It's no longer a secret: data is the currency of the 21st century. It is worth hard cash for companies in all industries. In mechanical and plant engineering, data has the important potential to stabilize sales and drive digitization in the after-sales business. Our series of articles highlights the biggest hurdles and the key benefits of monetizing data in after-sales service. You'll also learn how smartly used information from the field can increase customer satisfaction and open up new revenue streams for all areas of your business.
Today, companies of all sizes and in all industries want to make money from data. Monetization means using the data available in your organization to make a financial profit.
This can be achieved in three steps:
Most founders building a business today are building their business idea on a foundation of data. Whether they're in e-commerce, healthcare, fashion, F&B or consulting, companies need to be data-driven if they want to compete and retain customers over the long term.
It is no longer enough to convince customers with products alone. Today, customers are won over above all by an all-round successful customer experience and first-class service. Even machine and plant manufacturers whose core business is the production of components or the construction of entire factories must ask themselves today: How can we make our service business customer-oriented and future-proof?
There are several reasons for this:
So far, however, very few manufacturers are optimizing their service business on the basis of data. This is because most companies in the mechanical and plant engineering sector are staffed primarily by engineers. Their hearts beat for technology - hardly anyone thinks about data analyses after the sale of a machine.
The example of predictive maintenance shows why a rethink in this area is worthwhile.
Predictive maintenance is essentially about continuously monitoring and analyzing machine and process data in order to predict future maintenance requirements. This allows maintenance processes to be optimized and plant availability to be increased, while at the same time making it possible to plan downtime better and more cost-effectively.
A recent study on predictive maintenance shows that 75% of companies in the mechanical and plant engineering sector are already addressing the issue. Nevertheless, only 4% of companies are really exploiting the potential of predictive maintenance.
The achievable sales figures show that it is worthwhile: Increases of at least 10% are possible here. In addition, companies have been able to reduce their maintenance and service costs by 17% with predictive maintenance.
Predictive maintenance also has advantages for plant operators: reduced downtimes mean significant cost savings. Efficiently planned service calls and transparent insights into the maintenance of their plant components also improve the operators' customer experience.
If machine and plant builders want to make money with predictive maintenance and with data, they need to turn it into a service offering that customers can use for a fee.
More and more companies are using subscription models to monetize data, such as those found at Netflix or Handelsblatt. Another option is the Equipment as a Service (EaaS) model.
In the future, users of machines and systems will no longer buy a pure product, but a complete service package that guarantees them smooth operation of their system. Manufacturers are thus increasingly becoming operators themselves - they no longer sell their machines and systems, but the output that can be achieved with them.
Digital spare parts catalogs offer a much easier entry into the monetization of data: they boost the spare parts business on a data basis and help machine and plant manufacturers to increase their sales in the aftermarket.
Digitization in aftersales is ushering in the era of Service 4.0. The key to this is data that is available on the Industrial Internet of Things (IIoT) and provides the necessary information for first-class service.
In the second part of this series of articles, you will find out what challenges companies from the mechanical and plant engineering sector have to overcome if they do not want to lose out on the monetization of data.
