Almost half of managers (47%) are well acquainted with the possibilities of predictive maintenance. Interest in the subject is high; almost two-thirds of managers want to apply this technology in their own organisation. In practice, however, this happens only on a limited scale; only a third of respondents feel that their own organisation makes sufficient use of predictive maintenance.
This is according to research by Axians among 211 managers of manufacturing and industrial companies. When do you perform maintenance on machines? Preventive maintenance prevents machines from failing, but it also ensures unnecessary maintenance. After all, in this case you replace parts that are still functioning and could perhaps be used for longer to prevent problems. Another option is corrective maintenance, in which problems are corrected by carrying out research. In practice, you only replace parts once they have given up the ghost. However, this incurs failure costs and, in many cases, risks.
Maintenance just the right time
Predictive maintenance is an interesting alternative. In this form of maintenance, maintenance is carried out at exactly the right time. This is possible thanks to the use of sensors, which register vibrations, pressure or temperature, among other things. Does one of these values turn out to be unusual? Then this is an indication that maintenance is needed.
The main reason for not using predictive maintenance is by far 'insufficient knowledge'. This is striking, as at the same time only three in 10 managers say they need help implementing this form of maintenance. 37% say they reject help or have no opinion about it. Axians points out that external help can also help overcome the second biggest stumbling block: insufficient data. The same applies to other objections, such as a lack of a good business case or making the translation from theory to practice.
Collecting data
To get started with predictive maintenance, it is critical to collect data from machines. Not yet half of companies have taken this first step. Respondents also reported difficulty in extracting relevant steering information from collected machine and production data.
However, more and more managers do recognise the benefits and possibilities of predictive maintenance. For instance, almost two thirds of respondents label machine downtime as one of their organisation's biggest threats. Four in ten state that the average downtime of machines in their organisation is 'too long'. Many managers see predictive maintenance as an opportunity to reduce this downtime.
Many machines are already equipped with sensors
Axians points out that 'quite a lot of machines' are already equipped with smart sensors that can record the functioning of the machine. These sensors form the basis on which predictive maintenance can be rolled out more widely within Dutch industry.
The survey also looked at Internet of Things, which involves connecting all kinds of machines, devices and systems to the internet. 36% state that their organisation already makes sufficient use of IoT capabilities, while 40.8% state that this is not the case. 34.1% have a clear strategy or policy on IoT, while 47.9% say it is lacking.
More information is available in the research report 'How far along are Dutch manufacturing and industrial companies with
predictive maintenance?" by Axians. The report can be requested here.
Author: Wouter Hoeffnagel
