With the steep decline in crude oil prices, rising corporate debt, and increasingly strict regulatory standards, exploration, and production (E&P) companies are now faced with the challenge of meeting higher profit targets in a reduced capital expenditure environment. With the latest in sensor capabilities, drone technology, and machine learning science, oil and gas companies now have access to a vast array of their detailed site and asset information. See how Arundo's predictive analytics insights can improve initial site selection, optimize throughput during drilling, reduce risks and improve safety for all employees and stakeholders.
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While other sectors of the transportation industry have already started adopting the latest data science techniques to optimize fleet performance, the maritime industry, which accounts for 80% of all global trade volume, is ready for a digital transformation. Learn how “smart ships” can use Arundo's insights from voyage data, component data, and network structures to optimize shipping routes, prevent downtime, and improve supply chain visibility for 3rd party logistics (3PLs), their partners and customers to improve hull performance and ship's operational performance. The savings in operational expenses from applying Arundo's data science technology on the use cases ranging from bunker optimization for fuel consumption to savings in potable water consumption can be multiple orders of magnitude higher than previously possible.
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In the increasingly complex and changing energy landscape where traditional business models are disrupted, stakeholders from all sectors of the energy landscape must focus on improving asset reliability and performance while minimizing downtime and costs. Fortunately, the accelerated adoption of smart meter infrastructure and declining sensor hardware costs have provided a trove of valuable information to those who understand it. Learn how Arundo's real-time predictive insights on conditional maintenance can prevent asset downtime and how Arundo's machine learning techniques can detect statistical anomalies to improve asset performance and protect our grid.
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