Automation and Digital Manufacturing

Master of Engineering

Course Details

Course Code	GA_EAMUG_V09
Level	9
Duration	1 year
Credits	90
Method of Delivery	On-campus
Campus Locations	Galway City – Dublin Road
Mode of Delivery	Full Time

How to Apply

How to Apply

Non-EU Applicants

Non-EU applications are currently closed. Register to be notified when applications reopen

Course Overview

This programme is part of a suite of programmes developed with Industry to support the transition to Industry 4.0. The Masters is a full-time programme specifically designed for engineers and graduates who wish to upskill in the field of Automation and Digital Manufacturing. Students will take 40 credits of taught modules which will have assessments contributing to their 50 credits research project.

The programme will be delivered on a full-time basis over 1 year divided into 3 semesters. In semesters 1 and 2, students engage in a rigorous schedule of synchronous workshops and asynchronous material. The Research Project is fully integrated into the taught modules in semesters 1 and 2 and continues as a dedicated module in semester 3 under the guidance of an individual supervisor.

Structure of the Programme
In Semester 1, students take on three taught modules concentrating on:

Research Methods: Where they will conduct a literature review and develop a project plan for their Research Project by the end of the semester.

Lean Automation: Where they will analyse and optimise flow and variation in the manufacturing process they are planning to automate/digitalise.

Data Driven Decision-Making: This works in parallel with Lean Automation to identify the key data that needs to be captured from the manufacturing process and determine how these data should be utilised to support decision-making.

In Semester 2, students take on a new taught module and continue with the integration of their research, concentrating on:

Lean Automation: Integrated with the Research Project to ensure that the automated process is optimised from a Lean and Six Sigma point of view.

Data Driven Decision-Making: Integrated with the Research Project to investigate the data analytics side of the project.

System Integration: Supports the Research Project in the design of the data architecture of the process and the selection of hardware and software.

In Semester 3, the Research Project continues as the primary focus under the guidance of the individual supervisor to completion.

Course Details

Year 1

Semester	Module Details	Credits	Mandatory / Elective
1	Research Methods The aim of this module is to ensure that students will be fully competent to conduct and disseminate research. Students will develop skills in critically engaging with academic and technical literature and learn to synthecise information. They will learn to select appropriate research tools and plan their project to reach deliverables. They will reflect on some the ethical, societal and practical problems of data collection, including sampling, gaining access to data, designing a research instrument and the principle of quantitative and qualitative analysis. They will learn to integrate societal issues such as universal design and sustainability in the scope of their project. Learning Outcomes 1. Systematically review and evaluate current literature, using appropriate tools and techniques. 2. Identify, analyse and evaluate appropriate research methods for research project proposal development. 3. Demonstrate the synthesis and integration of knowledge. 4. Develop a research proposal in line with best practice in project management. 5. Create an appropriate data management structure. 6. Communicate research in various formats including written and oral presentation methods. 7. Integratesustainability and universal design principles in the development of an engineering solution to a problem.	10	Mandatory
2	System Integration This module will look at the data architecture of a manufacturing plant from manufacturing floor up to ERP level in accordance with the ISA-95. Students will learn how to assess an existing data architecture and plan for a new one taking into account validation requirements. Learning Outcomes 1. Analyse and critically evaluate published literature in the area of:system integration in manufacturing. 2. Critically assess the existing data architecture of a manufacturing plant and its components. 3. Design specification for a data architecturebased on user requirements. 4. Plan horizontal and vertical integration of a data architecture in a manufacturing system. 5. Develop a data management system for a manufacturing process. 6. Develop anintegration plan considering validation. 7. Lead a team through the problem-solving and thedecision-making process. 8. Disseminate and discussresearch findings amongst peers.	10	Mandatory
Year	Lean Automation This module looks at the application of Lean Automation in manufacturing, its implications and benefits for the company. The module also explores Lean Six Sigma concepts and tools that can be applied to a company's processes in order to provide the structure and rigour necessary to ensure that waste was removed and variation was reduced before automation. Leveraging Lean Six Sigma concepts and tools before automation technologies are chosen or deployed can ensure high-performing automation solutions. Learning Outcomes 1. Analyse and critically evaluate published literature in the area of: Lean Automation in manufacturing; application of Lean Six Sigma tools in the context of automation. 2. Identify, select and apply LeanSix Sigma tools for evaluating process performance, identifying bottlenecks, problem-solving, prioritising problems and uncovering the root causes of the process problems. 3. Propose solutions forprocess improvement and waste reduction, and identify areas where a process could benefit most from automation technology. 4. Disseminate and debate Lean Automation and LeanSix Sigma research amongst peers. 5. Demonstrate problem-solving and reflective critical thinking in relation to literature and in the context of discussions and presentations. 6. Lead a team through the problem-solving and thedecision-making process.	10	Mandatory
Year	Data Driven Decision Making Data-driven decision making is defined as using facts, visualisations, metrics and data to guide strategic business decisions that align with your goals, objectives and initiatives . The objective of this module is to examine how different decision theories, decision tools and data analytical and data visualisation approaches can improve the performance of employees & organisations, and to decide the types of business problems that these theories, tools and approaches can best address. Learning materials include online videos, forum based discussions and problem based learning. Learning Outcomes 1. Research and synthesiseinformation justifying the use of data driven decision-making in the manufacturing context. 2. Appraise how digital transformation can impact decision making and analysis 3. Critically analyse a set of data using data analytics, data visualisation tool and methods. 4. Formulate recommendations using decision theory. 5. Critically analyserisk and uncertainty issues in decision-making. 6. Appraise different methods for managing risk and uncertainty. 7. Communicate results of research and innovation to peers and engage in critical dialogue. 8. Revise recommendations based on feedback from peers	10	Mandatory
1	Research Project in Automation and Digital Manufacturing In this module, students will conduct a research project in the field of automation and digital manufacturing. They will define a problem in industry that can be solved by automation and digital manufacturing. They will then measure and analyse the impact of the problem. Using their literature review to identify emerging technologies in automation and digital manufacturing, they will then design or re-design the manufacturing process. Learning Outcomes 1. Develop knowledge and understanding of mathematics, sciences, engineering science and technologies underpinning Automationand digital Manufacturing. 2. Apply their knowledge of new developments in the field of Automation and Digital Manufacturing toimprove the quality, productivity, efficiency, sustainability and/ or ergonomics of a process. 3. Design of a novel manufacturing system or process using analysis and interpretation. 4. Design and conduct experiments to validate their design. 5. Identify, formulate, analyse and solve complex engineering problems. 6. Combinetheir knowledge of process improvement methods and standardsto design processes in compliance with regulatory frameworks. 7. Conduct research on advanced topics in automation, robotics and digital manufacturing to improve efficiency and improve data driven decision-making. 8. Propose novel solutions and act as a change agent to support the transition to industry 4.0. 9. Conduct research to fill self-identified gaps in their own knowledge of automation and Digital Manufacturing. 10. Apply high standards in the practice of engineering, including the responsibilities of the engineering profession towards people and the environment.	50	Mandatory

Recommended Study Hours per week

In addition to the onsite attendance on campus student will have to study independent an additional 15 hours

Examination and Assessment

The choice of assessment methods is aligned with the overall aims of the programme and the qualities or abilities being sought from the student. The modules continuous assessment strategies are aligned directly with the module learning outcomes. The assessment types which are used on the modules include assignments, contribution to collaborative forums, Moodle quizzes, group work, problem and scenario-based exercises, presentations, individual projects.
This combination of formative and summative assessments are used as learning techniques that seek to develop each student’s capability to identify and solve problems which arise in engineering practice. Assessments such as individual projects and small group assignments are used to assist with the development of each students’ communication, time management, leadership and conflict resolution skills so as to allow them to work effectively individually and as team members or team leaders.
The individual projects in taught modules are integrated into the Research Project in Automation and Digital Manufacturing module.

On-Campus Attendance Requirement

Students will work in the lab a minimum of 2 days per week in addition to the weekly workshops during academic terms. Attendance on site will be a minimum of three days per week

Download a prospectus

Postgraduate Prospectus

Entry Requirements

Candidates must hold level 8 Bachelor (Hons) degree or Higher Diploma in Automation & Digital Manufacturing or a B.Eng. (Hons) in Mechatronics or cognate discipline with a minimum grade classification of H2.2 or equivalent.

As the research is industry based, applicants must either have the support of their employer to conduct research or have an agreement with a company to use their facilities for the purpose of their research. The outline of the project must be agreed in advance of admission and an interview to discuss the validity of the project will be conducted. All potential applicants should contact Dr. Carine Gachon carine.gachon@atu.ie

English Language Requirements:
English Language Requirements will be as determined by ATU and as published in the Access, Transfer and Progression code. The current requirements are as follows:

Non-EU applicants who are not English speakers must have a minimum score of 6.0 (with a minimum of 6.0 in each component) in the International English Language Testing System (IELTS) or equivalent. All results must have been achieved within 2 years of application to ATU.
EU applicants who are not English speakers are recommended to have a minimum score of 6.0 (with a minimum of 6.0 in each component) in the International English Language Testing System (IELTS) or equivalent. Further details on English language requirements are available

Recognition of Prior Learning
ATU is committed to the principles of transparency, equity and fairness in recognition of prior learning (RPL) and to the principle of valuing all learning regardless of the mode or place of its acquisition. Recognition of Prior Learning may be used to:

gain access or advanced entry to the programme
gain credits and exemptions from programme modules after admission
in award years RPL will be considered to a maximum of 50% of the credits.

Engineers with significant industry experience who can demonstrate either certified or experiential learning (or combination of both) in Lean Automation, Data Driven Decision making and/or System Integration can get exemptions from the relevant taught modules and potentially shorten the duration of the programme.

Fees

Total Fees EU: €6,300

Total Fees Non-EU: €14,000

Further information on fees

Further Information

Start Date

September 2026

Who Should Apply?

The course allows Automation Engineers working in Industry to upskill to a level 9 while working. The programme can combine with the H.Dip. in Automation and Digital Manufacturing to redirect the career of other Engineers (Manufacturing, Mechanical, etc..) to the Automation and Digital Manufacturing field. The research project can be fully embedded in their day-to-day job to optimise their workload.