Public Funding for Nplan Limited

no public description

The construction sector, encompassing contracting, product manufacturing and professional services, had a turnover of around £370 billion in 2016, adding £138 billion in value to the UK economy -- 9% of the total -- and exported over £8 billion of products and services. However, the potential of the sector has been held back by productivity that is historically below than the wider economy -- an average of 21% lower, since 1975. With a pipeline of £600 billion in infrastructure projects, construction makes a critical contribution to the progress of the UK economy. Impacts as a result of COVID-19 have resulted in a dramatic slowdown to the industry with projects either slowing down or shutting down entirely. A vast majority of these projects will soon be expected to resume to normality. The tension of slowing down and restarting a construction project looms within the complexity of the operation, including effects of the supply chain. It is highly likely that a vast majority of projects will incur significant impacts to their final cost, schedule and benefits case. This project will use data from across the UK's construction industry to identify areas of opportunity within projects, to enable slow downs and restarts to be done as seamlessly as possible. The project will help identify risks in proposed plans, expose them to the project team and help benchmark each project against its cohort, to gauge competitiveness or antifragility. Hypotheses will be fully tested and validated during the course of the project, to ensure that the product developed provides sustainable solutions for stakeholders and customers.

Major infrastructure projects have the potential to transform communities who benefit from them, but the way the construction industry manages risk and executes contracts today has been outgrown by the immensely ambitious, complex, and demanding modern major projects of today. nPlan and Atkins have a vision for an industry with new business models, where risk is distributed fairly between parties, where projects are delivered in line with expectations, and where waste is reduced. To deliver this, these business models have to be underpinned by data and emerging techniques for learning from this data, like artificial intelligence. Recent advances in these computational methods and the reducing cost of computing power means that the ambition to transform the way we deliver complex projects is here, today. nPlan's patent-pending technology applies data science and machine learning to thousands of previous construction project timelines, the largest dataset of programmes in the world, offering a unique scalable solution for improved certainty and confidence in project planning for future projects. This approach means the platform can learn about what was planned to happen and what transpired on projects, enabling a future with reduced human bias, subjectivity and inaccuracy. As a global leader in engineering consulting and programme management, Atkins hold the knowledge and status to make meaningful change in the industry. They are well position to analyse the status quo, identify opportunities for improvement, and the execute on these ideas on their own projects.Together, nPlan and Atkins will leverage our technology, expertise and networks to shape the way modern construction projects are delivered.

"77% of all megaprojects are at least 40% late, and 98% suffer cost overruns of over 30%. Unrealistic and poor planning is acknowledged as a major contributor, accounting for up to 30% of failures for two key reasons: \[1\] limitations in the ability to transfer knowledge and extrapolate experience between projects; \[2\] the complexity of projects themselves. Project performance is critically limited by the knowledge and experience of the project team responsible for building schedules -- and there is a significant cost burden carried due to unverified and subjective allocation of risk. _There is a recognised_ _unmet market need for data driven solutions that can enable improved project planning and scheduling to increase certainty of budgets and timings, increase productivity and reduce costs._ The proposed project seeks to develop a novel automated 'schedule learning platform' that applies data science and machine learning to thousands of previous project schedules, offering a unique scalable solution for improved certainty and confidence in project planning for future projects. The solution is based on thousands of previous construction projects, allowing the platform to learn across projects what was planned to happen and what actually happened, thus reducing the effect of human bias, subjectivity and inaccuracy. Schedule data is analysed, similar tasks and relationships are automatically grouped, with patterns drawn using Artificial Intelligence, enabling the platform to predict the most likely outcome for every task and provide optimal paths/recommendations to mitigate risks/delays. The project builds on nPlan's existing risk prediction platform and their extensive dataset of construction schedules. To meet expressed industry need, government priorities, and to become a fully viable commercial offering with market disrupting potential, it is critical that the platform is technically advanced to enable the capability to recommend optimised pathways for schedule execution to allow informed decisions to be made based on intelligent predictive data-driven planning; and build benchmarking capability to enable sharing of best practice and improved overall performance. This project focuses on proving the feasibility of this technically complex approach through the development of a proof of concept prototype (TRL5) and testing in the field to provide initial validation. Significant benefits are foreseen e.g. increased certainty of schedules (budgets and timings); shorter project execution times and cost reductions (over 30%); reduction of reactive and wasteful work leading to increased productivity. The project will deliver ambitious growth and increased knowledge for all three partners, with further opportunity for R&D investment."

"Industrial productivity has improved over recent decades across most sectors due to process and technology innovation. However, construction has not shown such gains (value added per worker is 60% of that in wider manufacture). Without improvements housing and infrastructure demand will not be met. Conversely, productivity improvements will add significantly to the economy (construction represents c.9% of UK GDP). The size and nature of the sector suggest many opportunities for process and technology innovation. Techniques such as Design for Manufacture and Assembly and off-site construction could significantly improve construction productivity. However, uptake has been slow due to bespoke projects, supply chain complexity and fluctuating demand leading to a risk-averse approach to capital investment through supply chains. Effective planning, and supply chain collaboration are key to ensuring that productivity gains are consistently achieved. We will therefore develop, test and assess an integrated process planning and supply chain management toolkit for the efficient delivery of construction projects. It will improve construction productivity (potential cost and time savings of 25% and 28% respectively) via: * Better project planning; enabling project planners to identify optimum project delivery plans based on context-specific restrictions and supply chain 'pinch-points' where increased capacity/automation could improve overall productivity. * Improved supply chain collaboration; enabling supply chain businesses to securely collect, share and store information, such as task status/completion, component location, and in-use data. The use of 'blockchain' technology will enable smart contracts and timely payments to subcontractors, reducing their financial risk. Analysis of data from on-site sensor networks and through supply chain tagging/tracking systems will provide quantified metrics for planning scenario optimisation and industry-wide KPIs. These metrics will drive innovation, enabling planners to assess project-specific benefits of new digital and automation solutions. The mainstream implementation of such innovative approaches the project solution will help to leverage overall 55-70% savings in programme cost and time. The PLASMA project will be led by construction contractor Vinci, with Skanska also participating. These organisations will provide date from, and access to, ongoing construction projects to ensure that the project solution (made available to the industry as a spin-out) meets industry needs. UK SMEs nPlan and Assentian building on expertise of project planners and in-house innovations from Vinci and Skanska. It will be applicable in other sectors (e.g. Facilities Management). We anticipate revenues of \>30£M pa to the 'spin out"" in 5 years, with the solution used by 10% of the UK sector."