Digitalisation of Manufacturing - Blended

The Masters in Digitalisation of Manufacturing is a practice-based professional award for experienced employees in all sectors of manufacturing. The programme focuses on the challenges and benefits of applying advanced digital technologies to drive productivity, capacity and growth. The majority of the credits and learning outcomes are work-based, underpinned by a training programme of masterclasses and research supports, delivered through bootcamps, workshops, and on-line tutorials, and supported by guest lecturers and site-visits. The initiative is industry-led in conjunction with the Irish MedTech Skillnet and an Industry Expert Working Group. The programme aims to enable a ‘Digital Champion’, operating within a manufacturing enterprise, who can focus on the adoption of smart technologies, the integration of systems, the analysis of key data and the demonstration of opportunities and added value for the business.



The masterclasses provide an expert-level appraisal of relevant technologies, tools and techniques so that the Learner can assess current trends, engage with specialist professionals, and identify the potential benefits of digitalisation of manufacturing and embracing industry 4.0/5.0.



The completion of the applied research project brings significant advances in terms of professional and personal development, communication skills and confidence to present proposals and results. The necessary research and transversal skills (communications, research methods, project management) will be delivered through online, self-directed modules with regular workshops in the University.

Semester 1

Database Design & Data Visualisation

Cyber-Physical Systems & IoT



Semester 2

Data Analytics & Machine Learning



Elective*



* Electives: Choice subject to availability and prioritised relevance to research topic. Additional elective courses may also be available from cognate masters’ programmes in TUS and RUN-EU.



Integrated Database Systems

Manufacturing Automation & Robotics

Digital Twins in Production



Research Integrity



Semester 3

Applied Research Dissertation

Research Methodologies



Semester 4

Masters Research Dissertation

Scientific Dissemination

The programme is aimed at existing manufacturing, mechanical or engineering professionals, and those migrating from associated disciplines. The principal entry requirement for the Masters programme is a Level 8 honours degree, at minimum second class honours (NFQ or other internationally recognised equivalent), in a relevant scientific, engineering, computing, or technology discipline. Applicants from other Level 8 degree disciplines who have a minimum of five years experiential learning in an appropriate manufacturing environment (with a demonstrable knowledge of mathematics and computing) may also apply. Their admission to the program will be determined by the Technological University Shannon (TUS) Recognition of Prior Learning (RPL) Process. A deep knowledge of manufacturing environments and the potential benefits and challenges facing manufacturing from digitalisation would be beneficial.

2 years, part-time, blended.



The delivery of the programme will be through scheduled Bootcamps and Workshops, and online through the Institutes Virtual Learning Environment (Moodle), and is assessed through project work and completion of the Applied Research Dissertation.



On-campus Time: The structured elements require attendance in-person of up to 24 days over the two years structured into four Bootcamps (4 days, Wed-Sat) and four Workshops (2 days – Fri-Sat). The in-person bootcamps and workshops promote peer-to-peer exchange of knowledge and build the Learners’ professional network. Regular online tutorials will ensure continuous contact with the academic staff. The learner effort over the two years is estimated at approximately 15% of their working commitment, thus strong employer and management support for the learner is vital, as is the relevance and potential value of the Applied Research Project to the host enterprise.