to the patient, learn to evaluate various digital tools reproducing essential production elements (digital twins, serious games, immersive reality, virtual reality, and augmented reality) and cognitive approaches supported by artificial intelligence to promote understanding of processes and the appropriation of professional practices, using case studies where appropriate.


Engage in and demonstrate independent learning as well as communicate effectively as an Individual and or a member of a team

Teaching and Learning Strategies

This module will be taught using on‑line lectures. A range of Computer‑Aided Learning (CAL) packages are also used to support this module (e.g. Moodle,Adobe Connect, Panopto, Camtasia). Students are provided with electronic materials for self‑assessment and preparation for assessments/assignments. Self‑directed, student‑centred, independent learning is a core aspect through completion of module coursework.

Module Assessment Strategies

The assessment approach for this module will be 100% continuous employed including some of the following: Enquiry based Projects, Assignments/mini projects including Viva/Presentation Short-form assessment exams incl. MCQs, Short answer and Long Answer Questions.

Repeat Assessments

Students will have opportunities to re-submit work  as agreed with their lecturer.

Indicative Syllabus

The following is a summary of the main topics included in this module: Smart factory, PAT, ISA95 model, measurement and instrumentation, process control in biopharma manufacturing, shift from traditional supply chain to digital supply network.

Coursework & Assessment Breakdown

Coursework & Continuous Assessment
100 %

Coursework Assessment

Title Type Form Percent Week Learning Outcomes Assessed
1 MCQ 1 Coursework Assessment Multiple Choice/Short Answer Test 10 % Week 3 1,2
2 MCQ 2 Coursework Assessment Multiple Choice/Short Answer Test 10 % Week 6 1,2,3
3 End of Semester Assessment Coursework Assessment Assignment 30 % Week 11 1,2,3,4,5,6
4 Project & Viva Project Project 50 % Week 12 1,2,3,4,5,6

Part Time Mode Workload

Type Location Description Hours Frequency Avg Workload
Lecture Online Lecture 4 Weekly 4.00
Independent Learning Not Specified Self Study 10 Weekly 10.00
Total Part Time Average Weekly Learner Contact Time 4.00 Hours

Module Resources

Non ISBN Literary Resources
Journal Resources


URL Resources

Senatore, M. (2017) ‘Digitizing the Supply Chain: Why Pfizer Is Investing in IoT Technologies’.


Merck Millipore. (2019) Innovation and Digitalization - Merck Corporate Responsibility Report 2018.


Kirdar, A.O., Green, K.D. and Rathore, A.S. (2008) ‘Application of Multivariate Data Analysis for Identification and Successful Resolution of a Root Cause for a Bioprocessing Application’. Biotechnology Progress, 24(3), pp. 720–726.


Ündey, C. et al. (2010) ‘Applied Advanced Process Analytics in Biopharmaceutical Manufacturing: Challenges and Prospects in Real-Time Monitoring and Control’. Journal of Process Control, 20(9), pp. 1009–1018.


Bedre, P. (2013) ‘Applying QbD and Pat in Biological Manufacturing for “Continued Process Verification”’. Open Access Dissertations.


Chen, Y. et al. (2020) (9) ‘Digital Twins in Pharmaceutical and Biopharmaceutical Manufacturing: A Literature Review’. Processes, 8(9), p. 1088.


Chapman, C. (2018) How Amgen Uses AI Tools to Improve Manufacturing Deviation Investigations.

Other Resources


Additional Information