How nPlan uses machine learning in 2019


How nPlan uses machine learning to solve construction’s biggest problem in 2019


By Dev Amratia on LinkedIn


The construction industry is in dire and urgent need of improvement. The 10 largest construction companies in the UK made an average profit of 0.5% over the last reporting year. After Carillion, the next giant is now on the brink of failure and it is worth stopping to think if we, as citizens who rely upon infrastructure, can afford to let the industry continue down this path. What will it mean for the development of our nations? What will it mean for the utility we have come to expect of our infrastructure?

Among the many causes behind the performance of the industry, there is one that seems to be common to all parties. Sponsors, Clients, Contractors and Sub-Contractors are unable to systematically understand the risk they carry and, put simply, this risk materialises into wasted time. A delayed construction project has ripple effects in lost productivity, trust and reputation alongside the legal repercussions, financial loss and organisational breakdowns. Stories of CEOs stepping in to firefight a project going off the rails are commonplace and the continual burning of such fires would halt the progress of any organisation.



Our story at nPlan is centred on improving decision quality by learning from the past. We want to improve the way knowledge is retained and change the industries’ understanding of risk and uncertainty for the better. Over the last 12 months, we have built a software product that quantifies risk and improves certainty in predicting project outcomes. We close 2018 with proof points that our technology works where we demonstrated feasibility on the largest infrastructure project in Europe. We now have 18 partnerships with multinational construction companies, which enables the algorithms to learn from the largest dataset of construction schedules in the world. Without a doubt, teaching an algorithm to automatically understand the historical context of a project purely based on its schedule is a gargantuan technical challenge, which frankly, many have thought to be impossible (more on this in a later blog post).

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Tags: Planning , Analyse
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