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Predictive Analytics Can Improve Cost Estimating for Smart City Projects, a blog

On June 17th, at 10:15am, Anthony DeMarco, Richard Mabe, and Grady Noll will be leading a discussion and presentation on Predictive Analytics Can Improve Cost Estimating for Smart City Projects, at the 2019 AACE International Conference and Expo. Please view more on our LinkedIn page for detials: PRICE ® Linkedin Page.

Here is a taste of the presentation…

Smart city projects will dramatically improve urban living, if they succeed.  Many cities and traditional infrastructure construction contractors are being challenged to understand, estimate, budget, propose and execute complex technology development and deployment projects.  Failure to understand the internet of things (IoT) and other technologies, combined with typical contractor over-optimism, results in under estimates that may lead to disaster.  Urban living will only improve if these projects start out with solid baseline cost and schedule estimates.

New research shows that credible estimates can be achieved by leveraging benchmarks and lessons learned from defense and security command, control, communications, computers, intelligence, surveillance and reconnaissance projects (C4ISR).  New smart city statistical predictive cost and schedule models, along with proven C4ISR predictive models, are a critical resource for city governments and contractors undertaking these challenging projects.  This paper will describe these models and illustrate the value of accurate estimating through a smart city project case study.