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Case studies

Network Rail, nPlan and the Great Western Main Line Project

How Network Rail used nPlan’s risk management and forecasting AI during delivery of the Great Western Main Line project

Oil and gas megaproject in North America

nPlan has been engaged throughout the delivery and commissioning phases of a major oil and gas megaproject under construction in the Americas. Like almost all other projects, the project has been subject to loss of control, construction delays, unexpected changes and firefighting. Grab the case study to find out how the project team is getting incredible value out of using nPlan's technology to deliver the project.

Download our White Papers

The State of Rail Project Delivery 2023/24

How will rail operators deliver more than $1 trillion worth of new projects globally in the years ahead - while managing costs, providing confidence to stakeholders, and overcoming the limitations of current resource-intensive planning and risk processes while they do it? Grab our latest rail industry report to find out...

nPlan's State of Project Delivery 2022

Exclusive insights from a meta-analysis of nPlan's world-beating dataset of past programmes (over 750,000 and counting!)

Featured Series

Making decisions when everything is affected by uncertainty

In this neat two-parter, nPlan Data Science Lead Gerard Cardoso dives into nPlan's world beating dataset of past project schedules to explore the paradox of project delivery, the impact of 'long tails' on activity and project forecasting, and the impact of using a variety of distributions on the risk management process. Get it while it's hot!

articles
Part 0

The impact of extreme events on project delay

In early 2022, nPlan conducted a study on close to 500,000 schedules and found that around 80% of projects are delayed.

Part 0

PERT distribution leads to overconfidence in outcomes

In my last post, I explored the phenomenon that while most activities on projects are delivered on time, most projects are delivered late. I introduced the concept of long-tails in distributions and how it’s the activities that reside in these long-tails that cause most of the delay in projects.

Latest blog posts

Barry update: A more sophisticated AI agent

Since we launched Barry, our project controls AI, back in June, we’ve shared several videos of it ‘thinking’ and chatting with users - but we haven’t revealed much about what’s going on ‘under the hood’ - until now, that is 😲

nPlan's Barry

14.8.2023

We’re building a diverse team at the intersection of two male-dominated industries — here’s how you can too

Construction-tech is a man’s world — but it doesn’t have to be. People Ops Associate Freya McDonnell reveals how nPlan is trying to level the playing field.

Diversity

9.3.2023

PERT distribution leads to overconfidence in outcomes

In my last post, I explored the phenomenon that while most activities on projects are delivered on time, most projects are delivered late. I introduced the concept of long-tails in distributions and how it’s the activities that reside in these long-tails that cause most of the delay in projects.

Risk

8.3.2023

The impact of extreme events on project delay

In early 2022, nPlan conducted a study on close to 500,000 schedules and found that around 80% of projects are delayed.

Risk

7.3.2023

Watch videos

nPlan CEO Dev Amratia interviewed on Bloomberg News' Markets Today

Finalist for the MacRobert Award 2023

Read our latest research

Data-Driven Schedule Risk Forecasting for Construction Mega-Projects

V Hovhannisyan, P Zachares, A Mosca, C Ledezma, Y Grushka-Cockayne, AACE International Conference 2023

A graph-based approach for unpacking construction sequence analysis to evaluate schedules

Y Hong, H Xie, V Hovhannisyan, I BrilakisAdvanced Engineering Informatics 52, 101625

On Forecasting Project Activity Durations with Neural Networks

P Zachares, V Hovhannisyan, C Ledezma, J Gante, A Mosca International Conference on Engineering Applications of Neural Networks, 103-114

Determining Construction Method Patterns to Automate and Optimise Scheduling–A Graph-based Approach

Y Hong, V Hovhannisyan, H Xie, I Brilakis2021 European Conference on Computing in Construction

How to Calibrate your Neural Network Classifier: Getting True Probabilities from a Classification Model

N Culakova, D Murphy, J Gante, C Ledezma, V Hovhannisyan, A Mosca; Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 3499–3500, 2020.

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