AI-Optimised Pathways for Schedule Execution

Lead Participant: NPLAN LIMITED


"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."

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