This module will introduce students to industrial control systems from sensors to controllers. PLC and PID control will be covered as well as the standards used in automated machine building.

1. Discuss the applications of industrial control systems considering capability, cost, efficiency, monitoring and integration with other systems.

2. Investigate control strategies for a particular application.

3. Compare sensor technologies and select the appropriate one for a given application.

4. Set up and program an industrial control system.

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. Critically evaluate the role of decision theory in enhancing employee and organisational performance as well ascontributingto sustainable development goals

2. Evaluate different decision-making methods, tools, visualisations and interactive dashboards

3. Contrast the different data analytical, data visualisation tools and methods used by organisations

4. Consider risk and uncertainty issues in decision making

5. Critically evaluate different methods for managing risk and uncertainty

6. Appraise how digital transformation can impact decision making and analysis

This module will provide students with knowledge of robotic automation systems, including the use of vision systems in robotic applications. Students will learn how to integrate components in a robotic cell and program the cell using the teach-pendant. A 3D modelling software will be used for programming, optimisation and virtual commissioning of robotic cells. Finally, the module will cover cost and safety considerations when the automation of a manual operation is proposed.

1. Investigate the industrial uses, feasibility and cost-effectiveness of robotic systems.

2. Use a teach-pendant or a 3D model of a robotic cell for programming, optimisation and commissioning.

3. Use vision systems for robotic industrial applications.

4. Investigate and applysafety standards relating to a robotic arm application.

Machine vision is concerned with image-based automatic inspection and analysis of industrial products. The module introduces the fundamental ideas to setup and deploy vision applications for a modern manufacturing environment. This module balances theory with practical applications, using industry standard software and hardware for implementation and deployment.

1. Illustrate and discuss the working principles and applications of a vision system in a modern manufacturing environment.

2. Specify and select an industrial vision system for a given application.

3. Apply a range of image processing techniques for inspection and analysis of objects.

4. Develop, implement and optimise the environment setups for an industrial vision system.

5. Assess ethical, business and technical challenges in the deployment of a machine vision system.

Upon completion of the module, the student will understand the transition from a traditional enterprise in-house environment to a Cloud based enterprise environment. This involves an examination of concepts such as virtualization at each layer – compute, storage, network, desktop, and application – along with business continuity in a Cloud environment. The student will understand Cloud computing fundamentals, infrastructure components, service management activities, security concerns, and considerations for Cloud adoption.

Current developments with respect to IS technologies and their impact on business models will also be examined; the student will have a knowledge of significant new technology approaches.

1. Evaluate the traditional Enterprise Infrastructure.

2. Identify and implement a Virtualized Storage solution.
3. Design and develop virtualization technology of compute, storage, network, desktop and application layers of IT infrastructure.
4. Analyse business continuity solutions in a virtual data centre

5. Analyse the key considerations for migration to the Cloud

6. Investigate the emerging technology environment for manufacturing.

This module introduce the learner to Digital Twins, which are a virtual replica of the manufacturing process, and can, not only speed up the planning of a new product, but also support training, continuous improvement and maintenance planning when combined with IIoT and other technologies like AR and VR. Learners in the module will gain practical experience on building a digital twin and extracting data to support decision-making.

1. Appraise the benefits of adopting digital twin technologies when planning a new manufacturing process

2. Discuss how Industrial Internet o Things (IIoT) and digital twin technology combine to give a livemodel of a manufacturingprocesses

3. Investigate the use of Virtual Reality (VR) and Augmented Reality (AR) for the design, optimisation and planning of manufacturing processes

4. Develop a 3D model of a manufacturingprocess

This module will look at how the product flows through the manufacturing plant. Students will learn to optimise the process using the lean methodology. They will be introduced to technologies used to trace and handle material/products through the manufacturing process.

1. Optimise the factory flow using lean engineering techniques.

2. Select the appropriate technology for optimised traceability.

3. Investigate the use of AGVs, AMRs and intelligent conveyors in process flow.

4. Select and programme a Cobot for a given application.

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. On a practical level students will build a SCADA system integrating data from various equipments.

1. Assess the existing data architecture of a manufacturing plant and its components.

2. Design specification for a data architecturebased on user requirements consideringsustainable development goals.

3. Plan horizontal and vertical integration of a data architecture in a manufacturing system.

4. Develop a data management system at SCADA level.

5. Develop anintegration plan considering validation.

This Industrial Networks module introduces the fundamentals of networking technology for industrial applications. The module balances theory with practice, applying networking principles and techniques in practical laboratories.

1. Investigate current industrial network topologies and architectures

2. Assess and select a network protocol for a given application

3. Design and build a basic network fora practical application

4. Plan the integration of a network

5. Evaluate the performance of a network

This module covers the different actuators used in industrial automation from applications, specifications, selection to control.

1. Investigate the applications of actuators

2. Specify and select anactuator for a given application

3. Specify and select the control strategy for a given actuator

4. Develop a control system for a specific motion application

Students will learn to control motion systems and analog process variables using PLC technology.

1. Develop an application to control motion through a PAC/PLC.

2. Develop an application to control analog process variables using a PAC/PLC.

3. Develop an application to control a Variable Frequency Drive (VFD)using a PAC/PLC.

4. Design anautomatedapplication that uses advanced features of PAC/PLC.

This module is designed as an introduction to Database Design and Development techniques.

1. Design a relational database schema for a software application
2. Devise a set of relational tables and develop a relational database.
3. Query a relational database using SQL
4. Evaluate the use of non-relational data storage technologies
5. Develop a prototype of a non-relational database model

This module is industry based. Students will use the knowledge, skills and competences acquired in the programme to assess the digital maturity of their company (or part of) and present a business case or a research proposal that will support digital transformation in their company.

1. Assess the digital maturity of a company using the knowledge acquired in the programme.

2. Independently conduct research in a particular field of digital transformation.

3. Identify where and how digital transformation can support the vision of the company.

4. Develop a project plan which modularises the project into work packages.

5. Identify resources required to complete the project.

6. Prepare a business case or research proposal that justifies investment in some aspects of digital transformation.

7. Present their work in a professional manner.