Implementing A Predictive Maintenance Strategy Thanks To IoT

Predictive maintenance is the most effective industrial maintenance strategy to optimize machine maintenance interventions and improve plant productivity. Its emergence and development are the result of technological progress in recent years, and in particular the spread of the IoT, the Internet of Things.

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Thanks to this, combined with an adapted CMMS, maintenance becomes more precise and easier at the same time. All industrial companies, whatever their size, can now implement a predictive maintenance strategy thanks to latest maintenance management solutions.

Why move to predictive maintenance?

Predictive maintenance, is the most elaborate maintenance strategy. It directly results from the evolution of practices and technological advances, that have enabled the emergence of new tools.

Corrective maintenance

For a long time, corrective maintenance was the only strategy used to maintain industrial machinery and equipment, and it remains quite widespread today. Also called curative maintenance, this strategy consists of intervening in the event of a breakdown or malfunction. It is, of course, the simplest strategy to apply, but also and above all the one that involves the most frequent, longest and most random production stoppages.

CMMS and preventive maintenance

With the emergence of the first CMMS software, large companies began to implement preventive maintenance strategies. These seek to anticipate breakdowns by instituting maintenance operations at regular intervals, within the framework of systematic preventive maintenance, or by monitoring the condition of machines through regular checks, in the case of conditional preventive maintenance.

While this type of maintenance makes it possible to reduce the frequency and duration of production stoppages and increase productivity, it is based on the probability of breakdowns and malfunctions, and not on precise knowledge of a machine's operating status, which limits its ability to anticipate the necessary interventions.

iot predictive maintenance cmms

Predictive maintenance

Predictive maintenance, is a kind of improved preventive maintenance: it plans interventions by monitoring and analyzing the operation of each piece of equipment. These interventions are then carried out only when they are necessary, but before the failure occurs. This strategy optimizes maintenance by avoiding both unnecessary interventions and unplanned production stoppages, thus improving plant productivity.

However, the implementation of predictive maintenance is only possible by using the appropriate technological tools: IoT to collect data, and CMMS 4.0 to analyze it.

iot predictive maintenance cmms

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How CMMS and IoT make predictive maintenance possible

Next-gen CMMS and IoT are the two technological innovations that have enabled the emergence of predictive maintenance strategies.

Internet of Things and industrial maintenance

IoT (short for Internet of Things) is a technology that allows objects to be connected to the Internet, and thus to other digital tools. It is increasingly used both in the design of everyday objects and in the development of innovative industrial tools.

In the field of industrial maintenance, the IoT takes the form of sensors placed on machines that collect various data on their operation. These data can concern temperature or pressure, consumption, rotation speed. The sensors can also detect abnormal vibrations.

Thanks to these, machine anomalies, but also signs of ongoing deterioration can be identified and analyzed, using CMMS software adapted to the IoT.

iot predictive maintenance cmms

IoT and CMMS 4.0

To use the IoT in maintenance, it is necessary to have a CMMS capable of analyzing the collected data and exploiting them within the framework of a predictive maintenance strategy. This is the case with latest generation of maintenance management solutions such as Mobility Work, which have the technological features and functionalities required to use the IoT in maintenance.

This platform uses Big Data, an indispensable tool for analyzing the data collected by the sensors located on the machines. Thanks to this tool and to Artificial Intelligence, this CMMS predicts future equipment malfunctions with great precision. In addition, it operates in SaaS mode and benefits from frequent updates.

The benefits of predictive maintenance

In concrete terms, the adoption of a predictive maintenance strategy through the joint use of IoT and 4.0 CMMS offers many advantages. The first of them is a significant machine downtime reduction, in frequency and duration. These stoppages, which are better anticipated, can also be carried out at times when they least disrupt the smooth running of the plant.

In addition, spare parts inventory management is optimized: unnecessary and costly storage of superfluous spare parts is avoided as well as the risk of running out of parts needed for a maintenance intervention. The organization of team planning is also simpler, thanks to an accurate forecast of intervention needs.

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Who can implement a predictive maintenance strategy?

As we have seen, the IoT and 4.0 CMMS are the two essential tools for adopting a predictive maintenance strategy. Contrary to some preconceived ideas, these technologies are accessible to almost all companies. The sensors used in the IoT, for example, are becoming less and less expensive and can be placed only on the most strategic machines. As for 4.0 CMMS, a solution such as Mobility Work is more economical than older software and works with a subscription system that avoids the heavy and risky investments that other software entailed.

Implementing a predictive maintenance strategy involves using IoT and a next-gen CMMS, two tools that are now available to a large number of companies. Far from being superfluous, this approach is already leading to significant productivity gains. What's more, companies that adopt it are in a position to quickly integrate the permanent technological improvements from which these solutions benefit, and thus maintain a high level of productivity.

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