Avoid the 8 most common predictive maintenance challenges

Interest in predictive maintenance for machine operators is growing rapidly in the industry, thanks to advancing digitalization, the rise Industry 4.0, Industrial Internet of Things IoT, AI and machine learning among others. However, embracing these technological advancement don’t come alone. It’s important to get a proper picture of the advantages and challenges of each.

Whether you are an Original Equipment Manufacturer or a plant owner, making the transition to predictive maintenance – though worth it – can seem overwhelming at first. As an OEM talking to your clients about switching to assets with smart sensors, you’ll probably hear some reservations and doubts about the whole process of implementing a predictive maintenance technology.

In this blog, we’ll walk you through the 8 most common predictive maintenance challenges that come with implementing a predictive maintenance technology. We know the transition is a big one, but from experience we know how to tackle the toughest challenges with you to make sure you and your client will benefit from making this change.

Practical challenges in predictive maintenance

1. It's a big investment

When viewed from a business point of view, the costs are usually the first hurdle to overcome. Yes, implementing a predictive maintenance strategy for asset management costs a lot in a few ways.

Whether you are buying sensors to work with the assets you’re currently building, or a plant owner is buying your assets with smart sensors: both are expensive because of the technology and the monitoring software it comes with. Besides that, there will be some costs for training staff to work with this new technology, for building a secure Internet of Things Network (IoT Network) and a few other things you need to get started. For example ,well trained data scientist are needed to be able to make sense of the data collected. 

2. Current assets are not compatible with smart sensors

In some cases the industrial machines you are currently building are not compatible with (our) smart sensors. This means that the assets have to be altered in order to have sensors built into them. It’s not uncommon, and there are multiple ways to update and sensorfy your assets efficiently. Avoiding the challenges in implementing predictive maintenance, starts having a proper approach and maintenance strategy. 

Want to learn more about how to get started with predictive maintenance? Check out our ebook: How to implement predictive maintenance , a step-by-step guide to get started

3. Integration with an operational Business Intelligence System

To have a fully functional predictive maintenance strategy there needs to be a secure IoT Network to send, process and store all the data. Furthermore there is often a need for integrating existing systems, like an ERP system (Enterprise Resource Planning), MES (Manufacturing Execution System) or other supervisory and process control systems, with the new IoT technology and the software that comes with it.

API’s (Application Programming Interface) need to be in place to connect these different systems so they “understand” each other, and integrate all the data. This sounds like a big challenge for implementing predictive maintenance  and depending on the systems, it is. Working with a deeply skilled partner ensures that this integration will go smoothly. Let’s discuss together how we can help you. 

4. Train your staff to work with the new technology

To get started with predictive maintenance it’s not enough to just buy the sensors (for OEM’s) or the new assets (for end users). On both ends the production and maintenance process will change as you switch to PM and therefore the staff will have to be trained to learn how to implement these sensors into the assets, or how to work with the new assets and how to read and interpret the data.

5. Hire specialized staff to control the PM system

If the current (maintenance) staff can not be trained to a point that they’re fully specialized in the new technology, both OEM’s and plant owners need to attract new people with the needed skills to operate the new systems. Some of the skills needed to implement a predictive maintenance technology include knowledge into algorithmic/statistical/programming skills, predictive modelling, data engineering, data science among others.

But’t don’t worry, fortunately, the market is growing rapidly and the pool of people that have the right skillset is growing as well. You don’t have to have the knowledge in-house, you can also get external support. Talk to our experts to see how you can get started with predictive maintenance. 

Mental challenges in implementing predictive maintenance

6. Privacy and security concerns

This is a concern that mostly exists on the end user’s side. Using a predictive maintenance strategy means that a lot of their production and factory data is being collected, and send to and stored somewhere in a database.

We understand that organizations are concerned about the security of this IoT Network and the privacy of their data. Therefore we always advise to store and process as much data on the edge as possible, which keeps the data close to the assets instead of sending it far away to the cloud (learn more about edge computing in this blog). We always focus greatly on securing the network as safe as possible.

7. Maintenance and/or factory staff is reluctant to change

In some cases, the maintenance and/or factory staff might be against changing maintenance strategies. Maintenance staff feels like they know everything there is to know, and that they can tackle any problem with your industrial assets.

In general, people don’t like change and that’s totally natural. It’s important for the staff to know that changing to a predictive maintenance strategy for the upkeep of machines, doesn’t mean their skills and knowledge are no longer needed. Internal communication is preferred to get everyone on board and convinced about the benefits the change will bring.

8. A gut feeling that current downtime is neglectable

Last but not leat, the final predictive maintenance challenge depends on the size and production process of a plant. The plant owner might feel like his assets are doing perfectly fine with the current maintenance strategy, and/or that the current downtime for maintenance is neglectable and is not costing him a lot of money. In this case, a proper calculation of the Total Cost of Ownership to decrease equipment failure could give valuable insight into what would change with a predictive maintenance strategy, and how big the savings are.

A gut feeling that current downtime is neglectable

How to move forward with predictive maintenance?

Now, although there are several reasons why OEM’s and end users have their doubts about predictive maintenance and rather opt for prefer preventive maintenance, there are many benefits to switching. In this blog we wrote all about the benefits of predictive maintenance and we explain why this strategy is the best option in any case. Because costs are always a big issue, if not the biggest, we also wrote a an article about calculating the ROI and TCO in which you’ll find everything you need to estimate when predictive maintenance will start making you money.

Are you considering building assets with smart sensors? Are you already building assets ready for predictive maintenance but are you having issues taking away your client’s concerns? We’re here to talk and discuss any problems you might encounter. Get in touch with us.

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