As per Deloitte report, unplanned down time was costing industrial manufacturers $50 billion per year and cutting plants’ productive capacity by five to 20 percent. With Internet of Things (IoT) technologies, businesses are looking at predictive maintenance as a massive opportunity to optimise systems and reduce costs. But it turns out that when systems run better due to smart maintenance, they also run longer, says Jonathan Wood.

 

If you knew that five to 20 percent of your business value was disappearing into thin air, but a share of that loss could be pulled back into your bottom line, how much would it be worth to you?

 

If you knew that a largely preventable gap in a standard business process was holding your enterprise back, how quickly would you want your team to pinpoint the problem and solve it? Particularly if the short-term solution also opened the door for you to get more use out of essential equipment and postpone costly capital replacements for months or years?

 

With the rise of digital systems and Internet of Things (IoT) technologies, businesses are beginning to look at predictive maintenance as a massive opportunity to optimise systems and reduce costs. But it turns out that when systems run better due to smart maintenance, they also run longer.

 

No more trade-offs

With the tools and techniques available to them until just a few years ago, asset managers dealing with aging equipment and infrastructure often faced a tough choice between adding years to life or life to years.

 

With no precise or reliable way to know exactly when a device would fail, maintenance operations had to strike a fine balance between replacing parts that were still in good working order or jeopardizing the equipment itself by waiting for an obvious failure.

 

Really, it was no choice at all. And the problem was showing up in companies’ bottom lines. Last year, supply chain consultants at Deloitte reported that unplanned down time was costing industrial manufacturers $50 billion per year and cutting plants’ productive capacity by five to 20 percent.

 

“Traditionally, this dilemma forced most maintenance organizations into a trade-off situation where they had to choose between maximizing the useful life of a part at the risk of machine downtime, attempting to maximize uptime through early replacement of potentially good parts, or, in some cases, using past experience to try to anticipate when breakdowns might occur and addressing them proactively,” Deloitte noted. But now, “The rise of new connected technologies can enable machines to do these tasks for them, both maximizing the useful life of machine components while still avoiding machine failure.”

 

Predictive maintenance for the rest of us

Deloitte said the rise of smart, connected technologies has only recently made predictive maintenance possible beyond the largest of companies – making it practical to gather, manage, and act on the significant amount of data the process generates.

 

Now, the combination of affordable digital technologies and extended digital networks allows for deeper data analysis to drive actionable insights. This makes predictive maintenance the new gold standard for asset managers. With lower-cost sensors, computing power, and bandwidth, maintenance managers can quickly collect data from connected machines via diverse sources, such as critical equipment sensors and enterprise resource planning (ERP) systems. This enables them to identify the root of the 

issue which might have gone unnoticed previously.

 

But gathering more and better insights is just the first step in the process: There is no point in collecting data without a clear picture of why it matters and what you plan to do with it.

 

From data gathering to asset management

Predicting malfunctions and diverting maintenance and upkeep to the most pressing issues can only be done by analyzing the right real-time data. The shift in processes can reduce maintenance planning time by 20 to 50 percent, improve equipment up time by 10 to 20 percent, and 

reduce maintenance costs by five to 10 percent.

 

Even if operational efficiencies and cost savings are the initial motivation for a shift to predictive maintenance, there are a series of secondary benefits. A defective device might just stop working, but an essential piece of equipment operating below its optimal performance can turn out substandard product and undercut a company’s reputation. A smart predictive maintenance strategy reduces downtime, maximizes quality, and helps a manufacturer differentiate itself in a competitive marketplace.

 

A vision of the longer-term benefits of that day-to-day strategy came from a report by the World Economic Forum. As a big-picture, global organization, the Forum focused its roadmap on big, strategic infrastructure. But its insights on best practices in operations and maintenance were easily adapted at the plant level.

 

“As most of the cost of providing the infrastructure consists of fixed past investments, and given relatively low current operating costs, each additional year of service will produce high value as the asset is amortized,” the Forum stated. It called on asset managers to invest in both preventive and predictive maintenance, slow down deterioration by avoiding “excessive asset consumption and stress” and enhancing assets’ resilience against disasters and other extreme events.

 

Meanwhile, in Deloitte’s data-driven predictive maintenance strategy, technology was just part of the picture. “Without the foundational building blocks of process and people in place, investment in technology is not likely to yield the desired results,” the consultants warned. “All of the sensors and smart devices in the world are useless unless maintainers know what the values they are reporting mean.”

 

By leveraging technology to create a proactive instead of reactive maintenance strategy, asset managers can increase the 

longevity of mission critical equipment. This will ultimately reduce costly downtime, cut costs and increase productivity.

 

About the Author:

Jonathan Wood is the General Manager, India, Middle East, and Africa (IMEA) at Infor. He leads Infor’s customer-centric strategy and oversees the continued expansion of the Infor footprint outside of Cloud ERP with leading solutions including Enterprise Asset Management (EAM), Infor Coleman (AI), Human Capital Management (HCM), Infor CloudSuite Financials, and Birst analytics.