In the next few years, we will witness global changes in the industry. Digital solutions are rapidly being adopted, there is a massive shift to automated control systems and more cost-effective maintenance methods are being launched.
For large enterprises, this is a logical step. It will finally make it possible to turn a loss-making business into a new source of economic value. Thus, the optimization of maintenance planning and the introduction of digital systems for equipment lifecycle management will ensure an increase in operational efficiency.
For midsize and small businesses, deploying digital solutions will be a powerful way to reduce costs. According to McKinsey, companies that have introduced new maintenance technologies have been able to reduce service costs by 20-30%.
Let’s look at four avenues to understand what next-generation maintenance will look like.
Many companies with distributed fixed assets have appreciated the benefits of Predictive Maintenance. Even capital-intensive industries with long asset lifecycles practice predictive maintenance practices. They do not wait for equipment to stop, using Industrial Internet of Things monitoring and analytics (Big Data) to build an optimal maintenance schedule.
Where will this lead in the future? Until 2030, the principles of reactive maintenance will finally lose their relevance, and the next development step after Predictive Maintenance will be Prescriptive Maintenance. On the basis of advanced version of artificial intelligence (Artificial Intelligence) and cognitive data analysis tools for maintenance services, systems with RxM functions will be created.
Such platforms can not only predict breakdowns, but also provide dynamic prescriptions for equipment operators and reliability engineers. Artificial intelligence will provide a new level of control over assets and taking into account their condition.
In the future, such systems will become “digital consultants” for maintenance professionals and help implement strategies to optimize targets. This could be MRO costs, overall equipment efficiency (OEE), sustainability metrics, or even environmental performance.
The analysis of operational data will finally become complex. In the future, maintenance will not be based on individual indicators, but on a combination of them. By leveraging the capabilities of next-generation IT systems, engineers will be able to devote more time to deep analysis of metrics.
Artificial intelligence, processing multiple probabilities, can help find a balance between:
– asset performance;
– maintenance costs;
– business risks;
– time of equipment uptime.
Correct work with data will satisfy the needs of not only the manufacturing sector, but also the business as a whole. Already today, analytical systems from SmartEAM are used to optimize end-to-end processes – resource and supply chain management, load planning, asset accounting, etc.
In the near future, green initiatives will be added to the list of urgent tasks. AI-based systems will help in the analysis and regulation of maintenance-related environmental indicators: loss of raw materials, energy management efficiency, equipment life, carbon dioxide emissions.
The main technological trend in TO is the growth of cognitive automation. In the future, there will be an increasing demand for solutions that can provide effective asset lifecycle management with minimal human intervention.
Further development of virtual simulation and remote control tools will allow the creation of autonomous maintenance systems. They can:
– independently diagnose the equipment;
– recognize the context of faults;
– directly initiate orders for spare parts;
– to carry out operational redevelopment;
– to attract specialists with the necessary competence.
The next technological step will be the use of 3D printing to create components on site and drones for field work. However, you don’t even need to wait for the future: some of these technologies are already working in industry.
In the field of maintenance, the number of digital processes will increase, which means that the role of analytical work will increase. As long as intelligent control systems and cognitive automation tools continue to do routine work, maintenance engineers can build predictive models based on the collected data.
Many functions are delegated to machines, but for a long time a person will retain the right to manage processes and resources. The scope of duties of such a specialist will expand, because TO is increasingly integrated into the business model of the enterprise.
End-to-end process management will require the cohesive efforts of different departments. The implementation of such a scheme cannot be imagined without a well-oiled enterprise ecosystem.
This brings us back to the idea that next-generation maintenance will be part of a broader, smarter solution. And you need to prepare for them now.
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