Prescriptive analytics: Difference between revisions

Revision as of 10:18, 5 May 2022

Revision as of 19:44, 15 June 2022

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===Oilfield Equipment Maintenance===

In the realm of oilfield equipment maintenance, Prescriptive Analytics can optimize configuration, anticipate and prevent unplanned downtime, optimize field scheduling, and improve maintenance planning.<ref>{{cite journal |last=Presley, Jennifer |title=ESP for ESPs |journal=Exploration & Production |date=July 1, 2013}}</ref> According to General Electric, there are more than 130,000 electric submersible pumps (ESP's) installed globally, accounting for 60% of the world's oil production.<ref>{http://www.ge-energy.com/products_and_services/products/electric_submersible_pumping_systems/}</ref> Prescriptive Analytics has been deployed to predict when and why an ESP will fail, and recommend the necessary actions to prevent the failure.<ref>{{cite journal |last=Wheatley, Malcolm |title=Underground Analytics |journal=DataInformed |date=May 29, 2013}}</ref>

In the area of [[Health, Safety and Environment]], prescriptive analytics can predict and preempt incidents that can lead to reputational and financial loss for oil and gas companies.

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==Applications in maritime industry ==

Common Structural Rules for  Bulk Carriers and Oil Tankers ( [https://iacs.org.uk/publications/common-structural-rules/ managed by IACS organisation] ) intensively utilizes term "'''prescriptive requirements'''"  as one of two main classes of checkable calculations by dedicated numerical tools and algorithms for verifying safety of ship hull construction.

==Applications in healthcare==