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By John Biggs
November 5, 2018
Although originally developed to answer questions on the TV game show Jeopardy! (where it left human opponents in the dust back in 2011), IBM’s proprietary cognitive computing platform has evolved into one of the world’s premier machine learning and artificial intelligence systems.
Those smarts are being put to good use by Fluor, the global engineering and construction company, who sees a long-term home for IBM Watson to help improve decision making and active monitoring of megaprojects. Through better predictive analytics capabilities, artificial intelligence, and machine learning, Waston is helping to provide a clearer glimpse into the projects’ overall health. The company believes its efforts will result in considerable cost savings for such projects, as the tools they recently unveiled take a good deal of guesswork out of complex projects.
Working across so many complex industries which produce such large streams of data, Fluor is collaborating with IBM Research and IBM Services to develop new tools to better understand that data and extract real value from it in the form of greater efficiency and cost savings for megaprojects of all kinds.
“Harnessing the power of data to make meaningful insights will alter how megaprojects around the world are designed, built and maintained,” Arvind Krishna, senior vice president and director of IBM Research said in a news release. “Together with IBM, Fluor is embracing artificial intelligence as an engine for transformation in data-driven industries that are ripe for innovation including energy and chemicals, and mining and metals construction projects.”
Fluor recently introduced two systems developed as a result of that partnership, EPC Project Health Diagnostics (EPHD) and the Market Dynamics/Spend Analytics (MD/SA). These tools comb through and combine thousands of data points at every phase of a capital project to identify key dependencies and reveal actionable insights.
“The ability to rapidly analyze and comprehend big data that drives decisions at any point throughout the engineering, procurement, fabrication and construction of today’s megaprojects is imperative for the success of our company and the protection of our clients’ capital investments,” said Ray Barnard, Fluor’s senior executive vice president of Systems and Supply Chain.
“And to be the best at predictive analytics and project execution in our industry, we teamed with IBM to create EPHD and MD/SA, an advanced and effective set of diagnostic tools and capabilities that rapidly predict best-in-class pricing globally, project status and outcomes, and improves the quality of services and decision-making as we serve our clients around the globe.”
The systems provide a kind of all-seeing eye, making use of their predictive analytics capabilities to foresee issues before they arise by looking at things like historical trends and patterns of schedule delays or rising materials costs. Using that data, combined with insights gained from myriad other structured and unstructured project data it gathers, EPHD and MD/SA can identify not only the sources of issues project-wide, but understand how any changes will impact every other calculation it’s previously made.
The rollout of EPHD and MD/SA is the culmination of five years of time and money Fluor has invested in developing its artificial intelligence and machine learning capabilities as part of an overhaul of its "entire data-centric journey," as Fluor vice president of Information Management Leslie Lindgren puts it.
"We will be using these innovations on select large and megaprojects to quickly discover trends, patterns and meaning in our structured and unstructured data that deliver competitive advantage through the digital transformation of data into critical information with significant benefits to our clients, other stakeholders and our company."
Advanced data analytics has important applications across construction as companies are realizing there is real value locked up in their mass volumes of project data. By using intelligent, learning computers to analyze that data, that value can be realized. Decisions can then be made using the best possible evaluation of the factors at play, backed by math and statistical analysis that only a computer brain can provide.
Fluor’s multi-year bolstering of its analytics capabilities, and its ultimately turning to a system as advanced as IBM Watson for the job, is a solid example showing the possibilities for construction and engineering firms to lean on data analytics.
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