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.
This week, we had the pleasure of hosting the 3rd edition of the Maintenance Analytics Summit, the first time in a virtual setting. Traditionally PdMA Summit brings together maintenance professionals, experts, practitioners, and enthusiasts who are eager to learn from the latest and most innovative methodologies, tools and case studies in data-driven maintenance and advanced data analytics.
On Tuesday, we are opening up the 3rd edition of the Maintenance Analytics Summit where attendees will have a chance to follow online presentations by some of the esteemed experts in predictive maintenance, condition-based monitoring, IIoT, asset lifecycle management and Advanced Data Analytics.
We are just a few days away from the third edition of the Maintenance Analytics Summit. The inaugural Maintenance Analytics Summit took place in 2018 as a response to the need for a platform where experts can present and discuss innovative case studies on data-driven maintenance in the heavy asset and manufacturing industries.
Hamid Al-najjar, Maintenance Engineer at Seco Tools, will be joining us as one of the speakers at the virtual Maintenance Analytics Summit. During his presentation, Hamid will reveal a ten metrics approach that enables industrial companies to manage assets lifecycle and its performance from the inception to disposal with less cost.
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.
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