This applies to you as an Original Equipment Manufacturer (OEM) and especially to your customers. Why would your customer invest in assets with smart sensors and a condition monitoring system and why would you decide to build these? Let’s take a look at all the costs and gains of a predictive maintenance strategy and how you can build your own business case to determine the return on investment and total cost of ownership.
Before we get to the numbers part, a quick recap. Many industrial plants these days still use a preventive maintenance strategy for their asset management. A mechanic comes by every few months for a scheduled maintenance check of industrial assets, whether or not the machine is working fine or has a failure.
With predictive maintenance the assets have smart sensors on them that constantly monitor the condition of a machine and send that data to the cloud. By analyzing that data you can predict when and where an asset is about to fail, thus only requiring downtime when necessary. Click here to read more about different maintenance types and all the ways predictive maintenance will save you money.
Compared to reactive or preventive maintenance the costs of implementing a predictive maintenance program are higher, but it’ll save an organization money in the long run thus increasing profit. There’s two players in this story: the OEM and its customer, the plant owner/end user of the asset.
An OEM that builds machines without smart sensors usually sells the assets for a lower price and then makes money through a service contract and spare parts. Scheduled plant visits, asset checks and preventive upkeep are meant to keep machines from failing in the future. This is where part of the OEM’s profit comes from and where a big part of the maintenance costs come from for the plant owner.
Developing a machine with smart sensors requires a different approach for the OEM, therefore the initial cost of the asset goes up. On the buyer’s side it also requires setting up a secure network, training staff, changing procedures and more. It’s the returns that make it worth it though.
The US Department of Energy has reported on studies that give insight on average gains that can be realized by a predictive maintenance program. The most important number they’re reporting is a projected ROI of ten times the investment. Clearly, the exact outcome depends on the type of industry and size of the plant. However, even when you skip actual numbers and look at average savings percentages, the numbers don’t lie. Based on survey results, these are the industrial averages as a result of a predictive maintenance strategy.
In most business and marketing blogs the Return On Investment (ROI) is spoken about as the most important factor for making business decisions. This makes a lot of sense when it comes to business services, but for industrial organizations we prefer to calculate the Total Cost of Ownership (TCO). This is due to the calculation of ROI; it doesn’t account for certain elements that are industry specific when it comes to asset management. The TCO takes the calculation of the ROI a step further and balances these industry specific elements with the annual benefit. Therefore TCO is a more accurate and complete indication of the results of your investment.
Calculating the TCO of your investment is easier said than done. There are many factors you need to take into consideration and you need to know the exact or expected numbers of each of those factors. To help you build your own business case, we’ll briefly discuss a way to do this.
Determining the ROI and/or TCO is all about the difference of financial gains and costs between the present and the future situation with your new investment. To get the full picture of this there are different ways to build your business case. Which one applies to your business depends on how important timing is and whether you’re going to monitor an individual asset, a group of similar assets or a group of dissimilar assets. For this blog we’ll stick to the TCO/ROI for an individual asset.
The costs of a predictive maintenance strategy can be divided into initial or onetime costs and recurring costs. Initial costs are the payments you make upfront to implement the technology, such as the machine cost, installation, training, hardware replacement etc. Recurring costs will return every year and are made up of maintenance costs, energy use, labor/management and more. Part of these costs can be outsourced to the OEM (for example) who’ll take care of the data analysis and repairs.
The main value drivers for predictive maintenance are: Reduced maintenance costs, reduced capital expenditure, improved safety/reduced risk, reduced operational costs and increased overall equipment effectiveness. For your business case you need an estimation of these values and that’s difficult to specify upfront. You do not yet know how often the asset will break down, how accurate the predictive maintenance technology can predict failures, how much longer an asset will last with the new technology and so on.
To get a general idea of the difference in maintenance costs and therefore the benefits of your investment, take into consideration all the different failure types of the asset and whether or not the new technology will improve its sensitivity (the percentage of failures that are identified beforehand). For each failure, estimate how often they happen now and how often you expect them to occur with the predictive maintenance strategy in place. Then calculate the old and the new sensitivity, and estimate the costs of the situations in which the failure was foreseen (costs are most likely lower with the investment because the new technology ensures you a more ideal response time) and in which the failure was unforeseen (costs are most likely the same in the current and future situation). Take all different costs into account: Analysis, repair, downtime, lost production etc. For each failure you now have a comparison between the present and the future and therefore you have an estimation of the amount of money saved when investing in predictive maintenance.
Still, there will always be alarms whether they’re true or false, even with a predictive maintenance strategy in place. Identify the costs of these alarms and how you deal with them. You need to determine how much unnecessary maintenance and additional inspection is caused by these alarms, both in the old and new situation. This will decrease the benefit of the reduced maintenance costs.
Besides the savings made from reducing maintenance costs, determine all other ways the new technology will change and benefit operations. Think about reducing the amount of inspections, the benefit of extending periodic maintenance intervals or skipping them altogether, lower labor costs, having to keep less excess inventory, not needing a big inventory of spare parts, lower insurance premiums, reducing energy waste and extending the asset’s lifetime.
The ROI and TCO of investing in a predictive maintenance strategy clearly are important for the end user of the asset, the OEM’s customer. Additionally, there are also benefits for OEMs making and offering assets ready for predictive maintenance.
First of all, the ability to gather machine data and analyze this data from a distance greatly increases the service you can provide to your customers. Whereas before a machine failure would require a thorough investigation on site to find the source of the problem, a remote monitoring system enables you to visualize the issue and location of the failure with help of the data. Obviously a higher service level that saves time, saves the client money. Better service, lower costs: who wouldn’t want that?
Second, because of all the benefits predictive maintenance has for asset owners, selling these assets creates a competitive advantage for an OEM. Predictive maintenance is a unique selling point because it shows customers your ability to extend machine life and provide efficient maintenance service. Although the initial investment is higher, it saves a lot of money in the long run, as stated before. An OEM can assist a (potential) client in building a business case to determine the added value of investing in its assets by providing data and estimations.
Lastly, machines with smart sensors make a plant ready for the future. Technology is rapidly moving forward and with these sensors plants and OEMs are ready for more innovations that will increase productivity.
Want to know the exact ROI and/or TCO for one of your customers? We’re always happy to help out with specific business cases and go deeper into calculations with accurate numbers. Collaborate with our team at Sensorfy to find the best predictive maintenance solution for the assets you build. Get in touch with us.