Advanced Aerospace Engineering Management

Learning Outcomes

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

• The role of inclusivity and diversity within engineering
• The importance of risk and security within engineering
• Ethical practice within engineering

Subject Specific Practical Skills

Having successfully completed this module you will be able to:

• Manage uncertainty within a project management context
• Model, manage and optimise for the reliability/uncertainty of an engineering system

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

• Interpret the results of a model predicting reliability or uncertainty
• Explain the working principles behind the methods covered throughout the module and appreciate their strengths and limitations

Learning Outcomes

Having successfully completed this module you will be able to:

• M1 Students develop a comprehensive knowledge of applied statistics and its associated mathematical principles to the assessment of robustness and reliability of engineering systems within the coursework and present an awareness of the limitations of these techniques. M2 As part of the coursework and examination students develop statistical models from provided censored/incomplete data sets. The resulting models are then used to develop larger, more complex, models of system level failure probability which students then use to assess, for example, component importance, the impact of design changes, optimal maintenance schedules etc. M3 Through both coursework and examination students apply appropriate computational and analytical methods in the assessment of, for example, system level reliability problems, robust design optimisation and uncertainty management. Limitations of these techniques are qualitatively discussed and quantitatively evaluated through the calculation of appropriate confidence bounds. M5 Through examination and coursework students consider the impact of risk and reliability on the design of a complex system. Via examination students discuss inclusivity with respect to design. M7 The statistical models for system reliability developed by students through either examination or coursework are used to, for example, minimise through-life system downtime or maintenance costs. M8 Through examination and supported by formative class discussion, students analyse and discuss an ethical dilemma within the framework of the Royal Academy of Engineering’s statement of ethical principles. M9 As part of the examination students have to use statistical methods to evaluate risks with respect to system performance (reliability) or project/business management and then use this information to identify options and mitigate these risks through either changes to design or changes to the allocation of project resources etc. M10 As part of the examination students identify and mitigate common risks to the overall security of a business. This includes, for example, cyber security principles, business continuity, intellectual property and the supply chain. M11 As part of the examination and supported by formative class discussion students discuss the benefits of equality, diversity and inclusion within wider engineering practice and it’s relationship to the overarching statement of ethical principles. M14 As part of the examination and coursework students apply modern robust design methods to the management and impact of design uncertainty within the content of a complex engineering problem. M15 Via examination students apply stochastic methods to the management of project changes e.g. scheduling. As part of the examination students apply their knowledge of IP rights in the discussion of a case study.

Statistical Fundamentals for Management

• Probability theory, rules & notation, sequence trees, Bayes’ Theorem
• Single & multi-variate PDFs and CDFs
• Statistical model fitting, method of moments, least squares & maximum likelihood estimation (censored & uncensored)
• Monte Carlo & Quasi-Monte Carlo analysis

Systems Reliability Modelling

• Series, parallel, m-out-of-n systems, and balanced systems
• Reliability block diagram analysis
• Block diagram decomposition
• Active & inactive redundancy
• Determining component importance
• Fault tree analysis
• Competing risk models
• Load sharing systems
• Optimal maintenance scheduling

Management of Uncertainty, Risk & Security

• Business continuity planning
• Project risk modelling & management
• Supply chain uncertainty and management
• Task scheduling & resource planning
• Designing for reliability (inc. FMEA)
• Designing in the presence of uncertainty / robust design
• Robust regularisation, aggregation & randomised techniques for robust design
• Cyber security
• Intellectual property management & export control
• Concurrent & collaborative design processes

Engineering Ethics & Inclusivity

• Ethical practice including, accuracy & rigour, honesty & integrity, responsible leadership and respect for life, law and the public good
• Case studies in engineering ethics
• Diversity & inclusion
• Design for inclusivity

Teaching and learning methods

The module features a series of lectures covering the above syllabus in addition to tutorials covering worked examples, real world case studies and guest lectures. Background reading, pre-recorded material and self-study complement the face-to-face elements of the module.

Assessment strategy

(Summative) Assessment Method:

• Coursework 20%
• Examination 80%

(Referral) Assessment Method:

• Examination 100%

External Repeat Assessment Method:

• Examination 100%

Summative

This is how we’ll formally assess what you have learned in this module.

Repeat Information

Repeat type: Internal & External