The Short Guide to Understanding Predictive Maintenance ROI

Predictive maintenance is the holy grail for most manufacturing maintenance teams. But it’s also an expensive strategy to implement. 

If you can’t make a business case for a solid return on investment (ROI) to your boss (and their boss), it’s going to be difficult to get the investment you need to roll out a successful predictive maintenance strategy. But if you can show exactly how the benefits will outpace the costs over time, predictive maintenance (PdM) is a no-brainer. 

In this article, we’ll outline everything you need to know about predictive maintenance ROI and how to present it in a way that’s undeniable.

Key takeaways

• The average ROI of predictive maintenance is 250% when it’s set up and carried out correctly.
• Success depends on a thoughtful implementation strategy, realistic cost planning, and setting appropriate KPIs.
• Manufacturers report a 25-30% reduction in overall maintenance costs and 35-45% decrease in unplanned downtime with predictive maintenance.
• A good ROI requires quality data, robust training, enthusiastic user adoption, and early software integration.

How to calculate ROI for predictive maintenance

Just like any other ROI calculation, predictive maintenance looks simple at a glance:

Predictive maintenance ROI = Benefits - Costs

For example, let’s say it takes $10,000 to implement predictive maintenance on an asset group. Over the course of the year, you save $50,000 in unplanned downtime and lost production with the predictive maintenance technology. That means the ROI is $40,000 or a ratio of 5:1. 

Seems straightforward, right? Except, each component of this formula contains its own complexities and nuances. So let’s define each in detail.

Benefits of predictive maintenance

There are a lot of benefits of a predictive maintenance strategy, from cost savings to extended asset life and improved workplace safety. Here are the ones that will give you a direct return on investment.

Reduced downtime and equipment failure costs

Especially in manufacturing facilities that produce high-value assets, downtime is expensive (and unplanned downtime can be catastrophic). 

Predictive maintenance actively combats unplanned downtime by aiming to avoid it altogether. When carried out effectively, this results in a 35-45% decrease in unplanned downtime.

Downtime is happening less—but costing more.

Even as 74% of organizations report fewer unplanned outages, 31% say those incidents are more expensive than they were last year. Troubleshooting speed and accuracy have never mattered more.

Reduced maintenance expenses

Preventive maintenance is a great starting point for manufacturers looking to move away from reactive strategy maintenance, but even the best PM schedule still contains inspections and tasks that could waste resources. 

A predictive strategy ensures an optimal maintenance schedule where maintenance is only done when a machine is showing signs of wear or a future failure. This reduces maintenance costs by 25-30% by slashing unnecessary labor hours and parts costs while allowing maintenance teams to allocate resources more efficiently. 

Increased throughput

The more efficient your maintenance strategy, the better your throughput will be. Predictive maintenance can benefit manufacturing operations by minimizing downtime and scheduled maintenance, which allows manufacturers to make more, sell more, and watch their bottom line increase.

Extended asset life

Every unexpected breakdown, rebuild, and routine parts replacement puts a strain on equipment life. Predictive maintenance helps reduce that strain by eliminating catastrophic failures and reducing the amount of invasive maintenance performed on an asset. 

Keeping equipment in peak working order throughout its lifecycle extends equipment life and that of critical assets by intervening at the exact right time: not so late that machinery is damaged, but not so early that useful life is wasted.

Improved safety

Every kind of maintenance, from routine inspections to production-halting breakdowns, can put technicians and maintenance teams in contact with hazards, including fire hazards and unpredictable situations that threaten worker safety. 

Predictive maintenance sets the conditions for fewer, more precise maintenance tasks, which minimizes this risk significantly by reducing the likelihood of these instances. 

Costs of predictive maintenance

On the costs side of the ROI calculation, implementing a predictive maintenance strategy is a significant investment. You can expect the following costs throughout your journey to implement and sustain predictive maintenance.

Technology investment

A predictive maintenance strategy needs technology to operate. Each of these solutions comes with its own procurement fees, and some (like machine sensors) may also have associated installation fees.

• Machine sensors that can pick up changes in vibration, temperature, and performance.
• Programmable logic controllers (PLCs) that collect machine sensor data.
• Supervisory control and data acquisition (SCADA) systems, which collect and analyze data.
• Manufacturing execution systems (MES) that can pick up data anomalies and predict failures.
• A computerized maintenance management system (CMMS) that can integrate with each piece of technology above to generate an automated work order when maintenance is needed based on meter readings and predicted failures and anomalies.

Implementation services

Most technology that enables predictive maintenance comes with its own implementation services. Usually, a team will help you get your data organized, connect to other systems, and make sure your technology systems are operational.

Typical implementation costs vary widely based on a number of factors, including the size of the facility, production volume, and the number of assets that require condition-based monitoring. 

Operational expenses

Once you’ve procured and implemented your predictive maintenance program, you’ll have to factor in the ongoing costs of running the technology. This includes costs like:

• Software licensing and subscriptions: Software solutions such as MES and CMMS platforms come with their own ongoing licensing and subscription fees. These fees vary based on the size and needs of your operation, so make sure your vendor is clear and up-front about costs.
• Ongoing sensor management. Just like manufacturing equipment, machine sensors require ongoing maintenance, which could either cost you labor hours or third-party maintenance fees.
• Staff training. Predictive maintenance is much more data-intensive than a preventive or reactive strategy. Frontline technicians should be trained to understand machine data, meter readings, and smart software so that they can integrate it into their daily operations. 

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How long does it take to reach ROI with predictive maintenance? 

Predictive maintenance allows manufacturers to understand and control their costs, activities, and machine health much more precisely than a reactive or preventive strategy. Even so, it can still take some trial and error to accurately predict when a manufacturer will reach ROI.

The more accurately you can account for and predict each cost associated with predictive maintenance, and the better you can quantify and track each benefit that predictive maintenance provides, the easier time you’ll have predicting and proving out ROI. 

A CMMS will allow you to track important maintenance KPIs like maintenance costs, labor hours, mean time to repair (MTTR), mean time between failures (MTBF), and overall equipment effectiveness (OEE). Using these metrics, you can set baselines to track over time and compare against predictive maintenance costs. By tracking ROI this way, most manufacturers see a return on their predictive maintenance investment within 12-18 months. 

Additionally, you can express ROI as a function of a maintenance impact score. This calculation goes beyond costs and savings to account for efficiency, asset health, and risk reduction and paint a fuller picture of predictive maintenance benefits.

Common barriers that delay predictive maintenance ROI

How quickly your team sees the benefits of predictive maintenance depends on many factors. Some roadblocks can slow ROI, but being aware of them in advance can help you avoid them. 

• Data challenges. Getting the right data into your system early on is critical for continued success. If your data is inaccurate, or if your PdM efforts are tied to the wrong assets, your team will struggle to ramp up your efforts and scale them. Make sure you work with an implementation team who can help you set up the right data on the right assets so that no resources are wasted.
• Slow adoption. All maintenance strategies require frontline support to reach maximum effectiveness. If technicians are resistant to new technology or processes, it can impact data quality and system effectiveness. Providing a robust training program can ensure technicians have the information and skills they need to help your maintenance strategy thrive. 
• Integration challenges. Predictive maintenance requires hardware and software integration to work the way it’s supposed to. If machine sensors don’t easily integrate with your CMMS and existing workflows, you’ll have trouble realizing gains. Make sure the technology you’re investing in can be easily integrated with other systems required for predictive maintenance.
• Failing to maximize insights. Predictive maintenance opens up an entire world of predictive insights, but you have to know how to find and act on them. Take the time to understand what’s possible with predictive maintenance, whether it’s leaning on AI insights or setting up reports that can help you find further operational efficiencies.

Key factors that maximize predictive maintenance returns

Setting up predictive maintenance doesn’t mean you’ll start seeing returns right away. But if you’re intentional early on, you’ll maximize returns much more quickly. 

• Prioritize your assets. It rarely makes sense to apply predictive maintenance to all assets at once. Instead, focus on high-impact, failure-prone equipment first. This can help you realize benefits quickly and apply your learnings to other machinery later on.
• Focus on data quality. Predictive maintenance systems need accurate, relevant data to work as intended. Make sure your sensors are well-placed and feed optimal data into your systems.
• Integrate with your CMMS. In an ideal predictive maintenance scenario, any anomalies or warnings picked up by sensors trigger an automated work order that gets assigned to the appropriate technician. This requires early CMMS integration for a truly seamless workflow.
• Set up KPIs early. ROI is much easier to measure—and accelerate—when you know which KPIs you’re trying to improve. Go beyond cost savings and consider where you want to see improvements. When you have clear goals in mind, it’s easier to steer toward them. 

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Calculating predictive maintenance ROI: an example

Let’s run through an example of predictive maintenance ROI that takes both costs and benefits into account. 

In this example, a manufacturer installs a sensor on their CNC milling machine that performs vibration analysis. It’s also integrated with the manufacturer’s CMMS so that any anomalies trigger an automated work order.

One day, the sensor sets off an alarm: it’s detected an anomaly that suggests a spindle bearing is about to fail. A work order is generated in the manufacturer’s CMMS, and a technician replaces the bearing, avoiding a breakdown. 

Benefits

The benefits of this scenario are deferred downtime and deferred emergency shipping for spare parts. Downtime at this manufacturing facility is $10,000/hr, and if the bearing had failed on its own, it would have caused eight hours of downtime. Emergency shipping would have cost another $5,000. The total cost savings in this scenario are $85,000.

Costs

Let’s assume that this is the only failure that this predictive maintenance program catches in a calendar year (unlikely, but easier for our example). The total costs include a CMMS to trigger action, sensor to identify possible anomalies, the labor cost of having a technician replace the bearing, and the parts cost for the bearing. The total costs in this scenario are $35,000.

ROI 

$85,000 - $35,000 = $50,000

Communicating predictive maintenance ROI is the key

Without the right talking points, predictive maintenance can be a tough sell. But if you can clearly show how uptime, throughput, costs, and maintenance maturity will improve over time (and by how much), your work is done for you. 

If you want to learn more about how MaintainX can help you drive a predictive maintenance strategy and reach ROI quickly, book a free tour today.