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The Next Big Leap in Asset Management Comes with Predictive Maintenance at Scale
Manufacturers that adopt advanced predictive maintenance (PdM) with AI and machine learning are able to more accurately identify possible issues sooner and fix them faster. However, PdM - which enables more comprehensive analysis of very large datasets - also comes with challenges like integration and finding the right expertise.
This whitepaper will tell you how to overcome these challenges and successfully deploy predictive maintenance in your enterprise.
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Predictive Maintenance (PdM) is a strategy that allows manufacturers to monitor and evaluate the condition of their assets to predict when maintenance should be performed. By utilizing advanced technologies like artificial intelligence (AI) and machine learning (ML), PdM can analyze large volumes of data to anticipate asset failures, helping manufacturers avoid unplanned downtime and reduce maintenance costs.
What are the challenges in implementing PdM?
Manufacturers often encounter several challenges when implementing PdM, including a skills gap due to retiring subject matter experts and the difficulty in attracting talent skilled in digital technologies. Additionally, integrating PdM applications with existing systems can be complex, as many manufacturers struggle to connect various asset management solutions effectively.
How can manufacturers scale their PdM initiatives?
To scale PdM initiatives, manufacturers should focus on automating the development and validation of machine learning algorithms. This can be achieved through analytics pipeline accelerators that allow non-data scientists to build models without extensive coding knowledge. Collaborating with analytics leaders who offer scalable solutions can also help manufacturers transition from pilot projects to full production more efficiently.
The Next Big Leap in Asset Management Comes with Predictive Maintenance at Scale
published by Strategic Network Consultants Inc
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