![]() Pillar #3: Accelerate data value creation Open data policies and data-sharing agreements empower more people in your organization to use AI and analytics to gather insights, even without a full division of data engineers. Democratizing data resolves this issue by making data accessible and understandable to everyone, even if they aren’t data scientists. Some companies may be hesitant to try AI and analytics because they lack the technical skills to implement and manage it properly-or so they think. ![]() Democratizing your data involves making sure the right people have access to the data they need, as well as the tools to use it effectively. Pillar #2: Responsibly democratize your dataīuilding a common data foundation is only the start. Whether the data will be used for applications, applied analytics, machine learning, or AI models, having a cloud-based data foundation makes it accessible, structured, and ready to use. removing the need for manual data analysis.By bringing together and connecting your enterprise-wide data with the cloud, you can prepare it in one place for whatever purpose is needed.cost-efficient reliable real-time machine monitoring,.quantified return of investment from day one,.The Maritime industry demands a machinery monitoring system that can provide: unexpected downtime caused by recently replaced parts.Ĭonservative maintenance regimes engender: (a) exceptional direct costs, yet, shipowners follow TBM strategy to maintain the warranty on the equipment as well as maintain class and insurance coverage in case of machinery breakdown (b) exceptional indirect environmental footprint.unnecessary purchase and storage of spare parts,.unnecessary maintenance on working machines,.considerable crew time dedicated to machinery inspections,.TBM strategy has significant flaws, such as: Statistically, 89% of yearly machinery faults are not time-related, and 69% of them involve recently replaced parts. However, TBM strategy does not take into consideration that usage and operational environment have a bigger impact than time on machinery. Today 98% of ships and offshore platforms rely on a scheduled, Time-Based Maintenance (TBM) strategy. Maintenance processes used on ships today aim to avoid downtime by upholding machinery in "quasi brand-new state" maintaining assets according to a pre-defined schedule regardless of their condition. ![]() prevent the replacement of functional machinery parts significantly beneficiating the environment.digitally computed machinery insurance premiums, more cost-competitive than the norm.digital class, reducing ship machinery re-certification labour efforts while increasing safety.Gard) thereby digitalised the interaction between this maritime stakeholder enabling the following services: DNV-GL) and marine insurance companies (e.g. Maersk) marine certification societies (e.g. In this feasibility ESA project, we are investigating the viability of a circular business model where the digitalised machinery health data generated by Foresight are shared among shipowners (e.g. This cross-innovation dramatically increases MP commercial potential in the marine sector by introducing disruptive, yet trustable aerospace-certified algorithms in our sensors. Algorithms originally developed to monitor life-critical aerospace components are re-implemented in our product called "Foresight Marine". Machine Prognostics AS (MP) is a Norwegian company engineering machine monitoring systems for the maritime industry.
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