A large National Oil Company (NOC) spent more than $300 million annually on offshore-related logistics. Logistics planning was performed manually based on limited information, resulting in inconsistent asset utilization rates and sub-optimal routes and vessel chartering. Supply chain decisions were reactive, primarily driven from asset events after they occurred. For example, a recent equipment failure required replacement components to be shipped from an overseas supplier due to lack of inventory from local suppliers. These inefficiencies were a key driver of sub-par financial performance at the rig level, where one unit reported more than 1,400 hours of non-productive time (NPT) in the year related to logistic delays.
Arundo’s data scientists worked with the company’s internal analytics team to build a machine-learning model to predict logistics needs based on equipment operational data combined with forward-looking drilling plans. This model was then deployed and operationalized within the Arundo platform.