Systems Engineering MSc

• Dr Steve Barker

As a foundation for the degree, an introduction into systems science, systems philosophy and systems thinking in specific relation to engineered systems. The module will provide the theoretical basis for the remainder of the degree.

Unit 1: Systems Science

• Overview of systems science, philosophy and systems theories. Open system,
• Definitions and General Systems Theory, why and how can we apply systems ideas to a complex world, system complexity, emergence, viability, resilience, etc,
• Systems Science workshop, exploration and evaluation of key systems science literature.

 

Unit 2: Systems Thinking

• Overview of how system science ideas relate to real world problem and solution contexts,
• Definitions of Engineered Systems, Product, Service and Enterprise Contexts and the relationship between Capability and Solution.

 

Unit 3: SE Life Cycle

• Overview of life cycle principles and standards, introduction to the principles of Model Based SE (MBSE),Life Cycle workshop, SE Life Cycle case studies.

 

United 4: Systems Modelling

• Overview of modelling principles, the role dynamic and static models, overview of different kinds of hard and soft modelling method, introduction to multi method approaches.

 

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

• Outline the nature of systems philosophy and its relationship to the engineering of systems throughout the lifecycle,
• Critically evaluate the role of models in the systems thinking and engineering approach,
• Appraise current approaches to Model Based Systems Engineering (MBSE) and the value of MBSE through the systems lifecycle.

• Dr Tim Ferris

Prepare students to assess and critique the SE activities associated with the Design and Realisation phases of the life-cycle of an existing system and to propose modification to existing methods and the development of appropriate system design and realisation methods for new projects.

Unit 1: System design,

• Review of Model Based Lifecycle,
• Physical architecture,
• Design devolution and specification of sub-systems,
• Choice to use bespoke development, COTS products, or re-use of existing systems/components.

 

Unit 2: Trade Studies,

• Problem of judgements about multi-dimensional system outcomes,
• Methods for performing trade studies.

 

Unit 3: Integration, Verification and Validation,

• Overview of system integration,
• Three approaches to integration: bottom up, top down, middle out,
• Overview of verification,
• Overview of validation,
• Challenges and strategies for effective and efficient verification and validation.




Unit 4: Through life support,

• Planning systems for through life support: consumables,
• System use: design for system readiness, effect of operational demand · Impact of change in environment and/or use patterns

 

Unit 5: Logistics, obsolescence and system retirement,

• Systems engineering aspects of logistics,
• Obsolescence and responses to the challenge,
• Overview of system retirement.

 

Unit 6: Other considerations: Acceptance, training, impact of organisational role allocations,

• Overview of system acceptance process,
• Systems engineering aspects of training needs analysis and training system development,
• Examples of the impact on design of system boundary issues in the architecture of the system and the measures of performance applied to organisations interacting with the system.

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

• Create and sustain cost-effective, timely and effective complex systems through the use of system level analytical tools applicable to the various life-cycle phases from design to retirement,
• Evaluate the contribution of the systems engineering processes to the design, implementation and through life phases of the system life cycle,
• Propose and assess the systems engineering patterns, models, methods and tools needed for a successful integrated SE approach to the design, production, use and retirement of systems,
• Manage the integration of different specialist disciplines, to enable the development of systems recognized as successful through the whole system life cycle.

• Dr Steve Barker

Prepare students to undertake SE activates associated with the Concept Definition and System Definition elements of a Model Base SE approach. Going from Enterprise needs to a logical solution options, Including consideration of dependability and through life plans.

Unit 1: Model Based SE Life Cycle Processes,

Overview of early Life Cycle management and how SE Concept and System Definition are related in a Model Based approach,
Overview of Logical Architecture and Requirements theory and models.


Unit 2: Model Based SE (MBSE),

Introduction to the System Modelling Language (SySML),
Overview of the course MBSE methodology.


Unit 3: Model Based System Definition - Requirements

System Requirements Process outcomes and activities,
Using MBSE models to define System of Interest Functions and Requirements,


Unit 4 Requirements Management,

Principle of Good Requirements Specification and Review.


Unit 5: Model Based System Definition - Architecture:
Logical System Architecture Process outcomes and activities,
Using architecting notations to define Logical Architectures (LA), iterations between LA, Physical Architecture and Mission Analysis.


Unit 6: Module Workshops,

Apply Requirements and LA models in a group student project,
Discussion of MBSE Benefits and Challenges.

Note, for the SEE module these units are covered in weeks 1-6. For the SESD they are delivered over a 5 day residential timetable, with workshops each day.

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

• Evaluate the application of Model Based Systems Engineering (MBSE) Concept Definition and Systems Definition life cycle processes to complex problems,
• Plan and conduct logical system architecture processes to describe one or more whole system solution concepts,
• Plan and conduct Requirements Management processes to create and review Stakeholder and System Requirements,
• Create and review a through life project plan, including consideration of dependable system issues and whole life cost,
• Appraise current approaches to Model Based Systems Engineering (MBSE) and the value of MBSE through the systems lifecycle.

• Dr Steve Barker

The aim of this module is to allow students to explore the problem space using a pluralistic SE problem-exploration approach, modelling the nature of the problem and allowing an initial logical architecture and requirements to be created and reviewed. This will draw on methods, tools and techniques studied during ISSE and PASD

Unit 1: Problem Exploration

• Analyse problem situation and its context/environment,
• Understand the nature of enterprise systems and how they relate to a problem situation,
• Apply SE tools and techniques to explore problem situations.

 

Unit 2: Concept Definition: Mission Analysis (MA)

• Evaluate the application and development of scenario modelling and OA methods to quantify a problem context and support problem exploration,
• Evaluate applicability of SE tools to support MA.

 

Unit 3: Reflection

• Consider the value of high level problem focused modelling might have in helping to identify relationships, issues and possible problem in complex real world situations,
• Consider how the problem focused modelling in this module might integrate into the initiation and conduct of an SE life cycle.

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

• Evaluate the appropriateness of Enterprise modelling and SE tools and methods to an example problem space,
• Formulate and apply a systems thinking approach to suitable areas of consideration,
• Evaluate the challenges of remote group working and other aspects of the professional practice of SE in the conduct of problem analysis and concept definition life cycle processes.

• Dr Steve Barker

Successful integration and practice of systems engineering within a business environment requires a wider knowledge and understanding of the strategic management and business processes of the organisation and wider enterprise. Spanning a wide range of individual disciplines, enterprise management is used to ensure that all business activity is planned, managed and delivered to achieve strategic aims, objectives and goals.

The interdisciplinary nature and wide-ranging applicability of systems engineering means there are inevitable touch points and integration requirements to ensure the enterprise can continue to deliver and meet its overarching goals and requirements.

This module critically examines the relevance of underpinning theories and practice across the enterprise management domain from a systems engineering perspective. Taking a capability- and effects-based context, it aims to provide the systems engineer with extended knowledge of the wider business environment within which the systems lifecycle sits, and how systems engineering application itself requires systems thinking and practice to successfully integrate it within wider business and management processes and approaches

Unit 1 – Enterprises, Architectures and Lifecycles

• Defining the Enterprise as a system, Enterprise SE Project, Programme and Portfolio Management (P3M) Enterprise Architectures

 

Unit 2: Strategy and Mission Analysis

• Overview of how an Enterprise explores possible problems within its overall strategy, working with diverse stakeholders to balance their needs, enterprise goals, technology maturity and wider issues and regulations. Using Mission Analysis (MA) to understand current portfolio.

 

Unit 3 – Operations Management and Systems Engineering

• Project, Programme and Portfolio Management Finance and Commercial Decision Making Capability Thinking Supply Chain Management Risk Management

 

Unit 4 – Capability Management and Stakeholder Needs

• How to identify current and future capability needs and plan change. Expanding Mission Analysis (MA) and scenario modelling and OA to define problem context and stakeholder needs.

 

Note, for the SEE module these units are covered in weeks 1-6. For the SESD they are delivered over a 5 day residential timetable, with workshops each day.

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

• Differentiate the contributions that systems engineering and allied disciplines each make to deliver successful business outcomes,
• Formulate and apply appropriate operations and relationship management theory to solve enterprise systems problems,
• Create Mission Analysis models using scenario based modelling, to explore enterprise needs,
• Create and manage an integrated cross-discipline aware, outcome- focused systems programme in the context of the wider business environment.

The aim of this workshop is to consolidate the material in the taught phase of the Systems Engineering for Defence Capability, providing an opportunity to assess the student’s ability to apply this knowledge to a realistic real-world systems problem.

The module uses learning from all core and some optional modules to allow the implementation, practice and use of learning together in an evolving, example case study.

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

Knowledge,

• Demonstrate a systematic and critical knowledge and appropriate use of advanced Systems Engineering techniques.

Skills,

• Analyse a real-world problem using Systems Engineering approaches and tools as part of a through-life acquisition approach,
• Evaluate the application of advanced Systems Engineering techniques to real-world systems problems,
• Demonstrate the ability to work effectively as part of a group to address and resolve a realistic real-world systems problem.

• Dr Tim Ferris

This module is foundational to the MSc Systems Engineering, which demands the multi-perspective, multi-methodological approach to research taught in this module because of the range of both research questions which can be, and are, addressed in systems engineering research to inform the body of knowledge of the discipline. In addition, a distinctive perspective which we take in our approach to systems engineering is that systems engineering in a process of research to find the appropriate solution to needs, itself demanding a broad range of research methods to provide a foundation of assured knowledge to base project work upon through any and all phases of the system lifecycle.

 

Therefore, this module is a core module of the MSc because the learning achieved will be applied in both the academic setting (the MSc thesis, and possible later studies) and also in the practice of systems engineering in the various workplace setting in which course students and graduates will work.

Unit 1: Knowledge, novelty and verification and validation,

• The classical epistemological view of knowledge: S knows that p if and only if p is true, S believes that p and S is justified in believing p,
• Novelty in research and contrast to what is not novel,
• Potential sources of research topics, areas of interest,
• Purposes for which research may be performed,
• Impact of project purpose on what knowledge is needed,
• Requisites of a research project: a method to discover what is present and a method to provide assurance.

 

Unit 2: Areas of interest and research questions,

• Impact of intention to publish on project design,
• Broad approaches to research: discovery about an observable, improving practice in a field, improving individual or group practice, logic or mathematical proof, experiments, interpretation of extant data, analysis of text/discourse, etc,
• Transformation of an area of interest into an articulated research question,
• Reading research papers to determine research questions and methods.

 

Unit 3: Framing research projects,

• Knowledge and information,
• Logical reasoning processes,
• Deduction,
• Induction,
• Abduction,
• Errors in research,
• Verification in research,
• Validation in research,
• Knowledge and information.

 

Unit 4: Quantitative methods,

• Measurement and scales,
• Common kinds of quantitative research,
• Null hypothesis,
• Sampling.

 

Unit 5: Modelling and simulation method,

• Kinds of models,
• Challenges of physical testing/experimentation,
• Benefits of modelling and simulation in research,
• Relationship of modelling and real things,
• What is achieved through modelling,
• Calibrating models with real cases,
• X-in- the-loop modelling.

 

Unit 6: Formative feedback re proposed research methodology,

• Surveys,
• Interviews,
• Textual analysis.

 

Unit 7: Writing about research: Proposals, reports, theses, and papers,

• Description of research writing genres: proposals, reports, thesis, paper,
• For each genre: general description, generic outline, span of content, emphasis on sections,
• The nature and purpose of literature review in each genre,
• Pragmatic suggestions for writing: outlining, mind-mapping, reference management, document management,
• Creating publishable quality research including discipline specific journals and how to write academically credible and professional reports.

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

• Transform a description of an area of interest into a precisely worded research question, the answering of which will provide knowledge which is useful for the purpose for which the research project is to be conducted,
• Plan and execute a search of the literature to find existing research relevant to a research question at hand and to prioritise a reading list if too much material is found,
• Evaluate existing research literature to propose an apposite method to address a research question,
• Justify a proposed method to address a research project as a suitable method to generate knowledge of the kind that will achieve a result that will satisfy the motivating purpose of a research project.

• Dr Steve Barker

To conduct a self-directed piece of research applying the principles, practices and processes developed in the course to a real world problem of interest and relevance to the student.

• The thesis forms a vital element of the programme of study and offers the student an opportunity to develop and apply the knowledge and skills gained from the course to an agreed topic,
• Students will be allocated an academic supervisor who will guide them in the suitability of topic chosen and conduct of the research.



 

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

• Acquire, organise, discuss and assess knowledge associated with the specific matter the student is investigating,
• Plan, organise and undertake a piece of research with appropriate supervision,
• Evaluate and apply appropriate methods, tools techniques and knowledge to a complex problem,
• Gather and critically appraise data, and to utilise it within the appropriate academic and practical context,
• Prepare a written submission to effectively communicate findings.

Software plays a crucial role in both systems development processes and systems functionality in complex systems development. This makes it important for systems engineers to have understanding of software engineering and the issues surrounding cyber threats and cyber management systems. This module provides students with an understanding of software engineering and cyber systems by covering central principles, methods and tools in the context of systems engineering .

Unit 1: Software Engineering and Systems Engineering,

• Software engineering principles,
• Processes and lifecycle models,
• Software development methodologies,
• Types of software systems.

 

Unit 2: System Analysis and Modelling,

• Software specification,
• System analysis,
• System modelling,
• Software system requirements.

 

Unit 3: Software Architectures and Models,

• Component-based software engineering,
• Distributed software engineering,
• Real-time software engineering,
• Service-oriented software engineering.

 

Unit 4: Cyber Systems and Security,

• Cyber vulnerabilities and threats,
• Web-based attacks,
• Cyber management systems,
• Policies and processes.

 

Unit 5: Integration and Testing,

• Software system integration,
• Verification,
• Validation,
• Developing architecture and requirements workshop.

 

Unit 6: Other Considerations,

• Software obsolescence,
• In-service and disposal,
• Software process improvement,
• Developing integration and test plans workshop.

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

• Assess the role of software engineering and software development methodologies in developing and maintaining complex systems,
• Create software system architectures, models and requirements for a software intensive system,
• Analyse cyber threats and cyber management requirements for developing and maintaining software intensive systems,
• Evaluate various approaches and methods for software integration and testing.

• Sean Price

Megaprojects are large, complex projects that typically have a value of above $1Bn. Once considered rarities, megaprojects are not only large, but growing constantly larger and are being constructed in ever greater numbers. From high-speed rail to modern major defence projects, or from staging the Olympics to implementing national 5G communications networks, megaprojects affect our normal everyday lives and can impact millions of people.

What sets megaproject systems apart from more traditional system is not just their size but their complexity and scope – megaprojects often straddle public and private sector boundaries and are intrinsically linked to the general public. This means that technological, political, economic and societal aspects all coalesce and play an important role - both overtly and covertly – to system success.

This module explores the realm of megaproject systems and expands the traditional systems engineering approaches, thinking and methods to identify and address the complexity and closer integration of hard engineering, design, management and social sciences within a single entity.

Unit 1 – The Megaproject Engineering Mindset

• What are Megaprojects?
• The Megaproject Paradox,
• System of Systems Engineering for Megaprojects,
• Systems Thinking in a Megaproject Context.

 

Unit 2 – Planning and Delivery

• Megaproject Design,
• Megaproject Lifecycles,
• Systems Integration at the Megaproject Level,
• Megaprojects and Risk,
• Benefits Management in Complex Environments.

 

Unit 3 – Towards the Boundary and Beyond: Context

• Behavioural Economics,
• The Political Dimension – Drivers, Blockers and Influencers,
• Macroeconomics,
• Risk Redux.

 

Unit 4 – Accounting for Change

• System Evolution Dynamics,
• The System Evolution, Project Control and Agile Approaches Trade-off,
• Culture, Competition and Geopolitics,
• Ethical Aspects of Megaprojects,
• Teraprojects.
  
  

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

• Extend established systems engineering approaches to the megaproject domain,
• Assemble and integrate appropriate methods and tools to account for socio-economic and political influences within the megaproject systems lifecycle,
• Formulate and defend a megaproject-level systems engineering management plan,
• Identify, evaluate and mitigate system evolution and dynamically generated risks,
• Translate, summarise and communicate critical systems engineering elements of megaproject management to non-systems engineers to maximise project success.

• Dr Tim Ferris

Prepare students to analyse the life cycle cost and importance of resilience as a function of time of systems to enable judgments about the value for money afforded by design alternatives. This module will enable students to work at the interface of the systems engineering concern with the system and the business concern with Return on Investment.

Unit 1: Foundations,
Value and utility – financial value and cost of systems and benefits provided in the application domain,
Alternatives in design and engineering,
Interest, compound interest, time value of money,
Formulae for equivalent value over time.


Unit 2: Methodology,
Life cycle cost situation,
Life cycle cost analysis,
Cost over the life cycle,
Estimating cost – methods analogy, parametric methods, accounting, data, reference class forecasting.


Unit 3: Errors in estimation and sensitivity analysis,
Errors in estimation – methods to describe,
Sensitivity analysis – single and multiple alternatives,
Monte Carlo analysis and applications.


Unit 4: Additional Factors Affecting Decisions,
Depreciation (real and book value),
Taxation effects on analysis,
Projects with no financial return (typically government asset and service provision),
Inflation.


Unit 5: Application to complex System Planning,
Optimisation of investment decisions,
Planning of fleet size,
Economic life calculations.


Unit 6: System Life Cycle Events,
Effect of change of scenario during life,
Valuation of system resilience.

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

• Apply the time value of money concept to judge project Return on Investment,
• Construct complex engineering economics analyses of proposals to determine Return on Investment in complex, multifaceted, scenarios,
• Evaluate the impact of design options on the value of the system resilience afforded by those option through the system life cycle,
• Perform analysis of engineering economics problems with uncertainty, cost estimation and other limitations to data completeness and quality,
• Analyse scenarios with return in either or both the financial and physical domains.

• Dr Tim Ferris

Prepare students to specify dependability and resilience characteristics of systems, and to assess the dependability and resilience of system proposals and implementations as part of the verification and validation review of systems, and as part of the management of existing systems.

 

Unit 1: Foundational concepts of dependability,

The concepts of function, failure, fault and defect in the context of reliability, maintenance, maintainability, availability and resilience.
Relationship of availability, reliability and maintainability and systems engineering,
Contracting for availability, reliability and maintainability.


Unit 2: Reliability and maintainability,

Mechanisms of failure and their mathematical description,
Concepts and introductory analysis of fault tree analysis,
Corrective and preventive maintenance,
Maintenance strategies to provide equipment repair, overhaul and Through Life Support,
Level of Repair Analysis,
Failure Modes Effects and Criticality Analysis.


Unit 3: Availability,

Measures of availability,
Intrinsic Availability,
Achieved Availability,
Operational Availability,
Discussion of the relationship between the different views of availability in the three measures.


Unit 4: Introduction to Resilience,

Overview of resilience,
Concept of threat,
Time phases of resilience: before, during, after threat events,
Type A and Type B threats,
Active and latent threats,
Relationship of resilience and other specialty engineering fields.


Unit 5: Resilience Schools of Thought (1)

Hollnagel School of Thought based on safety and organisational response,
Protection School of Thought focused on identifying high impact threats and providing methods to protect from their effect.


Unit 6: Resilience Schools of Thought (2)

Engineering for Resilience through use of design heuristics,
Measurement of resilience – diverse approaches to measurement of resilience and the implied definition and view of resilience.

• Apply systems thinking knowledge to articulate the dependability and resilience issues associated with particular systems,
• Analyse the cost, availability and effectiveness of systems given the characteristics of the elements and specify the characteristics of the system elements to provide intended system attributes,
• Evaluate the contribution of the systems engineering processes and methods to the achievement of dependable and resilient systems,
• Plan and evaluate the systems engineering methods required to provide dependable and resilient systems,
• Analyse complex systems properties that enable dependability and resilience to ensure they are appropriately addressed across the life-cycle.

• Dr Fanny Camelia

Provide students with an understanding of the challenges raised by the consideration of humans in systems, and the tools and techniques for considering human system issues across the SE life cycle as part of a Model Base SE approach.

Unit 1: Human Systems Engineering Introduction,
Review of system, human-machine system and systems engineering,
Overview of human factors contribution in systems engineering,
Introduction of some key terminologies, HF, HFE, ergonomics, HFI and HSI and the history of their development.


Unit 2: Human Considerations in Engineering,
Overview of human factors areas that need considerations in Engineering System
The application of human factors in rail and highway industry,
The application of human factors in defence industry.


Unit 3: Human Cognitive Aspects and Organisational and Cultural Issues,
Overview of human cognitive aspects in complex socio-technical systems,
Overview of cultural and organisational behaviour aspects in complex socio-technical systems,
Overview of human requirements.


Unit 4: Human Factors Methods,
Human factors methods and tool and their relationship with systems engineering,
Human factors methods for supporting human systems engineering,
Systems engineering methods for supporting human systems engineering.


Unit 5: Human Factors in the SE Life Cycle,
A general discussion of the mapping HF Methods to the SE life Cycle,
Suggested generic HF activities and views, as part of the MBSE methodology presented earlier in the module,
Example of the HF extension to the MBSE methodology.


Unit 6: Human Factors Integration to SE Workshop,
The application of SE methods, including MBSE, for supporting Human Systems Engineering,
The application of HF methods integration into the SE life cycle for supporting Human Systems Engineering.

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

• Assess the ways that humans are considered and modelled in complex socio-technical systems,
• Evaluate the wider organisational and cultural drivers which affect the development of complex systems,
• Create human factors models to support Problem Analysis and System Definition,
• Create human factors models to support System Design and Realisation.

• Sean Price

To equip students with a sound understanding of the dynamic modelling of complex systems through the use of the System Dynamics methodology.

Unit 1: The System Dynamics Approach,
Dynamical systems, complexity and change. Closed systems and their behaviour. Structure as a driver of dynamic behaviour. Building candidate understanding models and testing them. Reference modes. The "twin pillars" of SD – feedback thinking and computational modelling. SD as a paradigm to support systems thinking,


Unit 2: Qualitative SD,
Seeing complexity as an emergent consequence of cause and effect. Visualising the interdependencies within complex systems using feedback structures and causal loop diagrams. Considering policy interventions as points of leverage. Developing understanding of complex systems and communicating it usefully. Relationship to other soft methods,


Unit 3: Quantitative SD,
Simulating feedback systems. First order, second order and higher order feedback systems. Suggesting and testing policy interventions for behavioural improvement. Theoretical underpinnings of SD modelling, including its background in control theory. Introducing delays. Formulating equations, using data and estimating parameters. Relationship to other computational simulation methods,


Unit 4: Implementing the SD approach,
Building useful models. Verification and validation of simulation models. Case studies and a detailed modelling exercise.

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

• Relate complex system behaviour to system structure, appreciating considerations such as cause and effect, feedback and delay within systems,
• Develop convincing and compelling qualitative models of problem systems and use them for beneficial communication with system stakeholders,
• Use computational modelling and simulation to explore the consequences of such models and to develop useful policy interventions to improve system behaviour,
• Combine the dual aspects of qualitative and quantitative system dynamics to study suitable problem systems and to make recommendations for system improvement.

To allow students to assess the role of simulation modelling across the systems engineering lifecycle, from concept through to disposal.  The module will enable students to critically assess the roles and applications of simulation modelling and to analyse issues related to quantitative modelling such as data availability and model verification and validation.  It will also allow students to create and evaluate simple models.

Unit 1: Foundations of Simulation Modelling

• The fundamental concepts of simulation modelling and how simulation is used in the SE lifecycle.
• The theoretical basis of quantitative modelling
• The role of modelling in supporting decision making.

Unit 2: Appreciation of Modelling in the SE Context

• Model types; discrete and continuous, stochastic and deterministic.
• Model appropriateness: the verification and validation of models, data considerations, model fidelity
• Model interoperability and networking.

Unit 3: Introduction to Modelling Paradigms

• Discrete event simulation
• System dynamics modelling
• Agent based models
• Virtual reality

Unit 4: Model building and analysis

• Introduction to simulation methods and tools
• Simulation as experimentation.

• Dr Steve Barker

As products, services and solutions become ever more detailed and interconnected in their nature, ideas of complexity become ever more important in understanding the structure and behaviour of these artifacts. Moreover, as these artifacts are required to alter and adapt to changes in requirement, use and context, it is important that a means to allow them to be adapted as rapidly as possible is considered during their development and use lifecycle. Therefore, the idea of Complex Adaptive Systems (CAS) is necessary to meet the challenges in developing and supporting products, services and systems in an ever-changing world. This module will consider CAS in the context of current industrial and consumer need and appraise how SE methods and methodologies such as Agile can be used to describe, characterise, and implement them. This will be placed within the context of the systems lifecycle.

Unit 1: Understanding Complex Adaptive Systems (CAS),

Examine the nature of complexity in systems,
Analyse the meaning of complex adaptive systems and consider the different forms that they might take,
Appraise how systems engineering can facilitate the development and support of CAS.


Unit 2: Complex Adaptive Systems Thinking,

Evaluate how a complex adaptive system (CAS) can be characterised and described within representative business and enterprise contexts,
Evaluate how SE methods and tools can help characterise CAS.


Unit 3: Complex Adaptive Systems and Agile,

Evaluate the worth, strengths and weaknesses of Agile methodologies such as SCRUM and SAFe in facilitating CAS,
Apply agile methods to a representative case study,
Reflect on the use of agile methods.


Unit 4: Design for Complex Adaptive Systems (DfCAS),

Analyse what is needed to institute a design philosophy that will support the development and implementation of CAS,
Create a logical architecture to support DfCAS for a representative case study,
Reflect on the extent to which architectures can be made reconfigurable.


Unit 5: Through Life Planning,

Evaluate the effect of Complex Adaptive Systems (CAS) on Through Life Systems Engineering and Management (TLSEM),
Devise a representative systems lifecycle for CAS.

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

• Evaluate the nature of Complex Adaptive Systems (CAS), and how it is relevant to systems engineering,
• Devise an appropriate systems lifecycle for CAS,
• Apply methods and methodologies to an example CAS problem situation,
• Create and review a reconfigurable logical architecture which facilitates CAS within systems development and usage,
• Evaluate the challenges of dealing with CAS in business and enterprise processes.