Linking Predictive Maintenance to the Bottom Line

Wireless sensor technology powers condition-based monitoring in thousands of global facilities and detects early signs of equipment abnormalities before they lead to costly repairs or downtime. Even though predictive maintenance is identifying the disruptors that erode overall equipment effectiveness (OEE), many organizations still view predictive maintenance as a nice-to-have exercise.

Implementing cutting-edge predictive maintenance solutions is a high-impact strategy that can help organizations reduce avoidable costs, protect revenue streams and optimize facility investments. Beyond downtime avoidance, predictive maintenance identifies areas to accelerate efficiency and addresses a multitude of industry setbacks, including skilled labor shortages and escalating maintenance and inventory carrying costs. But, just like other enterprise tools, predictive maintenance must be approached with a clear strategy and consistent methodology across the organization.

With the right deployment plan and oversight, predictive maintenance strategies will prove to be more than just a facility-level solution. Instead, they are strategic investments that can enhance productivity and long-term business performance for years to come.

The $11 million alert: Predictive maintenance in action

Downtime avoidance is predictive maintenance's most touted benefit, enabling facility managers to detect and resolve machinery issues before a catastrophic breakdown. Driven by AI, predictive maintenance strategies have the potential to save businesses millions. Owens Corning, a global manufacturing company focused on building and construction products, is a prime example of exactly how a company can leverage predictive maintenance to benefit its bottom line.

In February 2024, Owens Corning’s Belgium-based Tessenderlo Plant received an alert on one of its ball mills, a critical asset operating for more than 40 years. With a predictive maintenance strategy in place, Owens Corning became aware of a significant temperature spike within the machine. Upon immediate inspection, technicians discovered a cracked non-drive-end shaft, a damaged white metal bearing shell and inadequate lubrication in the oil-bath system. The key here? The company found those damages early enough to manage the 17-week lead time needed for replacement parts.

According to metrics generated by Owens Corning, having a corporate-wide predictive maintenance strategy in place helped the company avoid 5,376 hours of downtime, which could have resulted in production losses of more than $9.2 million. In addition, the manufacturer saved nearly $2 million in repair and labor costs, bringing total avoided costs to more than $11.2 million from a single action item alert. This example demonstrates that predictive maintenance is not purely a maintenance strategy but a proven driver of financial returns and business stability.

Smarter maintenance, better margins

While downtime avoidance is a rewarding feature of implementing a predictive maintenance strategy, facility managers often overlook other benefits that promote overall business success. With unprecedented insight into machine health across as many facilities as needed, predictive maintenance is helping entire corporations work smarter and cut back on spending, resulting in a considerable uptick in OEE.

Maintenance in and of itself is costly, sometimes requiring expensive parts, prolonged periods of high-priced labor and, in some extreme cases, major business disruptions. While there is not always a way to automate physical maintenance work, predictive strategies make that laborious machine upkeep as inexpensive and efficient as possible. Generally, predictive maintenance can flag disruptors before they reach the first stage of failure, meaning a simple lubrication, alignment, cleaning or tightening can fix the problem.

In a recent analysis of 35 production lines with more than 17,000 sensors deployed, 60 percent of alerts were generated post-installation (abnormal condition) and prior to Stage 1 defects. This allows for corrective action that extends both the asset life and P-F curve at the lowest possible cost.

Still, maintenance can be finicky. For example, if a machine needs a specific amount of lubricant to remain healthy, sensor-driven alerts will continue activating if maintenance technicians are routinely adding too little or too much. Repeated alerts and analysis of the mean time between action items have helped uncover simple lubrication issues and training adjustments critical for machine health and longevity.

Analyzing the causes of top equipment failures with sensor data allows facility teams to proactively address common issues, like bearing wear, structural looseness and lubrication, before they lead to costly downtime and disruptions.

Predictive maintenance insights can also eliminate other unnecessary expenses, such as inventory carrying costs. Having a thoughtful maintenance schedule backed by data allows facility managers to be more considerate with inventory planning. As predictive maintenance extends the P-F curve farther out, the need for an intense spare parts reserve lessens. Businesses experience significant cost savings annually by utilizing predictive maintenance data to its fullest potential, slashing carrying costs in partnership with their maintenance, repair and operations (MRO) supplier.

Even after saving upwards of $11 million, Owens Corning is the perfect example of leveraging predictive maintenance to its fullest potential by focusing on the long-term business benefits made possible by sensor-driven insights. Once the company’s ball mill was restored, manufacturers utilized that machine data to purchase additional parts for inventory, eliminating any lead time that comes with ordering parts in an emergency situation. In addition, this alert helped the company grasp the importance of maintaining consistent oil levels within its machinery, prompting leaders to install automatic lubrication systems across the entire organization. Today, that response is still critical to preventing repeat failures and saving money.

Owens Corning’s success story underscores a larger truth: predictive maintenance is not just about preventing breakdowns; it is about implementing more innovative and more efficient approaches in general. With clear savings across the board, the case for adopting predictive strategies has never been stronger.

Before businesses can tap into predictive maintenance's full potential, organizations must learn how to adopt cutting-edge technology into their everyday workflows, focusing on both the hardware and the people who work with it. Standardization and consistency are force multipliers.

How to enable efficient predictive maintenance through sensor deployment

A widespread sensor deployment plan provides the best visibility. Some organizations may be tempted to monitor only their most expensive machinery (The critical A list). Still, covering as much ground as possible (A, B and C assets) is the best approach for enabling data-driven results with the quickest ROI.

As a rule of thumb, a balanced plant has an expected alarm detection rate of 1.5 percent to 2 percent of all sensors installed on a monthly basis. If a facility only deploys 100 sensors, managers can expect about two actionable monthly alerts, too few to justify the program’s overhead or catch any emerging issues before they snowball into costly failures. Deploy even less, and alert thresholds can drop to 1 percent, 0.6 percent and lower, making it difficult for the technology to be a strategic lever for efficiency and ROI.

The solution here is simple: maximize visibility and open the aperture. The ideal deployment strategy does not just focus on big-ticket items but also on the dozens of other pathways that lead to them (such as the B and C assets and supporting facility equipment). While the upfront cost for a larger deployment may deter some businesses, larger footprints have the highest and fastest ROI.

Cultivating a maintenance-aware culture

Even with cutting-edge technology spanning an entire facility, maintenance technicians are critical to supporting a successful predictive maintenance strategy that bolsters bottom lines. After all, it is not the technology doing the physical maintenance work; it is the technicians. With customer expectations for fast, reliable delivery at an all-time high, it has never been more critical to optimize labor forces amid shortages and disruptions.

A global e-commerce leader knew that predictive maintenance could be an impactful solution. With millions of customers to satisfy, the company knew it had to enhance its workforce to prevent downtime that would inevitably hamper its profitability and service commitments. So, they deployed a predictive maintenance ecosystem that monitored machine health and lowered maintenance costs through optimized labor and resources. With maintenance delays and workflow disruptions at a record low, the company saved $4.06 million annually on labor costs. In addition, the elimination of traditional preventive maintenance routes and the lack of downtime enabled greater efficiency and improved reliability, truly demonstrating the power of using technology to support not only the crucial work technicians do but also a business' bottom line.

FMs must adopt this technology, especially as skilled labor shortages and increased production demands make it critical for humans and technology to work together. Predictive maintenance insights are the tool that can drive a maintenance team to unprecedented success, but only if the technicians embrace the technology and leverage that data correctly.

A culture that prioritizes urgency and accuracy can bring predictive maintenance to a new horizon, which is why it is important for FMs to bake these ideas into training programs and other learning tools. As predictive maintenance becomes crucial to efficiency, facilities should consider assigning a predictive maintenance champion to each plant floor. This person would be a leader who stewards alerts, coaches technicians and maintains daily visibility on a shared dashboard, ensuring no alert goes unanswered or improperly repaired.

Predict & prevail: The art of doing more with less

Predictive maintenance is well known for its downtime avoidance capabilities. Still, FMs often underestimate or overlook the full value of this insight into machine health and asset performance. The visibility that comes from predictive maintenance at scale should increase productivity by reducing unplanned downtime through strategic alignment, data-driven decision making, training and better resource allocation.

When every minute of uptime counts and every dollar of waste matters, predictive maintenance is not just a nice-to-have; it is a must-have. By leveraging predictive maintenance data in day-to-day decisions, businesses can do more than just stop things from breaking – they can build a smarter, leaner and more resilient operation from the inside out.