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.

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.

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.

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.

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.

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.

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.

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

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.

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.