Initially slower to catch up with the data-driven transformation, the heavy-asset industry has certainly accelerated the adoption of predictive analytics systems that provide multiple maintenance advantages such as fewer outages and breakdowns, extended lifecycle of the assets and equipment, better plant safety, and enhance the overall quality of operations and supply chain processes.
The offshore oil & gas industry is exposed to extreme levels of risk. Companies and equipment are subject to volatile price fluctuations, extreme weather conditions and operational hazards like explosions, spills and workplace injuries. This is why the importance of predictive maintenance of the drilling equipment is much more intensified and puts additional stress on offshore maintenance providers as minor errors in predictions may lead to massive scale consequences.
Everyone agrees that there is value in data science and advanced analytics. But still, companies are struggling to see that value in their business. Francisca Zanoguera from Expedia at her presentation at the Data Innovation Summit 2019, draws attention to a McKinsey study showing that only 8% of companies have managed to implement machine learning into their processes and only 12% have managed to go beyond the experimentation phase.
For the second time, on May 23-24, 2019, Stockholm was host to nearly 200 Predictive Maintenance practitioners and key players, gathered for the Maintenance Analytics Summit, an annual event to share ideas, and discuss ways to harness the full potential of machine data and Advanced Analytics to improve and automate their condition monitoring and maintenance processes.
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