What Is the Return on Investment for Predictive Maintenance?
Answered August 20 2020
Data from the U.S. Department of Energy indicates that predictive maintenance (PdM) can yield a potential return on investment (ROI) of roughly ten times the cost. However, that figure ultimately depends on your facility’s layout and requirements, how your PdM program is implemented, and what kinds of outcomes you’re looking for.
To get a better idea of what the ROI for PdM could be in your facility, let’s take a look at a few real-world examples.
Case Study: Tetra Pak
Food packaging and processing company Tetra Pak implemented predictive maintenance in order to better serve their customers. The company provides food packaging machinery, parts, and services to manufacturers worldwide.
Costly Maintenance Processes
In addition to their packaging materials, machines, and parts, Tetra Pak also helps their customers maintain their equipment. However, given that they have customers across the globe, servicing equipment could often incur high transportation costs if it meant getting a specialist on site. They needed a solution that would allow them to efficiently help their customers while at the same time improving reliability.
How Predictive Maintenance Eased Expenses for Tetra Pak and Their Customers
In order to better serve their customers, Tetra Pak implemented a number of digital technologies, including remote monitoring and cloud computing.
Using a cloud-based platform, they now gather and analyze data from equipment sensors worldwide, allowing their data scientists and specialists to pin down critical thresholds for each type of machine. Those specialists can then work closely with on-site technicians to resolve problems before they develop.
Not only does this spare them the expense of sending specialists to examine equipment on site, but it also helps reduce the costs of unplanned downtime that their customers would otherwise face.
The benefits have been astronomical. For instance, one dairy company they work with was saved over 140 hours of downtime thanks to their ability to predict machine failures in advance.
Case Study: Mueller Industries
Mueller Industries manufactures copper, aluminum, brass, and plastic products which it distributes worldwide. The company has grown to an enormous size since its founding in 1917 as it has satisfied the demands of wholesalers, retailers, and original equipment manufacturers across the globe.
Moving from Preventive Maintenance to Predictive Maintenance
Mueller traditionally used a preventive maintenance approach when it came to maintaining its equipment. However, given how much the technology had advanced—including in terms of its affordability—the metal manufacturer decided it was time to start using condition monitoring and predictive maintenance to care for its equipment.
Handheld Instrumentation Backed by Machine Learning
The solution came in the form of handheld contact microphone sensors linked with a smartphone app. These devices allowed technicians to measure vibrations on equipment while out on the production floor and get readings directly on their mobile devices. That data would then be uploaded to a cloud-based database where advanced machine learning algorithms would process the data and determine what types of problems might be present.
As they implemented this system, they found issues that hadn’t been readily visible before, such as rapid bearing wear on their high-speed machines. By detecting these issues early on, they were able to repair them with minimal downtime.
Case Study: Daimler Chrysler
Automobile manufacturer Daimler Chrysler needed to procure over 600 machines for a new plant in Toledo, Ohio. Making sure that equipment was in good condition was a major priority for the manufacturer.
Infrared and Vibration Analysis on New Equipment
In order to evaluate the condition of the machines to be purchased, they used infrared and vibration analysis to collect the necessary data. That data was then processed and analyzed through the use of advanced predictive maintenance software.
Modern Predictive Maintenance Software Turns up Much-Needed Fixes
By using advanced analytics to assess the condition of their machines, Chrysler was able to find issues with over 100 of their machines, namely alignment problems, poorly sized shims, and bad/worn bearings. By detecting these problems early on, they were able to take care of them quickly and save the plant thousands of dollars—estimated to be as much as $112,000—in repair costs.
Add to that the potential for lost production time due to breakdowns, and the savings were very much worth the initial investment.
Areas Where Predictive Maintenance Can Reduce Costs
By looking at these examples, we can see a few ways in which predictive maintenance benefits manufacturers. Some of the primary ways PdM reduces costs include the following.
The purpose of proactive maintenance in general is to keep equipment in good running condition and prevent breakdowns. Predictive maintenance is the most effective way to do this since it closely monitors the condition of equipment through infrared, vibration analysis, ultrasound, and oil analysis and assesses that data to predict what failures may occur in the future.
This gives maintenance teams a heads up on what tasks need to be performed to prevent major breakdowns, ultimately allowing preventive maintenance to be completed at optimum times rather than during an unplanned breakdown event. The end result is less lost time, higher equipment availability, and more consistent production.
Higher Maintenance Productivity
Given that PdM helps maintenance teams target preventive maintenance on only the most important tasks, it not only reduces losses from downtime, but it can also improve maintenance productivity. Less time is spent on machines or inspections that don’t actually need it, and the tasks that are performed are more effective.
As a result, fewer resources are wasted, which in turn can reduce overhead for the facility’s maintenance costs.
Fewer Spare Parts Used
In addition to streamlining the time spent on proactive maintenance tasks, PdM also helps reduce MRO inventory spend by as much as 10%. Since parts are replaced only when strictly necessary, there is less spend needed to keep spares stocked, which can further reduce overhead and allow resources to be directed toward reaching central organizational goals.
Improved Workplace Safety
With fewer equipment breakdowns, workplace safety improves. Not only are the direct risks of an actual breakdown event reduced—as may be the case with equipment that’s moving at high speeds or operating at high temperatures/pressure levels—but secondary hazards can also be avoided.
For instance, if a machine breaks down during production, it needs to be fully shut down in order to make sure the needed repairs are performed safely. While proper lockout/tagout procedures can make sure that happens, they may not be totally foolproof. The safest situation is to completely avoid the need to shut down equipment during production in the first place, which is what PdM is intended to accomplish.
More Consistent Product Quality
With fewer equipment breakdowns—and even with minor faults being taken care of early on—there is less of a possibility of product defects being introduced during production. Naturally, that means less waste in terms of materials and production time, less rework, and improved profitability from your processes.
This benefit comes through especially in automated processes where a minor issue somewhere in the line could result in flawed output that isn’t detected until it comes back out the end. By noticing and resolving those issues early, those flaws are prevented.
Greater Energy Efficiency
Equipment kept in top condition with targeted maintenance tasks tends to run more efficiently than machines that are allowed to wear out over time. Any issues that may lead to a breakdown may also cause the equipment to use more energy than they optimally should. Naturally, being able to resolve those issues quickly means preventing lost energy, further reducing overhead costs from operations.
Cost of Predictive Maintenance Implementation
While predictive maintenance does have a great deal of potential for reducing waste and improving efficiency in your facility, it does come with its upfront costs. However, those costs are significantly lower now than they were when PdM first got its start, since the equipment and software are much more accessible.
The cost of PdM comes from the following.
The sensors and devices used to monitor your equipment present a significant part of the cost, particularly if you’re fitting sensors to a large number of machines. However, some of that expense may be reduced by the fact that many newer machines have built-in sensors, meaning the only costs you’ll need to incur for those will involve connecting them to your analytics system.
IT is another point of concern here since you’ll need to make sure the readings from your sensors make it to your database, whether that’s an on-site PdM software system or a cloud-based solution.
Software and Analytics
Central to PdM are the software and analytics systems used to analyze the data gathered by your sensors. Implementing new software can be a lengthy and expensive process, and that will likely drive up the upfront costs. It helps to have a well thought-out implementation plan in place to make sure the process goes as smoothly—and cost-efficiently—as possible.
Fortunately, cloud-based solutions make installation much easier. If you go that route, you’ll likely curb a significant portion of the costs of implementing PdM.
Once your hardware and software are set up, you’ll need to start gathering data. Modern PdM is based on machine learning artificial intelligence using vast stores of data to make predictions about future failure events. As such, you need some form of database in place.
With a purely in-house solution, that means taking time to allow the system to gather data on meter and sensor readings, failure events, and so forth.
However, it is possible to benefit from existing databases in this area. Many cloud-based solutions already have significant stores of data that can be used to get your PdM program off the ground quicker.
Finally, PdM implementation is most successful with proper training and employee buy-in. It’s important to make sure your maintenance team understands the purposes and benefits of predictive maintenance, which include, but aren’t limited to:
- Making their jobs easier.
- Making their jobs more effective.
- Improving the reliability of your equipment.
- Making your facility safer.
Your team should also be trained in using the sensors, hardware, and software needed to make the predictive maintenance process work.
Calculating the ROI of Predictive Maintenance in Your Facility
The exact return on investment you’ll see in your facility depends on the scale of your operations, which machines are critical to maintain, how much you’re currently spending on maintenance, and so forth. As such, you’ll need to account for many factors when calculating the ROI of predictive maintenance.
Gather Data on Current Maintenance and Operations Costs
It helps to start by gathering data on the current costs of maintenance and operations in your facility. Consider factors such as:
- Maintenance hours spent on reactive maintenance
- Maintenance hours spent on preventive maintenance and inspections
- MRO inventory spend
- Unplanned downtime
- Production rejects and reworks
Looking at key indicators and maintenance data in your CMMS can give you a good start on assessing these costs.
Analyze the Types of Sensors and Analytics Available
Next, determine what types of sensors and analytics will work for your facility. In particular, consider how you might monitor the health of your most critical assets. That should inform the types of sensors and analytics software features you’ll want to implement, giving you an idea of the implementation costs.
Determine What Areas Might Be Improved by Predictive Maintenance
By looking at areas of your maintenance and operations that could be improved by PdM, you’ll start to get an idea for how it might improve your processes, preventive maintenance, and so forth. Those areas of improvement will give you a sound starting point for calculating potential savings.
Benchmarking with similar facilities in your sector and general data on the benefits of PdM can also help you get some estimates on how much costs may be reduced after implementation.
Predictive maintenance promises a high return on investment, but the exact shape it will take in your facility influences that figure quite a bit. Even so, it is possible to get a sound idea of how PdM will improve your facility’s operations and maintenance practices.
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How do sensors and actuators work together?
When working in tandem within a given system, actuators receive signals from sensors and perform some kind of task based on that input.
What industries can use IIoT sensors?
Any industry that uses or maintains equipment can make use of IIoT sensors. A few of them include agriculture, manufacturing, and retail.
How are sensors used in predictive maintenance?
Predictive maintenance (PdM) typically uses data from sensors that monitor various conditions on equipment. Algorithms analyze data to predict maintenance.
What are the up and coming IIoT projects in the near future?
The most exciting IIoT projects on the horizon are for maintenance and training tasks and improving energy management with AR.
What do I need to get started for a predictive maintenance (PdM) program?
We talk a lot about planning in implementing maintenance strategies, and predictive maintenance (PdM) programs are no different.
How can my facility use acoustic analysis?
Acoustic analysis has fewer applications than PdM-tool vibration analysis, but what it lacks in breadth of application it makes up for in effectiveness.
What are some industry use cases for vibration analysis?
Amongst the tools in the predictive maintenance (PdM) toolkit, vibration analysis sees tons of use because of its extremely wide variety of applications.
What are the best IIoT projects to start with?
The best Industrial IIoT projects to start with are small ones that meet a specific business need. Once successful, you can increase the size and scope.
How much does deploying IIoT at my business cost?
Industry experts say that deploying an Industrial Internet of Things (IIoT) will cost a minimum of $50,000 or roughly 10 percent of your information technology budget over three years.
What are the benefits of IIoT?
Early implementers of the Industrial Internet of Things (IIoT) have reported better protection of assets, and raised levels of reliability and performance.
What are barriers to IIoT adoption?
The top five barriers to IIoT adoption are cybersecurity issues, a lack of standardization, an installed legacy system, high upfront investment, and a lack of skilled workers
What is prescriptive maintenance and how does it differ from predictive maintenance?
Prescriptive maintenance, is a maintenance concept that analyzes an equipment’s condition to create specialized recommendations to reduce operational risks.
What’s the easiest way to start a predictive maintenance program?
Start with your most critical piece of equipment, track information related to failures, and set up alerts to generate work orders to prevent breakdowns.
What are the biggest problems IIoT could solve for maintenance departments?
Each of these challenges can be alleviated through proper application of IIoT technology, so let’s run through each one starting from helping managing cost.
Will Industrial Internet of Things (IIoT) replace SCADA?
If it does happen, it will probably take a long while, mainly because it would involve uprooting one well-established system in favor of installing another.
What is machine learning and how does machine learning work with predictive maintenance?
Machine learning allows for more intelligent ways of processing data to predict when an asset will require maintenance.
What is the difference between Industry 3.0 and Industry 4.0?
In terms of the words themselves, Industry 4.0 refers to the fourth industrial revolution. The term was coined in 2011 to represent the role that cyber-physical systems (CPS), cloud computing, and IIoT (industrial internet of things) will have on manufacturing processes.
How do I incorporate predictive maintenance without sensors?
Almost by definition, predictive maintenance uses sensors, but the core principle of PdM doesn’t necessarily depend on them.
What are common use cases for using a mileage sensor in predictive maintenance?
If your business maintains a fleet of vehicles, you’ll want to use mileage sensors to trigger regular inspections, fluid changes, and replacements.
What are common use cases for using a voltage sensor in predictive maintenance?
One use case is power failure detection which can create significant downtime losses, and immediate notification can help minimize larger problems.
How do I select assets for predictive maintenance?
Choosing assets for predictive maintenance is a matter of priority, especially starting out. A few of the factors you’ll want to look at include:
What are the most common types of IIoT sensors available?
Dozens of sensors are already available to monitor, track, and report on critical aspects of your operations with more under development each day.
What are common use cases for using a vibration sensor in predictive maintenance?
Vibration often signals a potential problem within production facilities that can result in future breakdowns or shorter equipment lifespans.
What are common use cases for using a pressure sensor in predictive maintenance?
Pressure sensors alert maintenance teams when the pressure in a certain tank or piece of equipment falls outside of a specified level,
What is the difference between IoT and IIoT?
Given the specific demands of industrial settings, IIoT needs to be more robust and flexible than most IoT devices. Characteristics that set them include:
What are common use cases for using a temperature sensor in predictive maintenance?
Most equipment don’t fare too well when temperatures get too high or too low, so even using a simple thermometer can be useful for detecting issues.
How do you improve operations with IoT and predictive maintenance?
The problem with PM is it’s based on the assumption that equipment failures occur on a schedule. The reality is that only 18% of assets fail based on age.
What’s the association between IoT and predictive maintenance?
Using interconnected technology allows us to network cameras and sensors easily with existing computer systems, creating automatic maintenance events.
What are some failure prediction models in predictive maintenance?
With predictive maintenance (PdM), it's understanding an asset's most probable failure modes and monitoring those conditions.
How do you apply continuous improvement to maintenance?
If you’re not focused on continuous improvement each and every day, it won’t be long before you’ll be wasting a significant amount of time and money.
What is the difference between predictive and preventive maintenance?
Although predictive maintenance is similar to preventive maintenance, this activity requires particular preset conditions.
What is level of repair analysis (LORA)?
Without getting too technical, level of repair analysis, or LORA, is a process used to determine when and where an asset should be repaired.