Market trends

EAM in constant evolution

Enterprise Asset Management (EAM) is a constantly evolving field. While specific trends may vary from industry to industry and company to company, here are some general trends in EAM:

IoT (Internet of Things) and connected sensors

The increasing integration of sensors and IoT devices into equipment enables real-time data to be collected on the performance and condition of assets. This facilitates predictive maintenance and proactive asset management.

Artificial Intelligence (AI) and Machine Learning (ML)

The use of AI and ML in EAM enables large amounts of data to be analysed to identify patterns, improve predictive maintenance, optimise maintenance schedules, and make more informed decisions.

Cloud Computing / SaaS offering

Migrating to cloud-based EAM solutions offers increased accessibility, improved flexibility and reduced costs associated with maintaining on-premise infrastructures.


Mobile applications are playing an increasingly important role in asset management. Technicians can access asset information, enter maintenance data and receive alerts on the move, improving operational efficiency.

Sustainability and risk management

Organizations are increasingly integrating sustainability criteria into their asset management, taking into account environmental impacts and assessing asset-related risks.

Integration with other systems

The seamless integration of EAM with other enterprise systems, such as Enterprise Resource Planning (ERP) systems, improves the overall visibility and efficiency of operations.

It is essential to note that the evolution of EAM trends depends on technological advances, specific industry needs, and changing business requirements. Organizations need to keep abreast of developments to get the most out of their asset management systems.

Partners & Customers

They trust us

Join many supported companies

Predictive Maintenance

L'impact de la data dans la maintenance prédictive

The impact of data in predictive maintenance Data plays a central role in the development and implementation of predictive maintenance. By exploiting real-time data, companies can anticipate potential failures, plan maintenance more efficiently, and reduce the costs associated with unplanned interruptions.

Some of the major impacts of data in the context of predictive maintenance include, but are not limited to, the following :

Early detection of anomalies

Predictive maintenance uses sensors and IoT devices to monitor the condition of equipment in real time. The data collected enables anomalies and variations in asset operation to be detected well before a failure occurs. This provides the opportunity to intervene before the problem becomes critical.

Predictive analysis using machine learning

Machine learning algorithms analyse historical and real-time data to identify patterns in asset behaviour. These models can be used to predict likely failures, estimate the time to failure, and optimise maintenance schedules.

Reducing unplanned downtime

By identifying potential problems before they lead to major failures, predictive maintenance helps to minimise unplanned downtime. This is particularly important in industries where business continuity is crucial.

Optimizing maintenance activities

Data can be used to define maintenance plans that are more precise and adapted to the actual condition of equipment. Rather than following a fixed schedule, maintenance work can be planned according to actual needs, optimising the use of resources.

Proactive spare parts management

By using data on the frequency of failures and breakdowns, organisations can optimise spare parts management. This avoids holding excessive stocks of unnecessary parts while ensuring that the necessary parts are available when they are needed.

Improving safety

Early detection of anomalies also helps to improve safety by quickly identifying and resolving problems that could pose a risk to the safety of workers or installations.

Reducing maintenance costs

By proactively planning maintenance interventions and avoiding unplanned breakdowns, companies can reduce maintenance-related costs, including emergency repair costs and lost production.

Adaptation to actual operating conditions

Real-time data allows maintenance plans to be adjusted according to actual operating conditions, rather than relying on assumptions based on fixed schedules. This ensures more accurate and efficient maintenance.

In conclusion, the impact of data in predictive maintenance is significant, bringing significant improvements in terms of operational efficiency, maintenance costs, and equipment availability.

FAQ - ELAZUR Answers your questions

Choose EAM software by assessing your needs, comparing features, checking usability, and asking other similar companies for recommendations.

The benefits include extended asset life, reduced maintenance costs, better planning, and more efficient management.

EAM solutions enable proactive maintenance planning, efficient spare parts management and extended asset life thanks to preventive maintenance. This reduces long-term maintenance costs.

Do you have a request?

For all inquiries, Contact us Elazur answers your questions.