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Leveraging Data Science to tame the Maintenance Monster in the Digital age

MMS Founder

Article originally posted on Data Science Central. Visit Data Science Central

The manufacturing industry is overwhelmed with the advent of machines, technology, and the internet. The fourth industrial revolution is set to make manufacturing more digital, and only intelligent machines can make this happen. With the rise of machines, robotics, more automation, agile manufacturing, and increasing customization, manufacturing operations have become very complex. This demands better performance and reliability, especially for asset-heavy industries, where the reliability of industrial assets is mission-critical. At the same time, industry is experiencing revenue and profitability challenges that call for enhanced operational reliability and lower maintenance costs. However, reliability is a complex function, it involves monitoring, control, and improvements over the entire asset lifecycle – from acquisition, commissioning, operations, and maintenance, through disposal.

There is only one answer and that is asset performance visibility.

Management thinker Peter Drucker once said, “you can’t manage what you can’t measure.” This rings true for maintenance operations. You can’t improve reliability or performance unless you have complete visibility with respect to asset performance. In the past, industry professionals spoke about asset visibility more in terms of tracking the physical location of critical assets. But in the digital age, it is possible to monitor location, status, and performance in real time.

  • The business benefits of the asset visibility program are not limited to productivity and safety; they are more about reliability and profitability. The benefits could be as high as a 7% improvement in return on assets (ROA) and 11 % improvement in overall equipment effectiveness (OEE). There are several key benefits you should consider when creating a viable business case for the asset visibility program:
  • Improved reliability of maintenance processes based on operational insights
  • Reduced overall maintenance cost thought-focused analysis
  • Ability to identify root cause, leveraging diagnostic analytics capability
  • Accurate predictions and timely prevention of asset failures
  • Improved performance through KPI and best-practice benchmarking
  • Building a foundation for Industry 4.0 by leveraging IoT-enabled solutions.

Asset visibility is not a new concept. Organizations have been investing in visibility solutions for over a decade. The challenge has always been to gain actionable insights. Thanks to the digital technologies available today, the Internet of Things, sensors, the cloud, analytics platforms, and mobile solutions make asset performance visibility an easy task. IIoT and sensors capture data in real time, analytics platforms with cloud computing analyze massive amounts of data in no time, and mobile solutions make insights available to people instantly. That’s the power of digital – connected operations with real-time collaboration.

Research suggests that only 20% asset-intensive companies have invested in asset visibility programs.  Gartner predicts that there will be 20.4 billion connected “things” worldwide by 2020. This would certainly mean more assets, equipment, machines, and devices to monitor and more data to analyze and visualize. Organizations will fail if they focus only on technology solution (tools) to perform analytics. The key to a successful maintenance visibility program is a structured approach to:

  • Identify key performance indicators (KPIs)
  • Define KPI calculation logic
  • Identify correct data elements
  • Streamline asset hierarchy
  • Design dashboards and reporting structure
  • Devise role-based access and continuous performance monitoring and improvement.

We can summarize with a quote from Elon Musk: “The future will be dominated by autonomous maintenance, machine capable of fixing itself.”  But will this be possible without asset performance visibility?




The original article was published here.

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