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This module aims to provide the ability to design and develop digital visualisation platforms that enable agile decision-making capability.

• Introduction to visualisation methods.
• Awareness of human machine interfaces and the associated challenges and solutions.
• Communication skills for effective illustration and collaboration on complex results.
• Design and develop dashboards, virtual and augmented reality demonstrators.

On successful completion of this module, you will be able to:

1. 1. Appraise different methods of visualisation for detailed data analysis.
2. 2. Evaluate human-computer interaction methods and their relevance to visualisation.
3. 3. Assess technical narrative and consolidated information for knowledge exchange and effective decision making.
4. 4. Justify the use of dashboards, virtual and augmented reality for adaptive visualisation through case studies.

This module will provide the skills to be able to integrate a set of digital technologies and solutions for a wider application and offer the means to test the integrated solution for assurance purposes.

• Introduction to system integration methods.
• Awareness of technical standards and the associated requirement and implementations.
• The processes for test case development and developing a test management plan.
• Understanding of safety and mission assurance and associated QA processes.
• Critical evaluation criteria and risk assessment strategies for digital engineering.

On successful completion of this module, you will be able to:

1. 1. Justify with sufficient evidence the efficient integration of a set of digital engineering technologies and solutions.
2. 2. Appraise testing standards and qualify their relevance to quality assurance (QA) processes.
3. 3. Compare, contrast and develop test management strategies for new digital technologies and solutions.
4. 4. Critically evaluate safety and mission assurance for a digital engineering technology and solution.

This module provides the skills to choose and justify new digital technologies and solutions by implementing appropriate requirements analysis, technology road-mapping and strategy development considering alternative targets such as return on investment and customer value.

• Introduction to digital engineering including digitalisation vs digitisation vs digital transformation.
• Introduction to requirements capture and systems engineering.
• Justifying Prevent, Safeguarding and British Values in the context of the digital technologies and solutions that will be considered for adoption.
• Building awareness and justification of digital technologies and solutions.
• Technology road-mapping to evaluate future potential technological developments.
• Return on Investment analysis in the context of digital technology and solutions.
• Developing a strategic plan for digital transformation and the future work environment considering the role of technology leadership, change management and continuous improvement.

On successful completion of this module, you will be able to:

1. 1. Justify appropriate methods for requirements capture for digital technology and solutions within the system of system context.
2. 2. Appraise the opportunities that digital engineering offers by developing roadmaps for alternative digital technologies and solutions.
3. 3. Critique and design methodologies to evaluate and prioritise digital technologies for alternative requirements including return on investment.
4. 4. Critically evaluate human-machine collaboration in the face of automation and manual tasks in industrial settings.
5. 5. Develop a strategic plan to seize the potential benefits of digital engineering via workplace transformations whilst considering a variety of factors such as ethics, human factors, IP, culture, sustainability, and value.

This module focuses on providing the skills to design and develop federated digital twin systems that are integrated in terms of their data, models and visualisation.

• Introduction to digital twins and demonstration of use cases.
• Introduce the key enabling technologies for digital twins -such as ontologies, AI, and IoT.
• Design detailed digital twin architectures including solutions for interoperability.
• Standards available to design and develop digital twins.
• Develop digital twin demonstrations considering the spectrum of data, model and visualisation interfaces.
• Demonstrate the added value that digital twins can offer.

On successful completion of this module, you will be able to:

1. 1. Appraise the contextual need for digital twins and design the digital twin architecture justified by suitable requirements and organisational benefits.
2. 2. Compare and contrast alternative digital twin architectures, which meet the functional and strategic requirements.
3. 3. Justify efficient use of digital twins considering human needs in the context of seamless data, model and visualisation interaction.
4. 4. Construct suitable resilience methods that enable continuous use of digital twins.
5. 5. Evaluate the added value generated from digital twins with a view to offer workplace transformations.

This module provides the skills to design and develop integrated data management approaches and systems to address data related challenges, including managing large volumes of data from disparate sources, identifying and resolving data quality issues, handling disparate data lacking integration and generating insights for agile decision making.

• Introduction to software programming with a view to developing data management systems.
• Evaluate existing standards related to data management.
• Apply methods for data needs analysis.
• Establish mechanisms for enabling connectivity of data acquired from alternative sources – e.g. people, sensors, 5G, IoT.
• Develop data structures and approaches to data modelling using ontologies and reference architectures.

On successful completion of this module, you will be able to:

1. 1. Assess the system requirements for the integration and accessibility of data to deliver value in complex systems.
2. 2. Critically evaluate existing approaches to acquire data from fixed and mobile sources.
3. 3. Appraise strategies and techniques to measure and optimise the quality of data.
4. 4. Construct efficient data structures enabled by ontologies and reference architectures to allow continuous and standardised data flow.
5. 5. Justify mechanisms for allowing connectivity of data to enable links to models and visualisation platforms.

This module will provide the processes to design and develop artificial intelligence (AI) based approaches to be trained for data analytics on a spectrum of data types (e.g. messy data, data gaps or big data), whilst also considering the ethical implications.

• Theory of data analytics, AI, ML, data mining, statistics and supervised learning, e.g., probability, decision trees, regression and classification.
• Experience of real-world AI/ML applications, in areas such as engineering, business, social media, medical data and financial data.
• Evaluate alternative ethical considerations including human-machine collaboration that are related to the use of AI/ML.
• The opportunity to work on industry problems that can benefit from AI/ML approaches.

On successful completion of this module, you will be able to:

1. 1. Compare and contrast data analytics methods including machine learning (ML) in terms of its current and future concepts, principles and theories.
2. 2. Construct ML concepts and methods to impart innovative problem-solving skills in a variety of data maturity scenarios.
3. 3. Evaluate value creation opportunities from ML, develop value propositions and revenue models for businesses and organisations.
4. 4. Construct data analytics based methods for real world problems with the changing nature of digital technology infrastructure and varying volume and quality of data.
5. 5. Appraise ethical responsibility considering human-machine collaboration in data analytics by reflecting on intelligent systems that benefit society.

This module will enable you to compare and contrast alternative approaches to servitisation. It will also offer approaches involving digital technologies and processes to design and deliver servitisation approaches.

• Introduction to servitisation and the alternative models of delivery.
• Compare and contrast alternative types of servitisation for different scenarios.
• Evaluate alternative digital technologies and processes that can be used for the design and delivery of servitisation.
• Provide case studies to reflect on good/bad practices and how to learn lessons from these within the context of servitisation.

On successful completion of this module, you will be able to:

1. 1. Compare and contrast alternative contractual options for servitisation in different scenarios.
2. 2. Appraise organisational transformation processes (including human factors) to evaluate the likely success of servitisation.
3. 3. Justify appropriate digital technologies and solutions to enable robust servitisation.
4. 4. Critically evaluate the pros and cons of digitalisation in the context of servitisation through case study analysis.

This module will provide the skills to apply emerging digital technologies and solutions in the context of cost engineering with a focus on optimising the lifecycle cost and value.

• Cost engineering principles, estimation techniques, processes, commercial tools and software.
• Creating value and enhanced sustainability, through advanced modelling and simulation for cost engineering.
• Real-time and stochastic cost modelling, risk analysis and uncertainty management using digital platforms.
• Big data, cost ontologies, knowledge-based systems, machine learning and AI for cost engineering.
• Cost visualisation, validation and reporting.

On successful completion of this module, you will be able to:

1. 1. Appraise the benefits that can be gathered through cost engineering (e.g. cost reduction, sustainability growth, optimised performance).
2. 2. Assess the role of data in cost engineering using appropriate data science methods and techniques such as ontologies and natural language processing.
3. 3. Justify the next generation of modelling, and simulation (e.g. machine learning) in the context of developing robust cost estimates.
4. 4. Evaluate risk and uncertainty in cost estimates using emerging dynamic modelling and simulation approaches such as agent-based modelling and machine learning.
5. 5. Assess the potential of continuous improvement in value creation and in achieving targets in projects/programmes through cost engineering.

This module will provide the skills to be able to build a system simulation architecture and associated model for agile decision making within an enterprise context.

• Introduction to modelling including overview of simulation methods and techniques.
• Simulation design and development.
• Root cause analysis and risk management for digital engineering.
• Business process analysis and outcomes prediction.
• Environmental sustainability analysis.

On successful completion of this module, you will be able to:

1. 1. Appraise different methods for business systems simulation design.
2. 2. Justify the use of simulation models to address significant decisional needs in business management.
3. 3. Compare and contrast the performance of alternative digital business processes through case studies.
4. 4. Construct alternative decision-making models for business process optimisation through case studies.