Contribution of SPA to the Anglian Water's digital twin roadmap
Anglian Water has a 15-year roadmap for achieving an enterprise-wide Digital Twin that can ultimately form part of the long-term vision for a UK National Digital Twin.
The very first steps of the roadmap, which defined how Anglian Water's digital twin can meet the regulated outcomes as prescribed by Ofwat, are complete. Those activities were followed by a proof of concept that showed how digital twin approaches could improve energy management and workforce efficiency within the example context of a pumping station.
SPA is now at the next stage of the roadmap, where we have rolled out a first version of our applications and data architecture. Our focus and biggest challenge are building an extendable platform that can support the future Anglian Water's Digital Twin ambitions, whilst value is driven for SPA in the short term.
Implementing our applications and data strategy is a substantial transformational change, which requires a cultural shift towards a product approach, and a concerted effort to align the architecture across people, culture, technology, and data.
Product approach
Anglian Water has been investigating the merits of product lifecycle management approaches to introduce a 'product mindset' to the business, ensuring that investments promote repeatability and robust standards.
In this context, SPA has been working with Anglian Water to develop an asset information model, process blocks and product data templates. Those elements provide the foundation for a product-based approach around digital assets that can be reused across the AW enterprise.
SPA has also developed various graphical interfaces through BIM, GIS and control and automation platforms, which have provided a mechanism to build products to allow user interaction with the digital assets. These graphical interfaces provide the stakeholders with powerful ways of asking "what if" questions facing different organisation objectives and asset functions.
Application architecture
SPA acts as a 'critical friend' to the Anglian Water enterprise. All SPA applications are continuously assessed regarding extendibility and compatibility within the Anglian Water applications landscape.
That said, SPA strives to use technologies recently approved or historically used by Anglian Water. That approach brings significant benefits in reducing procurement costs and ensuring continuity.
On the other hand, the focus on a conscious coupling of delivery and the longer-term roadmap for Anglian Water does mean that there is initially a greater level of complexity. However, that approach is already showing benefits in the velocity of our trajectory with the ability to reuse existing patterns and gain consensus and goodwill amongst the wider change community.
Data architecture
From a data perspective Anglian Water, with its strong Digital Twin ambitions, is in the process of maturing the curation and management of data. That involves activities in several areas such as data accuracy, integrity, completeness and timeliness. We are working with our Anglian Water colleagues to enable these through:
Aligning our data templates with key Anglian Water contextual data, such as Asset identification, Asset location and functional location codes. That is to ensure data continuity within the various systems through using the same identification system as the one that Anglian Water has developed.
Highlighting where current technologies cannot securely and safely house the core data required for the Digital Twin.
Ensuring that data can flow from systems of record, and sensors, into the Digital Twin in a manner that enables timely decisions to be made.
Ensuring that data captured within the SPA design and build phases can be held within the Anglian Water IT systems post-handover.
People and culture
There persists a view that technology such as digital twins will drive new value on their own. We believe however that value will be realised from business transformation enabled by digital twins. This will require a cultural shift in a very traditional industry.
The Strategic Pipeline Alliance was built on the principles of enabling data-informed decision-making. Valuing "data as an asset" is a new concept for Anglian Water.
Although there are robust governance processes within the organisation, an approach of open early communication has been taken to provide a "no surprises" philosophy. That approach ensures appropriate stakeholders are engaged as soon as is practical after identification and brought into the philosophy of our journey.
Education and storytelling are fundamental to help guide and draw the Anglian Water organisation along this transformational journey. Therefore, we are working closely with Anglian Water communities of practice to understand the required business capabilities and the current maturity of the Anglian Water organisation in these areas. For example, the Anglian Water and SPA architecture share a leading-edge enterprise architecture model, to ensure consistency and a frictionless handover.
A 'core delivery team' has been formed to work with SPA and Anglian Water stakeholders. The team ensures alignment from both a technical and cultural perspective, supporting the development of digital assets. Subject matter experts support the core team and are brought in as required to help deliver specialist services, such as penetration testing, installation of sensors and Operational Technology, or creation of data driven hydraulic models.
What is certainly clear is that we still have a lot to learn, however by following good architectural best practice and placing people at the heart of everything we do, we have put in place a good foundation from which to build.
If you would like to know more, please get in touch through the DT Hub.
The government's cyber-physical infrastructure consultation agenda is to pool industry sector ideas to address common challenges.
A joint activity with BEIS and Innovate UK, the roundtable formed part of the government's cyber-physical infrastructure consultation. As its base, it used earlier research carried out over a three-month period to identify and prioritise digital twin blockers.
Challenge questions
How can a cyber-physical infrastructure emerge from connections between the physical and digital world using digital twins? What are the socio-technical challenges in developing an ecosystem of connected digital twins?
Discussion perspectives
Participants examined scenarios across manufacturing, energy, multi-modal transport and aviation industries, feeding back from the room and online.
The event involved over 70 industry representatives and ran simultaneously online and in person at Connected Places Catapult, London on 24.5.22.
WATCH NOW if you missed it
In-person view - from the auditorium https://www.youtube.com/watch?v=jiy1swTKxqo&t=926s
Online view - focused on the slides https://www.youtube.com/watch?v=2Yi29EeyL7w
Join us to celebrate the launch of the Infrastructure Client Group Annual Digital Benchmarking Report 2021 on 15 June 2022 at 9:00 BST by REGISTERING HERE.
The ICG Report, powered by the Smart Infrastructure Index, surveys asset owners and operators who jointly represent over £385bn worth of capital assets and over 40% of the national infrastructure and construction pipeline.
After Mark Enzer, former Head of the National Digital Twin Programme, Centre for Digital Built Britain, introduces the report, Andy Moulds and Anna Bowskill, Mott MacDonald, will uncover the results of the latest research into the state of the nation for digital adoption and maturity.
This will be followed by a panel of industry thoughts leaders and practitioners, chaired by Melissa Zanocco, Co-Chair DTHub Community Council, sharing their views and best practice case studies from the ICG Digital Transformation Task Group and Project 13 Adopters including:
Karen Alford, Environment Agency – skills
Matt Edwards, Anglian Water – digital twins
Sarah Hayes, CReDo – Climate Resilience Demonstrator digital twin
Neil Picthall, Sellafield – common data environments
Matt Webb, UK Power Networks – digital operating models
Will Varah, Infrastructure & Projects Authority – Transforming Infrastructure Performance: Roadmap to 2030
REGISTER to find out how much progress has been made at a time when digital transformation is a critical enabler for solving the global, systemic challenges facing the planet.
For any questions or issues, please contact Melissa Zanocco: melissa.zanocco@ice.org.uk
Please note: We plan to make a recording of the event available. Please note that third parties, including other delegates may also take pictures or record videos and audio and process the same in a variety of ways, including by posting content across the web and social media platforms.
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The finance industry has long been at the forefront of using data and technology to make better decisions, to de-risk and improve return on investments, and to create better outcomes. Tackling the big challenges of our day—such as climate change, energy, and healthcare—relies on having the right technologies and data to make the right interventions.
One of the most significant technology developments of the past decade, digital twins—a digital replica of a physical asset or world—is the key in building infrastructure that supports future generations.
The Centre for Digital Built Britain (CDBB) has seen first-hand how digital twins can improve decision-making in the planning, design, build, and operation of assets, as well as the benefits of connecting the technology across organisations and sectors.
Based on the Gemini Principles, the CDBB spearheaded the development of the U.K.’s national digital twin project, building an ecosystem of connected digital twins that can securely share infrastructure information in real-time to support better outcomes.
If this goal seems one step too far into science fiction, then we can take inspiration from Singapore, which recently became the first country to create a country-wide digital twin. The technology will help create more sustainable, resilient, and smart development; help in the rollout of renewable energy; and protect against climate change and rising sea levels.
Cross River Rail in Brisbane, Australia, is another example of a publicly sponsored mega-project that can provide the catalyst for a city-level digital twin. Featuring multi-environment digital twins that constantly talk to one another, the AUD 5.4 billion (GBP 3 billion) project aims to introduce the benefits of a federated model approach to digital engineering.
The issues in Singapore and Brisbane that are being solved through digital twins are similar to those that are affecting, or soon will affect, the U.K. However, there are other use cases for the technology. The Grenfell Tower tragedy, for example, has ushered in a renewed focus from government and regulators on the safety of higher risk buildings, while national and international banks are having to demonstrate the impact of their investments against economic, social, and environmental (ESG) targets.
As key investors, the finance community has an opportunity to realise the benefits of digital twins by using them to add value, track sustainability targets, attract new investments, and manage risk better.
Having spoken with a wide range of investors, the conclusion is that there is huge untapped potential for investors to influence how data is used to improve infrastructure decision-making. Also, by taking on a greater role in the digital transformation of infrastructure, investors can be involved in providing better outcomes for businesses, people, and nature.
There is also a significant opportunity to leverage digital twins to support key challenges facing the investment community: where to allocate capital; screening and managing risk; enhancing asset value by improving performance and reliability; and complying with environmental, social, and governance (ESG) requirements.
The key to unlocking this potential is to apply the fundamentals of the information value chain.
By collaborating with the wider industry to develop practical use cases, the finance community can help use the insights derived from data to solve their most pressing problems.
The infrastructure sector needs to play its part and approach this dialogue with openness and flexibility. Infrastructure professionals need to understand that investors generate their return on investment (ROI) in a variety of ways, via different types of assets and at different stages of the infrastructure lifecycle. Some of their use cases overlap with those already developed for supply chain businesses or operators while others will not.
We first need to improve the quality of the dialogue between investors and other parts of the infrastructure sector, re-imagine the information value chain from an investor’s perspective, explore how investors can expand their leadership role, and share some use cases investors are currently pursuing.
From an infrastructure industry perspective, there are three important steps to achieve this goal:
Understand the variety of infrastructure investors and what that means for the different ways that they can benefit from digital twins
Understand how investors categorise infrastructure
Relate digital twin use cases to different investor strategies
Collaboration across the infrastructure industry and investors is key to building smarter, more sustainable infrastructure. When we collaborate, across boundaries and across borders, we can do amazing things. We can make better business decisions that drive better economic, social, and environmental outcomes.
There is already progress being made, with digital twin spending set to reach USD 27.6 billion by 2040, according to an article published by Twinview. It will be exciting to see how the finance community will join with the wider infrastructure community and solution providers to use higher quality data and digital twins to improve investment returns, meet ESG goals, and create the sustainable future we all want to see.
Listen to the Engineer's Collective, New Civil Engineer podcast where NCE editor Claire Smith discusses the paper with former executive director of the Centre for Digital Built Britain, Alexandra Bolton, and talks to Cross River Rail's CEO Graeme Newton and digital delivery manager Andrew Curthoys.
by Alexandra Bolton, Executive Director, Centre for Digital Built Britain; Mark Coates, International Director of Public Policy and Advocacy, Bentley Systems; Peter El Hajj, National Digital Twin Programme Lead, Centre for Digital Built Britain
How Finance and Digital Twins Can Shape a Better Future for the Planet.pdf
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The bigger and more complicated the engineering problem, the more likely it is to have a digital twin. Firms that build rockets, planes and ships, for example, have been creating digital twins since the early 2000s, seeing significant operational efficiencies and cost-savings as a result. To date, however, few firms have been able to realise the full potential of this technology by using it to develop new value- added services for their customers. We have developed a framework designed to help scale the value of digital twins beyond operational efficiency towards new revenue streams.
In spite of the hype surrounding digital twins, there is little guidance for executives to help them make sense of the business opportunities the technology presents, beyond cost savings and operational efficiencies. Many businesses are keen to get a greater return on their digital twins’ investment by capitalising on the innovation – and revenue generating - opportunities that may arise from a deeper understanding of how customers use their products. However, because very few firms are making significant progress in this regard, there is no blueprint to follow. New business models are evolving but the business opportunities for suppliers, technology partners and end-users is yet to be fully documented.
Most businesses will be familiar with the business model canvas as a tool to identify current and future business model opportunities. Our ‘Four Values’ (4Vs) framework for digital twins is a more concise version of the tool, developed to help executives better understand potential new business models. It was designed from a literature review and validated and modified through industry interviews. The 4Vs framework covers: the value proposition for the product or service being offered, the value architecture or the infrastructure that the firm creates and maintains in order to generate sustainable revenues; the value network representing the firm’s infrastructure and network of partners needed to create value and to maintain good customer relationships; and value finance such as cost and revenue structures.
Value proposition
The value proposition describes how an organisation creates value for itself, its customers and other stakeholders such as supply chain partners. It defines the products and services offered, customer value (both for customers and other businesses) as well as the ownership structure. Examples of digital twin-based services include condition monitoring, visualization, analytics, data selling, training, data aggregation and lifespan extension. Examples of customer value in this context might include: decision support, personalisation, process optimisation and transparency, customer/operator experience and training.
Value architecture
The value architecture describes how the business model is structured. It has 5 elements: 1. Value control is the approach an organisation takes to control value in the ecosystem. For example, does it exist solely within its own ecosystem of digital twin services or does it intersect with other ecosystems? 2. Value delivery describes how the digital twins are delivered, are they centralised, decentralised or hybrid? It also seeks to understand any barriers that may prevent the delivery of digital twins to customers. 3. Interactions refers to the method of customer interaction with the digital twin. Common examples of interaction include desktop or mobile app, virtual reality and augmented reality interactions. 4. Data collection underlies the digital twin value proposition and can be a combination of the following: sensor based and/or supplied/purchased data. 5. Boundary resources are the resources made available to enhance network effects and scale of digital twin services. This typically comprises of the following: APIs, hackathons, software development toolkits and forums.
Value network
The value network is the understanding of interorganisational connections and collaborations between a network of parties, organisations and stakeholders. In the context of digital twin services, this is a given as the delivery mechanism relies on multiple organisations, technological infrastructure and stakeholders.
Value finance
This defines how organisations approach costing, pricing methods and revenue structure for digital twins. Digital twin revenue model most commonly refers to outcomes-based revenue streams and data-driven revenue models. Digital twin pricing models include, for example, freemium and premium, subscription models, value-based pricing and outcome-based pricing models. Four types of digital twin business models were identified from extensive interviews with middle and top management on services offered by digital twins, we identified four different types of business models and applied our 4Vs approach to understand how those models are configured and how they generate value.
Brokers
These were all found in information, data and system services industries. Their value proposition is to provide a data marketplace that orchestrates the different players in the ecosystem and provides anonymised performance data from, for example, vehicle engines or heating systems for buildings. Value Finance consists of recurring monthly revenues levied through a platform which itself takes a fee and allocates the rest according to the partnership arrangements.
Maintenance-optimisers
This business model is prevalent in the world of complex assets, such as chemical processing plants and buildings. Its value proposition lies in providing additional insights to the customer on the maintenance of their assets to provide just-in-time services. What-if analysis and scenario planning are used to augment the services provided with the physical asset that is sold. Its Value Architecture is both open and closed, as these firms play in ecosystems but also create their own. They control the supply chain, how they design the asset, how they test it and deliver it. Its Value Network consists of strategic partners in process modelling, 3D visualisation, CAD, infrastructure and telecommunications. Value Finance includes software and services which provide a good margin within a subscription model. Clients are more likely to take add-on services that show significant cost savings.
Uptime assurers
This business model tends to be found in the transport sector, where it’s important to maximise the uptime of the aircraft, train or vehicle. The value proposition centres on keeping these vehicles operational, either through predictive maintenance for vehicle/ aircraft fleet management and, in the case of HGVs, route optimisation. Its Value Architecture is transitioning from closed to open ecosystems. There are fewer lock- in solutions as customers increasingly want an ecosystems approach. Typically, it is distributors, head offices and workshops that interact with the digital twin rather than the end-customer. The Value Network is open at the design and assembly lifecycle stages but becomes closed during sustainment phases. For direct customers digital twins are built in-house and are therefore less reliant on third-party solutions. Its Value Finance is focused on customers paying a fee to maximise the uptime of the vehicle or aircraft, guaranteeing, for example, access five days a week between certain hours.
Mission assurers
This business model focuses on delivering the necessary outcome to the customers. It tends to be found with government clients in the defense and aerospace sector. Value propositions are centered around improving efficacy of support and maintenance/ operator insight and guaranteeing mission success or completion. These business models suffer from a complex landscape of ownership for integrators of systems as much of the data does not make it to sustainment stages. Its Value Architecture is designed to deliver a series of digital threads in a decentralised manner. Immersive technologies are used for training purposes or improved operator experience. Its Value Network is more closed than open as these industries focus on critical missions of highly secure assets. Therefore, service providers are more security minded and careful of relying on third-party platforms for digital twin services. Semi-open architecture is used to connect to different hierarchies of digital twins/digital threads. Value Finance revealed that existing pricing models, contracts and commercial models are not yet necessarily mature enough to transition into platform-based revenue models. Insights as a service is a future direction but challenging at the moment, with the market not yet mature for outcome-based pricing.
For B2B service-providers who are looking to generate new revenue from their digital twins, it is important to consider how the business model should be configured and identify major barriers to their success. Our research found that the barriers most often cited were cost, cybersecurity, cultural acceptance of the technology, commercial or market needs and, perhaps most significantly, a lack of buy-in from business leaders. Our 4Vs framework has been designed to help those leaders arrive at a better understanding of the business opportunities digital twin services can provide. We hope this will drive innovation and help digital twins realise their full business potential.
Now for a small request to the reader that has reached this far, we are looking to scale these research findings in our mass survey across industry on the business models of digital twins. If your organisation is planning to implement or has already started its journey of transformation with digital twins please help support our study by participating in our survey. Survey remains fully anonymised and all our findings will be shared with the DTHub community in an executive summary by the end of the year.
Link to participate in the survey study https://cambridge.eu.qualtrics.com/jfe/form/SV_0PXRkrDsXwtCnXg
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The Digital Twin Hub is at the heart of the UK’s digital twin community, shaping and promoting the development and use of this technology motivated by complex challenges that require a connected collaborative approach.
Recently the Hub was integrated into Connected Places Catapult, the UK’s leading innovation accelerator for cities, transport, and places. As part of this transition, we are looking at building out a new strategic board to help steer the DT Hub and build its community.
If you’re up to the challenge of helping shape one of the most powerful and impactful digital agendas, we’d like to hear from you. We're looking for diverse experience - can you represent an infrastructure asset owner, a technology solution provider or help us shape our growth into adjacent sectors?
We’re looking for a Chair of the Board and a number of board members with a range of technology and market experiences.
Please see details: Board Member roles Chair of the Board role
PRESS RELEASE
With the announcement that the Digital Twin Hub will transition to an Industry/Catapult partnership housed at the Connected Places Catapult (CPC), we are pleased to add that the next phase of the National Digital Twin programme’s Climate Resilience Demonstrator (CReDo) will also move to CPC. This next phase will build on the excellent efforts of the interdisciplinary and cross-sectoral team who worked on CReDo so far. This work is being shared openly to contribute to a culture of secure data sharing for the purposes of resilience and adaptation to climate change.
The first phase of CReDo, showing the benefits of connected digital twins across infrastructure networks on adaptation and resilience, is coming to a close at the end of March. This phase of the project, funded by UK Research and Innovation, the University of Cambridge and CPC, wrapped up with a public webinar on 2nd March 2022, which was attended by over 240 participants and featured insights from the technical development team, funders and asset owners.
So far, CReDo has demonstrated how collaboration through connected digital twins is key to tackling climate change. The project is marking the move into its next phase at CPC with a series of outputs that will share key findings, benefits, lessons learned and the technical approach to this first-of-its-kind collaboration. These are all openly available on the Digital Twin Hub from today.
Discussing the urgency for collaboration through connected digital twins, Sarah Hayes, Head of the CReDo project, said: “The risks arising from failing to adapt to climate change are huge. CReDo seeks to mitigate these risks by increasing our understanding of infrastructure interdependencies and the future impact of interventions to increase resilience. The CReDo team have worked incredibly hard to lay the foundations for increasing infrastructure system resilience. It is the skills of our people, supported by new technologies, which will take forward our capability to tackle climate change through connected digital twins.”
Pointing to the potential for this work to have a positive impact, Mark Enzer, Head of the National Digital Twin programme, said: “In a wonderfully tangible and relevant way, CReDo has shown the value of enabling secure information flow across sector boundaries. But this should be just the beginning. The idea of connecting digital twins must be extended to other sectors and other use cases – not only in addressing climate change, but wherever we need to understand systems better and intervene more effectively. I believe in CReDo!”
Looking forward to the next phase of the project, Yalena Coleman, Director of Applied Data & Technology at CPC, said, “Integrated infrastructure is a key strategic focus area for Connected Places Catapult, and we will be investing in further phases of CReDo, working together with partners to take forward the key learnings from this phase. We will ensure the learnings are shared with the wider community and across other relevant initiatives like the Digital Connectivity Infrastructure Accelerator, National Underground Asset Register and others; and link up industry, academia and government thinking in this area.”
The Digital Twin Journeys workstream has taken world leading research and turned it into accessible and useful information to enable those who are just starting out on their digital twin journeys to get ahead. We have learnt about more than just innovative technologies and their implementation, we have learnt about the type of thinking that makes this research ground-breaking. To take this research forwards and discover what your Minimum Viable Twin is, check out the infographic, the final summary of our workstream.
Join Desmond and Mara as they embark on a journey of their own to develop a digital twin. As you follow them, you will learn about an approach to design thinking and iterative development that paves the way for effective digital twin prototyping.
Read the full infographic here.
We have taken our journey through assessing the need of users as they utilise our services. This enables the interventions that we make to be tailored to their needs, considering the ecosystem of services they rely on and the differing levels of access to these services.
We have learnt that care needs to be taken when selecting whether to create your own solution from scratch, buy something pre-existing or work with partners. The Deep Dish project used well established code to handle computer vision, the sensors used in the Staffordshire bridges projects were not custom made for it. In short, there is no need to reinvent the wheel.
As digital twins were themselves first conceived by NASA as a way of managing assets in the most inaccessible place, space, so too have we learnt how we can manage inaccessible assets from space with the help of satellite telemetry. But we also discovered how important skilled data scientists are to making this technique accessible to industry.
We learned that digital twin prototypes can be used as a tool for their own continuous cycle of improvement, as each iteration teaches us how to better classify, refine and optimise the data we use in our decision-making.
The key to it all is the decisions that we make, the way that we change the world around us based upon the information that we have in front of us. We have learnt that working with decision makers is central to creating digital twins that improve outcomes for people and nature as part of a complex system of systems. We can provide these stakeholders with the information that they need to realise our collective vision for a digital built Britain.
This research forms part of the Centre for Digital Built Britain’s (CDBB) work at the University of Cambridge. It was enabled by the Construction Innovation Hub, of which CDBB is a core partner, and funded by UK Research and Innovation (UKRI) through the Industrial Strategy Challenge Fund (ISCF).
Check out the rest of the outputs on the CDBB Digital Twin Journeys page.
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Transforming an entire industry is, at its core, a call to action for all industry stakeholders to collaborate and change. The National Digital Twin programme (NDTp) aims to do just that, enabling a national, sector-spanning ecosystem of connected digital twins to support people, the economy, and the natural environment for generations to come.
But to achieve these ambitious impacts, a great deal of change needs to occur. So, to provide clear rationale for why potential activities or interventions should be undertaken and why they are expected to work, Mott MacDonald has worked with CDBB to develop a Theory of Change (ToC) and a Benefits Realisation Framework (BRF) to represent the logical flow from change instigators (i.e., levers) to overall benefits and impacts. The ToC and BRF are expected to provide future leaders and policymakers with a clear understanding of the drivers of change and the actors involved to create an ecosystem of connected digital twins.
Components of the Theory of Change
Within the ToC, we outline several key components - actors, levers, outputs, outcomes, impacts, and interconnected enablers. As a national programme uniting the built environment through a complex system of systems, it is essential that multiple actors collaborate, including asset owners and operators, businesses, government, academia, regulators, financial entities, and civil society. These actors need to share a common determination to move the needle towards better information management by utilising a combination of interconnected levers to kickstart the change: financial incentives, mandates and legislation as well as innovation.
We see that pulling these three levers is likely to trigger tangible change pathways (i.e., the routes in which change takes place), manifested through the ToC outputs and intermediate outcomes, leading to the creation of institutional and behavioural changes, including organisations taking steps to improve their information management maturity and exploring cross-sector, connected digital twins. Ultimately, we consider these change pathways to lead to the long-term intended impact of the NDTp, achieving benefits for society, the economy, businesses, and the environment.
Underpinning and supporting the levers and change pathways are the enablers. We see these as positive market conditions or initiatives and are key in implementing and accelerating the change. They span having a unifying NDTp strategy, vision and roadmap, empowering leadership and governance, leveraging communication and communities, building industry capacity, and adopting a socio-technical approach to change.
The five levels of the Theory of Change
We intend for the ToC to outline how change can occur over five distinct levels: individual, organisational, sectoral, national, and international. The individual level involves training and upskilling of individuals from school students to experienced professionals, so that individuals can be active in organisations to drive and own the change. Our previous work with CDBB focused on the Skills and Competency Framework to raise awareness of the skills and roles needed to deliver a National Digital Twin in alignment with the Information Management Framework (IMF).
At the core of establishing the National Digital Twin is the organisational level, within which it is essential for change to occur so that organisations understand the value of information management and begin to enhance business processes. Broadening out from these two levels sits the sectoral level, where the development of better policies, regulations and governance can further support the change across all levels. Similarly, change at the national level will guide strategic engagement and should encourage further public support.
Ultimately, change at these four levels should achieve change at an international level, where the full potential of connected digital twins can be realised. Through the encouragement of international knowledge sharing and by creating interconnected ecosystems, challenges that exist on a global scale such as climate change can be tackled together.
Benefits Realisation Framework
Monitoring and evaluation have been fundamental to the assessment of public sector policy and programme interventions for many years. The potential benefits of the NDTp are significant and far reaching, and we have also developed guidance on how to establish a benefits realisation framework, based on UK best practice including HM Treasury’s Magenta Book, to drive the effective monitoring and evaluation of NDTp benefits across society, the economy, businesses, and the environment. We intend for this to provide high-level guidance to measure and report programme benefits (i.e., results) and track programme progress to the NDTp objectives outlined in the Theory of Change.
The Gemini Papers
Our work in developing the Theory of Change for the National Digital Twin programme has informed one of the recently published Gemini Papers. The Gemini Papers comprise three papers addressing what connected digital twins are, why they are needed, and how to enable an ecosystem of connected digital twins, within which the Theory of Change sits.
Together, we can facilitate the change required to build resilience, break down sectoral silos and create better outcomes for all.
To asset owners and managers, understanding how people move through and use the built environment is a high priority, enabling better, more user-focused decisions. However, many of the methods for getting these insights can feel invasive to users. The latest output from Digital Twin Journeys looks at how a researcher at the University of Cambridge has solved this problem by teaching a computer to see. Watch the video to learn more.
Working from the University of Cambridge Computer Laboratory, Matthew Danish is developing an innovative, low-cost sensor that tracks the movement of people through the built environment. DeepDish is based on open-source software and low-cost hardware, including a webcam and a Raspberry Pi. Using Machine Learning, Matthew has previously taught DeepDish to recognise pedestrians and track their journeys through the space, and then began training them to distinguish pedestrians from Cambridge’s many cyclists.
One of the key innovations in Matthew’s technique is that no images of people are actually stored or processed outside of the camera. Instead, it is programmed to count and track people without capturing any identifying information or images. This means that DeepDish can map the paths of individuals using different mobility modes through space, without violating anyone’s privacy.
Matthew’s digital twin journey teaches us that technological solutions need not be expensive to tick multiple boxes, and a security- and privacy-minded approach to asset sensing can still deliver useful insights.
To find out more about DeepDish, read about it here.
This research forms part of the Centre for Digital Built Britain’s (CDBB) work at the University of Cambridge. It was enabled by the Construction Innovation Hub, of which CDBB is a core partner, and funded by UK Research and Innovation (UKRI) through the Industrial Strategy Challenge Fund (ISCF).
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We all want the built environment to be safe and to last. However, minor movements over time from forces such as subsidence can impact how well our assets perform. It can also make connecting and modifying assets harder if they have shifted from the position in which they were built. If the assets are remote or hard to access, this makes tracking these small movements even more difficult.
The latest instalment from the Digital Twin Journeys series is a video showing the construction and built environment sectors what they need to know about remote sensing and using satellite data, featuring the Construction Innovation Hub-funded research by the Satellites group based at the Universities of Cambridge and Leeds.
Using satellite imaging, we may be able to detect some of the tell-tale signs of infrastructure failure before they happen, keeping services running smoothly and our built environment performing as it was designed over its whole life.
You can read more from the Satellites project by visiting their research profile.
This research forms part of the Centre for Digital Built Britain’s (CDBB) work at the University of Cambridge. It was enabled by the Construction Innovation Hub, of which CDBB is a core partner, and funded by UK Research and Innovation (UKRI) through the Industrial Strategy Challenge Fund (ISCF).
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Several Terms such as Digital Ecosystem, Digital Life, Digital World, Digital Earth have been used to describe the growth in technology. Digital twins are contributing to this progress, and it will play a major role in the coming decades. More digital creatures will be added to our environments to ease our life and to reduce harms and dangerous. But can we trust those things? Please join the Gemini call on the 29th of March; Reliability ontology was developed to model hardware faults, software errors, autonomy/operation mistakes, and inaccuracy in control. These different types of problems are mapped into different failure modes. The purpose of the reliability ontology is to predict, detect, and diagnose problems, then make recommendations or give some explanations to the human-in-the-loop. I will discuss about these topics and will describe how ontology and digital twins are used as a tool to increase the trust in robots.
Trust in the reliability and resilience of autonomous systems is paramount to their continued growth, as well as their safe and effective utilisation. A recent global review into aviation regulation for BVLOS (Beyond Visual Line of Sight) with UAVs (Unmanned Aerial Vehicles) by the United States Congressional Research Office, highlighted that run-time safety and reliability is a key obstacle in BVLOS missions in all of the twelve European Union countries reviewed . A more recent study also highlighted that within a survey of 1500 commercial UAV operators better solutions towards reliability and certification remain a priority within unmanned aerial systems. Within the aviation and automotive markets there has been significant investment in diagnostics and prognostics for intelligent health management to support improvements in safety and enabling capability for autonomous functions e.g. autopilots, engine health management etc.
The safety record in aviation has significantly improved over the last two decades thanks to advancements in the health management of these critical systems. In comparison, although the automotive sector has decades of data from design, road testing and commercial usage of their products they still have not addressed significant safety concerns after an investment of over $100 Billion in autonomous vehicle research. Autonomous robotics face similar, and also distinct, challenges to these sectors. For example, there is a significant market for deploying robots into harsh and dynamic environments e.g. subsea, nuclear, space etc which present significant risks along with the added complexity of more typical commercial and operational constraints in terms of cost, power, communication etc which also apply. In comparison, traditional commercial electronic products in the EEA (European Economic Area) have a CE marking, Conformité Européenne, a certification mark that indicates conformity with health, safety, and environmental protection standards for products sold within the EEA. At present, there is no similar means of certification for autonomous systems.
Due to this need, standards are being created to support the future requirements of verification and validation of robotic systems. For example, the BSI standards committee on Robots and Robotic Devices and IEEE Global Initiative for Ethical Considerations in Artificial Intelligence and Autonomous Systems (including P7009 standard) are being developed to support safety and trust in robotic systems. However, autonomous systems require a new form of certification due to their independent operation in dynamic environments. This is vital to ensure successful and safe interactions with people, infrastructure and other systems. In a perfect world, industrial robotics would be all-knowing. With sensors, communication systems and computing power the robot could predict every hazard and avoid all risks. However, until a wholly omniscient autonomous platform is a reality, there will be one burning question for autonomous system developers, regulators and the public - How safe is safe enough? Certification infers that a product or system complies with legal relevant regulations which might slightly differ in nature from technical or scientific testing. The former would involve external review, typically carried out by some regulators to provide guidance on the proving of compliance, while the latter usually refers to the reliability of the system. Once a system is certified, it does not guarantee it is safe – it just guarantees that, legally, it can be considered “safe enough” and that the risk is considered acceptable.
There are many standards that might be deemed relevant by regulators for robotics systems. From general safety standards, such as ISO 61508, through domain specific standards such as ISO 10218 (industrial robots), ISO 15066 (collaborative robots), or RTCA DO-178B/C (aerospace), and even ethical aspects (BS8611). However, none of those standards address autonomy, particularly full autonomy wherein systems take crucial, often safety critical, decisions on their own. Therefore, based on the aforementioned challenges and state of the art, there is a clear need for advanced data analysis methods and a system level approach that enables self-certification for systems that are autonomous, semi or fully, and encompasses their advanced software and hardware components, and interactions with the surrounding environment. In the context of certification, there is a technical and regulator need to be able to verify the run-time safety and certification of autonomous systems. To achieve this in dynamic real-time operations we propose an approach utilising a novel modelling paradigm to support run-time diagnosis and prognosis of autonomous systems based on a powerful representational formalism that is extendible to include more semantics to model different components, infrastructure and environmental parameters.
To evaluate the performance of this approach and the new modelling paradigm we integrated our system with the Robotics Operating System (ROS) running on Husky (a robot platform from Clearpath) and other ROS components such as SLAM (Simultaneous Localization and Mapping) and ROSPlan-PDDL (ROS Planning Domain Definition Language). The system was then demonstrated within an industry informed confined space mission for an offshore substation. In addition, a digital twin was utilized to communicate with the system and to analysis the system’s outcome.
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Intelligent infrastructure is a new trend that aims to create a work of connected physical and digital objects together in industrial domains via a complex digital architecture which utilises different advanced technologies. A core element to this is the intelligent and autonomous component. Two-tiers intelligence is a novel new concept for coupling machine learning algorithms with knowledge bases. The lack of availability of prior knowledge in dynamic scenarios is without doubt a major barrier for scalable machine intelligence. The interaction between the two tiers is based on the concept that when knowledge is not readily available at the top tier, the knowledge base tier, more knowledge cab be extracted from the bottom tier, which has access to trained models from machine learning algorithms.
It has been reported that the need for intelligent autonomous systems – based on AI and ML – operating in real-world conditions to radically improve their resilience and capability to recover from damage. It has been expressed the view that there is a prospect for AI and ML to solve many of those problems. A claim has been made that a balanced view of intelligent systems by understanding the positive and negative merits will have impact in the way they are deployed, applied, and regulated in real-world environments. A modelling paradigm for online diagnostics and prognostics for autonomous systems is presented. A model for the autonomous system being diagnosed is designed using a logic-based formalism, the symbolic approach. The model supports the run-time ability to verify that the autonomous system is safe and reliable for operation within a dynamic environment. However, during the work we identified some areas where knowledge for the purpose of safety and reliability is not readily available. This has been a main motive to integrate ML algorithms with the ontology.
After decades of significant research, two approaches to modelling cognition and intelligence have been investigated and studied: Networks (or Connectionism) and Symbolic Systems. The two approaches attempt to mimic the human brain (neuroscience) and mind (logic, language, and philosophy). While the Connectionism approach considers learning as the main cognitive activity, the Symbolic Systems are broader, they also look at reasoning (for problem solving and decision making) as the main cognitive activity besides learning. Although, learning isn’t the focus of Symbolic Systems, powerful – but limited – methods were applied, such as ID3 (define) and its different variations and versions. Furthermore, the Connectionism approach is concerned with data while Symbolic Systems are concerned with knowledge.
Psychologists have developed non-computational theories of learning that have been the source of inspiration for both approaches. Psychologists have also differentiated between different types of learning (such as learning from experience, by examples, or a combination of both). In addition, unlike in animals (it is difficult to test intelligence in non-human creatures), human psychologists have also produced methods to test human intelligence. Mathematicians have also contributed statistical methods and probabilistic models to predict behaviour or to rank a trend. The subject of Machine Learning (ML) is the bag for all algorithms used to mine data in the hope that we can learn something useful from the data, which is usually distributed, structured or unstructured, and of significant size. Although there are several articles on the differences and similarities between Artificial Intelligence and Machine learning, and articles on the importance of the two schools, there are no real or practical attempts that have been reported in the literature to practically use or combine the two approaches together. Therefore, this is an attempt to settle the ongoing conflicts between the two existing thoughts for modelling cognition and intelligence in humans. We argue that two-tiers intelligence is a mandate for machine intelligence as it is the case for human. Animals, on the other hand, have one-tier intelligence, which is the intrinsic and the static know-how. The harmony between the two tiers can be viewed from different angles, however they complement each other, and both are mandatory for human intelligence and hence machine intelligence.
The lack of availability of prior knowledge in dynamic complex systems of is without doubt a major barrier to scalable machine intelligence. Several advanced technologies are used to control, manipulate, and utilise all parts whether software, hardware, mobile assets such as robots, or even infrastructure assets such as wind turbines. The two-tiers intelligence approach will enhance the learning and knowledge sharing process in a setup that heavily relies on some sort of symbiotic relationships between its parts and the human operator.
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