Composable design methods provide a systematic approach to the combination of existing system elements into a range of system alternatives that can be quickly evaluated and compared in the context of a nominated organisation cost function. Value driven design (VDD) is becoming increasingly prevalent in the development of complex materiel systems. This sees performance requirements replaced with objective functions that equate organisational value.

Models and simulations have always played an
important role in engineering and systems engineering.
Physical scale models, full-sized models, and computer
models are commonly used in all forms of engineering
and design. In recent times, interest in modelling has
increased to span the full system lifecycle and there has
been a significant focus on Model-based Systems
Engineering (MBSE). The extension of formal modelling
into all phases, and particularly the conceptual design
phase, of a system development is a significant step and

The benefit to research staff and students from the cross-fertilisation of ideas is well recognised and UNSW Canberra actively pursues strategies to promote collaboration across schools, other faculties of UNSW, Defence, government organisations and universities/research organisations in Australia and overseas. Many staff and students are already involved in research collaborations with academic, research, industrial and government organizations.

System robustness and future proofing are two essential aspects for any system design. However, it is common for designers to consider only one of these elements on the assumption that meeting one aspect will fulfil the requirements for the other. For many systems, particularly defence systems, with long life cycles and diverse operating environments, it is essential that both robustness and future proofing requirements are accommodated in system design.

Successful project management is defined by the ability to deliver on time, on budget, and with the quality and specifications required. In its simplest form, projects can be viewed as a series of activities. In large-scale complex systems, project activities involve a multi-tier and distributed network of development processes, resources, material and information flows, managerial mental models, and decision making. Mismanaging parts of this network may lead to knock-on or ripple effects on the project’s behavior and performance, such as risks of budget overruns and major delays.

Complexity measures are informative indicators that can potentially aid designers and system engineers in decision making process in all system life-cycle stages: problem definition, requirements analysis, conceptual design, configuration design, detail design and optimization, and integration and testing. The guiding principle here is that of 14th Century philosopher, William of Okham. This principle, better known as Okham's Razor, states that "objects may not be multiplied beyond necessity" or simple is better. This is a generally accepted principle, but why is it so?

CBP facilitates organizations to systematically develop capacity to achieve their business objectives in highly uncertain, dynamic and competitive environments. However, the successful implementation of CBP framework and realization of its full potential is not possible without the development of sophisticated analysis techniques that can identify, model, optimize and subsequently support integration of the optimized models of all linear and non-linear processes, functions and operations within an organization.

Whereas there are many claims in the literature about the potential of using an ST/SD approach to understand and learn about complexity, this is still limited to being a largely untested hypothesis with little empirical evidence, much of which is inconclusive. This status not only raises scepticism about the value of ST/SD, but also implies a wasted learning opportunity for the field to grow and improve.

First, they are and must be a simplified representation of the modelled system. To be useful, models need to provide a cognitively-mediated environment to explain the systemic behaviour. If a model keeps growing in complexity, it will be difficult to understand and use. Yet, in many applications, large models are inevitable to support system understanding, and decision making at the appropriate and acceptable level of detail This has been long recognized as the 'modelling paradox' (Bonini, 1963).

Modularity (the degree to which a system's components may be separated and recombined) is a therefore principal notion for all engineered systems, so much so that the need for modularity is almost taken for granted. In the context of system development, Baldwin and Clark (2006) suggest three natural purposes for modularization: to make complexity manageable, to enable parallel work, and to accommodate uncertainty.

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