Make the switch to maintenance 4.0 and reduce downtime for your equipment

Maintenance 4.0 in industry is not limited to factories of the future. It can be introduced and adapted to your context regardless of your level of digital maturity and types of maintenance (preventive, curative, predictive).

The new opportunities provided by the IoT and artificial intelligence have been integrated into the InUse platform and significantly improve availability rates for your industrial equipment and reduce unplanned downtime.

Anticipate equipment failures with predictive maintenance

While machines and supervision systems are able to detect critical errors and shutdowns, their predictive capacity remains limited. The InUse platform offers new options for identifying weak signals prior to shutdowns and predicting when they will occur, starting with the most critical cases.

Identify equipment behaviour deviations


  • Predictions based on multiple data sources: from automated systems, supplemented if necessary by edge sensors which provide additional computing power locally (e.g. vibration analysis).
  • Long-term detection of equipment behaviour deviations.
    This is made possible by the platform’s continuous data collection, which is unlike traditional supervision systems.
  • Modelling of behaviour deviations prior to breakdowns in the Studio: from simple cases of threshold exceedance to the most complex cases that are handled using advanced machine learning techniques

Critical production shutdowns avoided


  • Teams receive warnings before the shutdown occurs via automatic posts providing the associated diagnosis for the machine and the application’s recommendations. The warning is sent early enough to plan for the associated maintenance operation outside production periods.
  • Gain greater equipment availability and an increase in overall output thanks to reduced unplanned production shutdowns and the introduction of optimised maintenance plans.

Optimise daily preventive maintenance operations

Preventive maintenance operations based on scheduled recommendations cannot ensure that equipment is maintained in optimal operating condition. Our industrial IoT platform takes these operations to the next level by taking into account actual equipment wear while also digitalising the entire process.

Interventions specifically suited to the needs of your equipment


  • Optimised frequency of preventive maintenance operations by transitioning from scheduled maintenance operations to maintenance based on actual wear cycles (hour counters, number of operation cycles completed, etc.)
  • A fully digitalised process with maintenance reports including comments and remarks from technicians and even the client’s electronic signature. The teams’ expertise is therefore safeguarded and files are further enhanced.

Simplified procedures for operators and technicians


  • Maintenance operations are anticipated by the equipment and communicated to maintenance teams in advance via dedicated posts for scheduling purposes.
  • Maintenance teams are assisted by digitalised maintenance files listing the procedures to follow (tasks, instructions, etc.) and access to documentation directly in the application if needed.

Reduce machine downtime with corrective maintenance

The cost of a production shutdown is considerable in industry sectors with fast-paced operations. Responsiveness is therefore key in reducing restart times for broken down equipment. The InUse platform provides a practical solution that allows maintenance operators and technicians to resolve these incidents more quickly with greater independence.

Digitalised root cause analysis


  • Identification of the most problematic shutdowns allows you to focus on the most important ones. It identifies frequency, the duration of the downtime, and the complexity of the troubleshooting process.
  • Digitalised root cause analysis based on the expertise of the most experienced technicians. A specific model is created for each shutdown including the various root causes and associated resolution methods.
  • Creation of a digital breakdown reference frame made possible thanks to the digitalisation of collective knowledge. Human-machine cooperation is the key to a complete and intelligent application.

Instant and precise diagnosis with an associated resolution procedure


  • Instant diagnosis of the failure sent to the users concerned with additional information on the shutdown and most probable root cause
  • Operators and technicians are more independent on the ground thanks to assistance that confirms the diagnosis of the shutdown and the resolution method to follow for faster restart times.
  • Additional external assistance is available in the application itself (written interaction with experts, video assistance)