Technical Training

Master of Science in Digitalisation of Manufacturing

Objectives

The Master of Science 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.

Programme Costs

Programme cost: €9,660 subsidised rate, split into 2 payments.

European Social Fund +

The European Social Fund + supports training across a wide range of industry sectors and geographical regions. The aim of the ESF+ programme is to invest in people with the objective of achieving high employment levels, fair social protection and a skilled and resilient workforce for the future world of work.  ESF + funding will also improve employment opportunities by providing training to up-skill and reskill, lifelong learning, and education measures with a focus on those who face greater challenges in accessing the labour market. The ESF+ will contribute to reducing the risk of poverty and social exclusion, with an emphasis on disadvantaged groups. Learn more on European Funding here: www.eufunds.ie

Delivery

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.

Content includes

Core Technical Modules

• Data Analytics & Machine Learning
• Database Design & Data Visualisation
• Cyber-Physical Systems & IoT

Electives (1)*

• Integrated Database Systems
• Manufacturing Automation & Robotics
• Digital Twins in Production

* 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.

Thesis

• Applied Research Dissertation
• Scientific Dissemination

Research Support Modules

• Research Integrity
• Research Methodologies
  

Core Learning

Data Analytics & Machine Learning: This module will review the application of statistics and experimental design to applications in industry. The aim of the module is to enable the learner to program statistical, and in particular, machine learning applications, based on manufacturing data sets, using standard mathematical tools.

Database Design & Data Visualisation: This module adopts an applied learning approach to identify opportunities and work with data through the lens of the relational database model. The aim of this module is to enable the learner to interface with standard industrial systems and collect and interpret datasets for data-driven intelligence. Therefore learners will acquire the skills necessary to design and develop database systems, collect, clean, visualise and interpret data rooted in best data analysis practice.

Cyber-Physical Systems & IoT: This module adopts an applied learning approach to understanding embedded systems, the Internet-of-things (IoT) and the cyber-physical systems (sensors, control boards) necessary for data acquisition in industrial environments. The aim of this module is to enable the learner to programme standard ICT Boards, I/O, sensors and gateways in order to collect time-series data streams. Furthermore, the application of data stream analysis at the Board/Gateway level (edge computing) will be explored.

Applied Research Dissertation: The learner is expected to apply an innovative approach to a complex problem while collaborating with an industrial partner in a professional manner. The applied research project will require the completion of a comprehensive range of relevant elements, including; a review of existing knowledge, the evaluation of change requirements, the definition and analysis of a manufacturing digitalisation problem in industry, the design, planning and/or implementation of a solution within a constrained schedule and budget, appropriate consideration of social and ethical norms, the validation of performance (value engineering) and the dissemination of the project’s results and impacts. The applied research project will be self-directed by the Learner, supported by an industry mentor and academic supervisor.

Research Support Modules: These modules aim to provide students with the fundamental skills to scope out a suitable research project, to carry out the research work and to produce and present scientific and technical outcomes, within the principles of integrity, ethics, data management and good scientific research practices.

Schedule of Delivery

Semester 1

1. Cyber-Physical Systems & IoT
2. Database Design & Data Visualisation

Semester 2

1. Data Analytics & Machine Learning
2. Elective 1
3. Research Integrity

Semester 3

1. Applied Research Dissertation
2. Research Methodologies

Semester 4

1. Applied Research Dissertation
2. Scientific Dissemination

Who should attend

Aimed at existing manufacturing, mechanical or engineering professionals, and those migrating from associated disciplines.
Entry Requirement

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.

Qualification

NFQ Level 9 - 90 credits.
Successful completion of the master's classes (30 Credits) may lead to an exit award – a Certificate in Smart Factory Operations (L9 Minor Award).

Upcoming dates:

No training dates available at the moment.