Data-Driven Predictive Maintenance: Proven ROI and Increased Performance
April 20, 2023
Data-driven predictive maintenance (PDM) has become a critical tool for businesses looking to improve operational efficiency in their supply chains and production processes. By taking a proactive approach to equipment maintenance and leveraging data to identify patterns and anticipate problems in commercial machinery before they become an issue, PDM offers an increasingly attractive return on investment (ROI) for businesses of all sizes.
To understand PDM and its specific benefits, let’s first look at the facts and figures associated with machine downtime in a typical business. According to a Global Industry 4.0 report from 2019, each hour of unplanned downtime costs organizations an average of $6.2 million in lost production and repair costs. Meanwhile, maintenance costs account for an estimated 30-50% of operational expenses for most organizations. These statistics make it easy to understand why predictive maintenance can offer so much value to businesses.
At its core, predictive maintenance is an early warning system or digital twin of machinery built on analytics and real-time data collection capabilities. By tracking the performance of individual machines over time, predictive analytics helps businesses to identify patterns and anticipate mechanical issues before they happen. With the proper data-driven insight, companies can proactively identify components prone to failure and take proactive steps to prevent losses, leading to improved system performance, less downtime, and cost savings.
To get the most out of PDM, companies must have the proper infrastructure in place, which includes the following:
Digital System Infrastructure
The first step in PDM is creating a reliable digital infrastructure to collect data from machines and analyze it for predictive insights. This can be done with various tools, such as sensors, edge computing, IoT networks, and cloud analytics. All of these pieces of the puzzle must be appropriately integrated to get the most accurate data and build an effective predictive maintenance system.
Data-Driven Operational Adjustments
Once data is collected and analyzed, it’s time to make operational adjustments. This can include changing the schedule for preventive maintenance, adjusting component settings to create a system more efficient, or scheduling repairs when a component is likely to fail. Many options are available to adjust operations based on predictive analytics, and the best approach will depend on the type of equipment and the organization’s budget and goals.
Operational Dashboards
To enable monitoring and tracking, it’s also essential to have an operational dashboard that can visualize data and provide an overview of machine performance. This type of dashboard can help executives and operations managers make informed decisions about their PDM strategy and quickly pinpoint areas for improvement.
Improved ROI
The bottom line impact of PDM is improved ROI. Through predictive analytics, organizations can achieve greater machine efficiency, improved cost savings, and decreased downtime. For example, a 2019 report from Fortive’s Fluke division found that businesses that adopted PDM experienced a 49% reduction in downtime, a 72% reduction in operating costs, and 36% in component failure.
PDM is becoming an increasingly important tool for businesses looking to optimize machine performance and cost savings. By leveraging the power of data, predictive analytics, and the right supporting infrastructure, businesses of all sizes can experience enhanced ROI, improved product reliability and quality, and decreased downtime.
At its core, predictive maintenance is an early warning system or digital twin of machinery built on analytics and real-time data collection capabilities. By tracking the performance of individual machines over time, predictive analytics helps businesses to identify patterns and anticipate mechanical issues before they happen. With the proper data-driven insight, companies can proactively identify components prone to failure and take proactive steps to prevent losses, leading to improved system performance, less downtime, and cost savings.
To get the most out of PDM, companies must have the proper infrastructure in place, which includes the following:
Digital System Infrastructure
The first step in PDM is creating a reliable digital infrastructure to collect data from machines and analyze it for predictive insights. This can be done with various tools, such as sensors, edge computing, IoT networks, and cloud analytics. All of these pieces of the puzzle must be appropriately integrated to get the most accurate data and build an effective predictive maintenance system.
Data-Driven Operational Adjustments
Once data is collected and analyzed, it’s time to make operational adjustments. This can include changing the schedule for preventive maintenance, adjusting component settings to create a system more efficient, or scheduling repairs when a component is likely to fail. Many options are available to adjust operations based on predictive analytics, and the best approach will depend on the type of equipment and the organization’s budget and goals.
Operational Dashboards
To enable monitoring and tracking, it’s also essential to have an operational dashboard that can visualize data and provide an overview of machine performance. This type of dashboard can help executives and operations managers make informed decisions about their PDM strategy and quickly pinpoint areas for improvement.
Improved ROI
The bottom line impact of PDM is improved ROI. Through predictive analytics, organizations can achieve greater machine efficiency, improved cost savings, and decreased downtime. For example, a 2019 report from Fortive’s Fluke division found that businesses that adopted PDM experienced a 49% reduction in downtime, a 72% reduction in operating costs, and 36% in component failure.
PDM is becoming an increasingly important tool for businesses looking to optimize machine performance and cost savings. By leveraging the power of data, predictive analytics, and the right supporting infrastructure, businesses of all sizes can experience enhanced ROI, improved product reliability and quality, and decreased downtime.
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