Loughborough University
Leicestershire, UK
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Technology alone cannot provide the answers to solve the Global Grand Challenges. This is where systems engineering fits in, because it brings together high-value engineering disciplines with the purpose of delivering transformational changes or new approaches, wherever needed. The term ‘systems engineering’ is used here, to indicate a broad area of relevant activities that include complexity science and systems science; and has deliberately steered clear of a precise definition.

The Grand challenges for Systems Engineering Research are about looking into complex systems their emergence, organisation and behaviour and the utilisation of tools including modelling and visualisation to understand their complexity, document their evolution and access the relevant information about them.  Establishing a set of indicative grand challenges for systems engineering has the objective of inspiring a research agenda and bridge the gap that exist between academic knowledge and industrial applications. In order to do this a thoughtful exploration on the features of complex systems and the approaches that are followed to understand them is initiated through a series of easily recognisable grand challenges. Their achievement is regarded as a major milestone or breakthrough in the advancement of knowledge or technology.

On-going research on systems engineering has resulted in a draft of seven Grand Challenges presented here with no particular rank or order (because of the different priorities of the several sectors to which they apply). However, two basic types of complex systems are noted: heterogeneous and autonomous.  Briefly explained, heterogeneous systems are formed by multiple-interconnected systems and involve humans in the loop. Autonomous systems are independent of human interaction. The other remaining five challenges are applicable to either system; they are generic although the extent of their application will clearly depend on particular system in question.

Business Case, Value Proposition and Competency Development

Although the benefits from adopting a systems engineering approach across most sectors is evident to many industrialist and academics, many senior managers are reluctant to incorporate a systems methodology into their processes, as they are yet to appreciate the benefits.

The first grand challenge is therefore to communicate case study material where systems engineering tools have been successfully applied. However, a persuasive argument for the adoption of systems engineering should take into account details of costs for the transition and preparation of skilled systems engineers.

Grand Challenge
Clearly articulated business case and value proposition for systems engineering adoption. Evidence of the value of systems engineering though case studies and with the support of a network of systems engineering practitioners from both academia and industry.
Details of costs of transitioning to systems engineering.
 Documented engineering methodology where systems engineering is at the heart.
Availability of excellent case studies. Publically available showcase material that shows how systems engineering can contribute to analyse, understand and manage complex systems in terms that senior managers (non systems engineering people) can understand.

Appropriately skilled systems engineers

Development of a frameworks for Systems Engineering competency based on the work of institutions like the International Council of Systems Engineers and Royal Academy of Engineers.



Through Life Information and Knowledge Management

Design of  complex systems can originate vast amount of information that includes the inherent design and aspects of systems operation during a long service life, which can extend in some cases to several decades.  The development of a system that allows searching, storing and securing information, including methods of annotating the information to permit rapid retrieval and interpretation of data is very necessary.

Most complex systems have a lifespan that extends two or three decades. During this time the information related to the system can grow exponentially. The information can involve the original design, the decisions taken, the final product and in-service data. At a small-scale level such information could also include diagrams, photographs, minutes, and associated emails.  A particular problem is to consider that certain decisions in a system are taken against a specific requirement or context but over a period of time the original basis for a design may be forgotten or lost as personal moves or retires. For this reason it is very important to develop methods to store and manage information so that it is possible to access it at the lowest possible level as well as at a high level of abstraction to satisfy a particular query.  Several challenges within this context are: Managing access to sensitive commercial data in a product development enterprise, the effective use of distributed databases and the creation of an electronic diagnostic consultant that is able to search the data for relevant information.

Grand Challenge

Through life information and knowledge management

Model based information/knowledge management system

Reliable and efficient storage of data from a vast information repository

Ensuring data is secure and reliable

Protection of information provenance

Ensuring what is real remains real data

Incorporation of legacy data into the system

Retrieval of information in an intelligent and usable manner over the full lifecycle of a product's life

Improved security of data

How is the data managed and what happens if it changes

Secure cloud based data storage

Creation of an electronic diagnostic consultant that is able to search the data for relevant information
Manage access to sensitive commercial data in a highly distributed product development enterprise
Effective use of distributed database



Modeling and Simulation (M&S) - Total Representation

Modelling and simulation support the design of systems allowing testing of solutions and assessing systems performance. This is particularly true where system testing on real equipment is not cost effective or where additional data is required. The third grand challenge is concerned with developing modelling and simulation techniques to allow systems analysts to concentrate on critical systems parameters/behaviours and evaluate their potential results

As the complexity and the scale of a system increases, so does the problem of developing an accurate and validated model. When dealing with complex systems involving humans in the loop, the mayor weakness is that there is not a comprehensive model of human behaviour or cognitive performance. Our ability to build and use complex models has been enhanced by the use of systems engineering techniques such as hierarchical decomposition, object-oriented modelling and programming, graphical depiction of systems behaviour, visual analytics, model based systems engineering, and agent based modelling. Multi-paradigm and multi-scale modelling at different levels of abstraction also constitute a definite advancement in many application domains. However challenges remain in many instances, for example in the case of systems of systems (SOS); these are neither designed from scratch nor reach a particular end state so they challenge the systems community in terms of whether it is possible or even practical to undertake extensive testing.

Modelling and simulation constitute a specialised area requiring high levels of knowledge and skill within the application domain. As systems become more complex and multi-disciplinary, the challenge extends to requiring the support of domain and subject matter experts.  The key challenge of any model and simulation activity is how accurately it represents the system under investigation. The verification, validation and assurance of models and simulations require confidence in them. Some of the specific challenges in this area are: shortened system design and development time-scale, model representation of the human within the context of a system model, comprehensive modelling frameworks, and complete model driven engineering environments.

Grand Challenge Objective

Shortened system design and development timescale

Faster development time with significant risk elimination
Robust and fully verifiable models with supporting evidence
Accurate and reliable representation of human physical, cognitive, behavioural and performance levels
Continued refinement of modelling languages (e.g. SysML) and other system representation tools to allow complete system representation at different levels of abstraction
Generation of executable code from model based representations through to implementation
Full support for multi-vendor tool integration
An open systems architecture supporting integration of models across disciplines, levels of abstraction and geographic locations
Coupled models that execute in real-time and across different design disciplines
Appropriate abstraction methods permitting full span of multi-scale (fine grained models to course grained) representations

Verification, validation and assurance of models
Model representation of the human within the context of a system model
Comprehensive modelling frameworks
Complete model driven engineering environments (whole system virtual engineering environment)


Systems Engineering Development, Environment and Tools

Modelling and simulation require the correct environment to allow the systems engineer to investigate the impact of complex interactions taking place throughout the life of a system. The fourth Grand Challenge is therefore to create an environment in which systems models can start as abstractions that later evolve into a full representation of the system. In order to achieve this all the models in a system must be compiled into an executable code and downloaded into the required target hardware. Model Systems Base Engineering MSBE is a step in this direction but at this time very few of its tools provide a traceable route to catalogue a system evolution from requirements to implementation. 

An example of this challenge is illustrated by UK Defence Industrial Strategy, which has introduced the requirement for capability based acquisitions rather than only equipment and performance specifications. This has had a profound effect on the modelling and simulation tools used for taking design decisions in the associated engineering systems. The areas of research for this challenge include the development of heterogeneous modelling tools which encapsulate human performance data, the creation of open architecture models that allow integration and incorporation of legacy system models from across different vendor tools, the generation of executable code from model based representation so that transformation tools can support systems from requirements to implementation and the development of tools that support design-model-evaluate-redesign-model…build processes.

Grand Challenge

Heterogeneous modelling tools Develop techniques to support use within a multi-organisation enterprise?

Accurate representation of the human (physical, cognitive and performance) in systems modelling tools Encapsulation of human performance data into systems engineering tool sets.
An open systems architecture Develop an open tool standard supporting integration of models and incorporation of legacy system models from across different vendor tools

Generation of executable code from model based representations
 Creation of model transformation tools that support requirements through to implementation

Design space exploration (trade space) Develop tools that support design-model-evaluate, re-design, ...build processes


Verification Validation & Assurance (VV&A) of Extremely Complex Systems

The increase level of the integration of products/processes  in the manufacturing industry has become extremely complex requiring an inter-disciplinary team to devise a system from design to in-service deployment. The cost of a malfunctioning system can be staggering lingering to the aftermath of a failure. Unfortunately, some systems faults are not detected until the moment they happen in service and then only after an incident occurs. The fifth Gran Challenge is therefore to avoid errors in systems by designing them for reliability from the outset with an emphasis on Verification, Validation and Assurance.

As systems become more complex their reliability becomes a major concern for manufactures. The need for VV&A cannot be overemphasised as design faults escalate costs of production. There are several examples that illustrate unanticipated problems in industry:  In January 2010, Toyota announced recalls of approximately 5.2 million vehicles for the pedal entrapment/floor mat problem, and a further 2.3 million vehicles for an accelerator pedal issue. Since January 2012 the following products were recalled for a variety of operational failures: Lexus RX400h, Crown Upright Fan Heater, Renault Kangoo, Saltrock Scooter, Sterimar nasal sprays, BMW Mini Cooper, Mitsubishi ASX, Koni Shock absorber, Isuzu Trooper 3.0 Diesel, MAN Trucks, Volvo Xc60, Ford Fiesta and Poly Implant Prostheses Silicone Breast Implants.  These faults result in both financial and reputational costs to the companies involved. The inevitable complexity of modern products makes it difficult to understand where in design undesirable emergent behaviour appears or simply, oversights in the design process result. There are several important factors that need to be addressed including functional correctness issues such as quality, and many non-functional issues such as dependability. Unless the system has been designed for reliability from the outset a single bit error can have catastrophic consequences for the whole system.

Grand Challenge


Undertake VV&A on complex systems
 Develop robust techniques to undertake reliable VV&A
Facilitate incorporation of legacy systems in a VV&A Develop techniques to embed (poorly defined) legacy systems within a VV&A evaluation
Development of metrics and techniques for assuring system trust
  Establish key metrics and evaluation criteria
Development of an integrated suite of tools to support all aspects of verified system construction Requirements capture, specification, validation test-case generation, refinement, analysis, verification and run-time checking.
Self-optimising system  Develop techniques and tools to facilitate VV & A

Development of reliable early detection of undesirable emergent behaviour  Improve understanding of emergence and VV & A (especially for Systems of Systems)
How do we deal with learning in multi-agent systems Develop methods to VV&A multi-agent systems


Ultra-scalable Hetereogeneous Systems

Over recent years systems have grown from single user to include geographically distributed multi-user systems. The advent of high bandwidth networks has further enable data connectivity between hardware and software systems around the world.  However some of these connections are unregulated and can lead to security breaches. As the scale of the systems grow the chances of failure increase too. The sixth Grand Challenge is therefore the design of ultra-scalable heterogeneous systems.

Large-scale systems present a significant challenge in terms of architecture, security and privacy of the data. Ultra scale systems need to be open in order to evolve into larger and more complex systems and include rapidly evolving technology.  The architecture of such systems must support from one to many hundreds of nodes with the required level of security and integrity. The Internet is a good example; its adoption has led to a phenomenal plethora of uncontrolled/unregulated connected computing resources resulting in a system that is prone to attack. On the other hand grid computing although based on similar network architectures is not as vulnerable because security certificates have been built in from the outset. As large systems have emerged there are growing examples of cases where they have failed to deliver. For example, the National Programme for IT in England was designed to reform the way the National Health Service (NHS) uses information, and hence improves services and the quality of patient care. The estimate cost of the programme was £12.7 billion and required substantial organisational and cultural change if it was to be successful. At the outset of the program the aim was the implementation of the systems by 2010 but latest forecasts suggest it is likely to take until 2015.

Cloud computing has recently appeared as a computing architecture in which dynamically scalable and often distributed computing resources are loosely-coupled to create a virtual computer comprising a cluster of networked computers acting in concert to process extremely large tasks as a service over the internet. It is anticipated that the demand for networked assets could increase further because of the number of invisible computers embedded in everyday objects around us. Many of these will have the capability to communicate with each other, which will in turn create more complex systems. Such systems present new challenges and prompt the following questions: What are the key features of an infrastructure capable of supporting ultra-scale network enabled systems? Will ever be possible to test and validate the whole system?


Grand Challenge


Development of a trusted open systems architecture

Independent of the number of interconnected nodes, their location, time of access and which is resilient in terms of loss of one or more nodes and the inherent network whilst still being human error free

Development of techniques and tools within a framework in which an ultra- scalable system can undergo VV&A

How to design, integrate, test and evaluate SoS?

Develop approaches ensuring only relevant data of interest is accessed

Manage trust, security and privacy aspects of data

Ensure data provenance – knowing the exact status of information

Establish integrated tool environment to permit design, evaluation and testing of an ultra-scalable system

Improved modelling and simulation tools

How to VV&A Ultra-scalable Heterogeneous Systems

Systems of Systems (SoS) design
Management of ultra-scalable data
Establish virtual engineering environment to support design of ultra-scale systems
Ensure resilience to system failure and external denial of service attack

Ultra-scalable Autonomous Systems


Autonomous systems replace human based systems and are beneficial in many respects such cost effectiveness, infrastructure requirements and ability to perform without emotion. However, it is necessary to ensure this type of systems act responsibly. The seventh Grand Challenge is about ensuring that autonomous systems interact with each other for their individual and collective good, in particular as they evolve into ultra-scalable systems. 

A true autonomous system has little if any reliance on human operators. Such system incorporate disciplines including artificial intelligence, distributed computing, object oriented systems, software engineering, economics, sociology and organisational science. Currently the field of autonomous and multi-agent systems is receiving a great deal of attention and represent a rapidly expansive area of research and development.  However this challenge is particularly concerned with ultra-scalable autonomous systems that can exist from a single autonomous system to a large civilisation of autonomous systems working co-operatively. The form and function of the individuals in the civilisation need not to be all the same and diversify according to need. Clearly autonomous agents and multi-agent systems require effective ways of analysing, designing and forming more complex systems.  The design and construction of ultra-scalable autonomous systems should be such that they are able to automatically reconfigure to tackle new tasks and evolve with technology. Each individual autonomous system in the ultra-scale must be fully aware of its environment and undertake task cooperatively with neighbouring systems. The ultimate goal for such systems is to respond intelligently to an event, in the same fashion as humans do.

Grand Challenge


Design and construction of an ultra-scalable autonomous system architecture

Automatically reconfigures itself to tackle new tasks and is able to evolve as its tasking changes or the enabling technology evolves.

Comprising a large number of smaller autonomous systems that are collectively fully aware of their environment and undertake tasks co- operatively with neighbouring systems and agents

Ultimate goal is for such systems to intelligently respond to events and situations with the same (or better) outcome as corresponding human based systems would achieve.

Establish new tools and theories (including autonomous cognition models) to reduce requirement for human intervention

Establish a completely autonomous system paradigm - identifying what new systems engineering methodologies and tools will be required to support this.

Develop confidence and mechanisms where humans will pass total authority to the autonomous system

Develop self-healing in the event of system failure (either complete or intermittent)

Investigate ability for system to assume full accountability for actions

Develop methods that ensure that autonomous system fails in a non- detrimental manner

Develop new context awareness in terms of self and the whole autonomous community

Self-configuration and adaptation to changing situations

Systems engineering design environment that permits the design, evaluation and testing of an ultra-scalable autonomous system

Define autonomous system trust, security and privacy

Ethics, social and legal acceptability of autonomous systems

Integration of autonomous system environment where human controlled systems coexist. 
System architectures supporting totally autonomous agents

The derivation of the grand challenges for systems research is a continuous process as it is expected that the systems community will review these proposals from time to time in order to refine, develop, and redefine their nature and scope. The aim of the challenges presented is to identify a set of deep technical problems that are common in different application contexts to then provide systems solutions that are far-reaching and durable.

To validate the grand challenges they must:

  1. Be endorsed by the community. To gain credibility, the community as a whole should endorse a grand challenge. This does not necessarily guarantee success, but the challenge serves to focus research effort towards a shared understanding of where the breakthroughs are required.
  2. Be independent of the short-term market pull and technology push. The academic community endeavours to achieve breakthroughs and scientific impact whilst the industry sector typically wants exploitable results in the short term. At the heart of this process is the need to bridge from Technology Readiness Level (TRL) 1-3 to TRL 7-9 via TRL 4-6 the so called ‘valley of death’ in order to ensure effective knowledge and technology transfer. 
  3. Initially research must be based on an specific discipline
  4. Must envision future development of the systems engineering approach