How maintenance engineers benefit from digital tools

The digital transformation is currently massively changing how maintenance is carried out. New concepts such as condition-based monitoring or predictive maintenance are based on data and its analysis. The aim: fewer downtimes, higher overall equipment efficiency (OEE), longer plant life cycles and, last but not least, better sustainability. The most important added values of digital maintenance include, above all, shorter throughput times and improved data quality, as data is collected promptly on site: Work can be better organized, resulting in faster processes from fault detection and task allocation through to rectification.

New maintenance concepts are based on topics such as sensor technology, IIoT and connectivity, which allow data to be digitally networked and brought together. At present, however, maintenance technicians have generally only arrived at condition-based monitoring, if at all. However, the pressure for digitalization is becoming increasingly noticeable in practice, and more and more companies are tackling the issue of networking. In the case of predictive maintenance in particular, it is mainly individual proofs of concept or lighthouse projects that are being implemented, but widespread use is still a long way off. In order for companies to successfully master the digital transformation, it is important to have concepts that involve everyone involved: After all, the actual work is still done by the people who need to be made enthusiastic about the new solutions.

Digitally networking maintenance planning and execution

However, the digitalization of maintenance is already changing the market and customer expectations and is increasingly becoming a competitive success factor. Many companies still have a lot to do to become fit for this. In practice, it is clear that the planning and execution of maintenance in particular are usually still very separate. Classic Enterprise Asset Management (EAM) has not changed much in this respect. However, if one person plans and the other documents the implementation on paper, this leads to many process breaks and considerable friction losses.

Manual records are often logged in the system, but this does not always work without significant loss of time. In some cases, not all information is recorded, sometimes errors occur during recording and image documentation is also not possible. Important information is then sometimes only available much later. Many companies are currently working on equipping their maintenance teams with mobile applications on smartphones or tablets: an important prerequisite for digital and better networked processes as well as significantly improved data quality.

However, there are a number of hurdles to overcome. On the one hand, on-site Wi-Fi or GSM network coverage is often problematic and is not good enough everywhere. Maintenance software should therefore definitely be usable offline

Involve many different stakeholders

Secondly, in the maintenance environment, third-party employees – for example from external service providers – must also have low-threshold access for their documentation without having to access the core system straight away. Ease of use, minimal training requirements and self-explanatory systems are essential in mobile maintenance.

For customers or store floor employees, on the other hand, the ability to enter tickets via a portal, for example, is particularly important. This means that many different stakeholders with very different expectations need to be involved in the project. For example, management in particular wants a condensed view of the data in clear dashboards, which requires the intelligent evaluation and aggregation of data.

With mobile solutions, service technicians benefit from better results thanks to extensive knowledge management. They can look up the documentation of other tickets on site that deal with the same issue – or the software can automatically suggest possible troubleshooting measures. It is also possible to call in other experts via video. It is important to address different roles: Service technicians on site need clear access via a mobile app, and the information should be tailored precisely to the current tasks and reduced in size. Experts, on the other hand, need in-depth analysis and planning functions.

The use of AI improves planning and forecasting

Digitalization helps dispatchers to plan routine activities into the work programme for as long as possible, in line with resources. For companies with several regional maintenance teams, planning is often already extremely complex: employees need to be coordinated at the right time and in the right place with the right skillset for the job. Machine learning methods are particularly well suited for sophisticated optimization, for example to automatically balance the shortest travel routes, the appropriate tool and material equipment for the vehicles and the skills: Manual balancing, on the other hand, is often very time-consuming. In the age of condition-based monitoring and predictive maintenance, customers also have significantly higher expectations: For example, a maintenance service provider must be able to keep an eye on the standard maintenance that is due in the near future anyway in the event of an acute problem and combine both tasks in one visit.

Minimize costs and downtimes

This means that longer machine or system downtimes can be avoided if necessary – a significant cost aspect for operators, as production downtimes often result in costs in the millions. But from the maintenance provider’s point of view, it can also be cheaper if a team only has to set off once. With a good basis for planning thanks to integrated data and mobile networking, dispatchers can concentrate better on the planned activities and react more quickly in the event of a problem. Practical projects show: In the dispatching of maintenance services, the number of ten technicians per dispatcher can be increased to 18 – reducing dispatching tasks by almost half.

At the same time, more and more attention is being paid to the issue of sustainability. Considerable distances are covered in field service, for example at utility companies such as municipal utilities where smart metering or water meters need to be replaced in a region. Including not only the regular due date, but also geographical aspects, enables more sustainable solutions. Digital maintenance also helps to shorten downtimes in the event of overhauls or unplanned outages. It also contributes to the optimization of larger projects with a large number of dependencies to be taken into account – such as the dismantling of plants.

EAM taken further: asset performance management

In view of the technological possibilities, it is therefore no wonder that trends such as asset performance management – in the sense of optimizing plant availability in terms of operating times and maintenance costs – are slowly but surely being reflected more strongly in practice. However, traditional EAM systems are not sufficient for this. The focus here is on the processing and documentation of planned and unplanned measures on the plant. Asset performance management, on the other hand, keeps an eye on the status of the entire system. For example, an action is automatically triggered proactively and directly in the EAM based on certain threshold values – instead of waiting for a fault to be manually transferred to the EAM. The perspective that can be achieved in this way, which many companies want, is a data situation that enables predictive maintenance.

One of the main challenges of predictive maintenance is bringing together many different approaches and people from different disciplines in one project – including specialist users, software developers and data scientists. Many difficulties also arise when it comes to identifying good use cases. This is where it helps to involve external expertise so that no funds are wasted on POCs that ultimately do not become productive practical solutions. The rule of thumb is: just get started! Getting started with the first stages of digitalization, such as condition-based monitoring, is often much easier in practice than expected.