In this article, we talk about the most preventive trends in asset maintenance, examples of the application of these strategies and the benefits they bring.
Technological advances in recent years have awakened companies’ interest in achieving operational excellence, ushering in a new era in asset management. This is the perception of most asset maintenance company managers who see the possibility of using, analysing and applying the value of data to achieve better efficiency in maintenance operations as a great opportunity.
A study by Ernst & Young indicates that achieving operational excellence could lead to cost reductions of up to 43 per cent in an industry such as oil and gas, and reduce the number of safety incidents by a similar percentage. Operational excellence applied to asset management would lead organisations to benefit from increased asset integrity and improved planning and execution of maintenance activities. This could reduce critical incidents by up to 50 per cent.
Better (and cheaper) to be safe than sorry
The vast amount of data that can now be collected thanks to the IoT and M2M devices, the information storage and processing capabilities provided by Big Data platforms, and the possibilities offered by advanced analytics techniques, are helping to make the future of asset maintenance preventive rather than corrective. The objective is clear: to help companies and public bodies to ensure the reliability of assets by increasing their availability, and uptime and improving their safety; and to reduce equipment maintenance costs. New technologies offer real and tangible benefits and are already a reality.
Factors such as ambient temperature, humidity level, or excessive vibration influence machine performance. The collection and correlation of operational and environmental data provide a clearer picture of machine performance and maintenance requirements, allowing maintenance requirements to be predicted for each piece of equipment. As it is often said, “prevention is better than cure”, and especially cheaper.
The number of beneficiaries of more preventive maintenance is large considering the wide range of assets (transformers, pumps, wind turbines, panels, piping, vehicles ….), and the immense risks associated with their malfunction or downtime. This is why a reliable maintenance strategy is a priority not only for critical sectors such as energy, transport and the public sector but also for others such as manufacturing. An IDC survey of manufacturing companies found that 38% of respondents acknowledge that the approach to their maintenance operations is reactive with a focus on fixing failures rather than preventing them.
There are strong indications, however, that this is changing, and will revolutionise the way companies will deliver asset maintenance services in the coming years, based on a more preventative and therefore more proactive approach.
Example of a preventive-predictive maintenance strategy
Companies such as lift giant Tyssenkrupp Elevator AG have already started working along these lines, moving their maintenance strategy towards a more predictive dynamic. This allows them to identify the elements of a lift that need to be repaired and replaced before unplanned service interruptions occur. To do this, it has also had to update its processes, train its more than 20,000 technicians, as well as modify its service contracts with its customers to incorporate this new way of offering its lift maintenance service.
Scheduled preventive maintenance represents a breakthrough, but it is not the end goal. Prescriptive maintenance is the next big step forward in the evolution of asset management. Prescriptive maintenance systems use algorithms to evaluate multiple variables and provide greater reliability of results. This means moving towards more preventive maintenance by incorporating intelligence into the process, improving efficiency in the planning of maintenance operations to maximise the viability and performance of equipment and optimising the management of material and human resources.
Benefits of advanced analytics in maintenance operations
The benefits of applied analytics are clear, such as increasing the average life of equipment by avoiding major breakdowns. But it also helps to have a better understanding of the assets and what we need to prevent them from stopping working. A better understanding of the maintenance activity allows us to know in advance what resources we will need to carry out the maintenance task (tools, spare parts, vehicles…), the number and training of operators that will be needed and how much time we will need to repair it. With all this information we can develop solutions that allow us to incorporate business intelligence in the automation of the planning process and resource allocation in the scheduling of maintenance tasks, reducing costs, minimising risks and ultimately improving the efficiency of the process. Advanced analysis tools can therefore help us to improve the management of maintenance operations.
For example, suppose a pump component starts to vibrate more than normal or the sound of a machine changes the frequency of its tone, indicating that something unusual is happening. The sensor can pick up that change and analysis of that data using predictive failure modelling (based on manufacturer information, historical equipment data and the behaviour of other similar equipment) detects a loss of performance in the next 36 hours. This gives the maintenance team the opportunity to schedule its repair by sending the operator with the right skills for the repair, with the necessary tools and spare parts for its replacement, and fitting it into the maintenance team’s schedule and timetable. Therefore, the use of solutions based on analytical models will allow us to maximise the uptime of the assets and have information to minimise the number of times that the maintenance teams must act on the pump, reducing the time that the operators will need to repair it. In addition, the system will be able to use this information to automate the scheduling of maintenance activities. In other words, it will be able to optimise the planning of operations, looking for the optimal work plan to minimise the costs of maintenance operations, and adjusting resources to those merely necessary to resolve the incident and to the level of support contracted.
Nowadays, the study of data allows us to detect patterns and trends that can guide us in the development of the most efficient and effective maintenance plans. The future of maintenance operations points towards personalised treatment of assets.
Interested in learning more about preventive maintenance strategies and what they could mean for your company?