The intricate relationship between creating and keeping things running can reveal some challenges. Production is focused on having a steady and uninterrupted process to maximize productivity, while maintenance insists on stopping the machines at regular intervals to make sure they function correctly.
Predictive maintenance, the answer to smart maintenance in industrial digitalization, offers a hand there. But, if the machinery is running, why change something so fundamentally right now?

Step 1: Remove fears

As a machine manufacturer, it is your task in the Industry 4.0 market to convince customers to make changes now. Most companies do not know where to start with their investments and might worry that too much transparency will harm their warranty claims. While talking about Predictive Maintenance, advertising is a minor part of the work. Above all you must allay fears: data-driven systems do not replace jobs!
Working with data requires learning. Without anyone to turn the amassed information and insights into predictions, they stay useless. Companies are often mistaken in thinking that data is automatically dissected by the system and delivered meaningfully. This however is not the case; extracting worth from gathered data necessitates expertise. Analysis can only be automated to a certain extent. Human understanding and the insights of the machinery experts are indispensable. Consequently, alongside artificial intelligence, the notion of ‘enhanced intelligence’ assumes importance: insights are collaboratively harnessed by experienced specialists, empowering companies to make a diverse array of decisions grounded in robust data.

Step 2: Highlight the benefits of investing

The most effective approach for assessing the impact of projects within a company is to evaluate their financial benefits. Predictive maintenance plays a significant role in achieving two crucial objectives:

  • Cost Reduction in Maintenance
    By transitioning from a corrective maintenance model to a predictive one, it involves the early identification of components in need of replacement. This proactive approach minimizes maintenance costs by focusing solely on the replacement of the identified part. This targeted intervention not only saves costs but also mitigates the risk of more extensive damage or complete machine failure

Delving into the details of cost reduction through predictive maintenance:

This includes the possibility of completely skipping certain inspections, leading to time and resource savings

Predictive maintenance helps eliminate the need for expensive emergency repairs, thereby reducing unplanned expenditure

Improved scheduling ensures that resources are allocated efficiently, reducing downtime

Ordering spare and wear parts in a timely manner minimizes delays in maintenance and prevents costly disruptions

Saving both time and money

Early identification of issues prolong the lifespan of the machine components and reduces replacement costs once again

  • Improving machine reliability
    Utilizing predictive models can alleviate uncertainty regarding potential failures, thereby optimizing production planning and ultimately enhancing productivity.

The following calculation is an example of return on investment (ROI) for a predictive maintenance solution:

According to our project references, industrial machines typically achieve a return on investment (ROI) within one to two years, with variations depending on the specific application. Especially for critical process machines, the potential impact on ROI can be substantial.

Are you ready to start your Predictive Maintenance journey? Find out more about our Predictive Maintenance Solutions here.

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