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IAC Partners: Predictive Maintenance - Where to Begin?

Predictive maintenance systems are the result of major innovations in the fields of sensors and big data. These systems are designed to anticipate failures before they occur.

Many industrial companies already benefit from major savings on maintenance costs, but the most advanced companies rely on predictive maintenance systems to develop differentiated business models, including “pay-per-use” and “never fail service.”

However, it must be noted that while the general principles of successful predictive maintenance systems are now widely available, most players in the sector struggle to efficiently and coordinately implement these systems.

So how should a company go about implementing a predictive maintenance system?


We found that three common mistakes must be avoided:

  • Focusing primarily on the technology (which sensors? which algorithms?)
  • Starting without support in a self-learning, trial-anderror approach (methodological, technological and organizational)
  • Underestimating the need for organizational transformation and change management

With an ambitious transformation approach, aligned with the company's strategic objectives, manufacturers can capture all the benefits of predictive maintenance.

This type of approach will be structured around four steps:

  • The identification of economic savings and business opportunities
  • The implementation of technical solutions for data collection and analysis
  • The integration of a new digital ecosystem
  • Transformations at process and organizational levels

Before defining the deployment strategy and the resources that would be required, the company must have a clear idea of the benefits that company will obtain from predictive maintenance in the short, medium and long term.

This step is approached from two complementary angles: the reduction of overall maintenance costs (savings) and the identification of new business opportunities (revenues).

While the exact cost impact differs from one sector and organization to another, our studies show that the impact of predictive maintenance on basic operating metrics is generally very significant:

  • A reduction in the frequency of breakdowns of up to 70%
  • A reduction in overall maintenance costs of up to 30% compared to preventive maintenance
  • A reduction of unplanned downtime by up to 50%

Predictive maintenance thus makes it possible to achieve new levels of operational efficiency by relying on the development of proprietary technologies and predictive algorithms for the analysis of topological data. The second benefit of predictive maintenance is the ability to generate new business opportunities through the development of new, intelligent business models.

The development of new business models is based on a transition from the sale of traditional products to the sale of services. This could be, for example, new value propositions based on operating timeframes (e.g.; number of hours/ months...) or the guarantee of a certain level of product availability ("never fail service").


A business case: Michelin Tire Care

In an ultra-competitive market where the product alone is no longer a source of value, Michelin has chosen to sell a turnkey solution to its key accounts (more than 100 vehicles). Their predictive maintenance solution supports a new service-based business model. The earnings? For users, a forecast of the actions to be carried out on their entire fleet and a better use of the equipment (e.g.; tires are used until the end of their life cycle). For Michelin, in addition to direct access to its end customers, this predictive maintenance offer allows the company to plan interventions at the customer's site as accurately as possible.

These benefits apply to all types of organizations, including specialized product manufacturers and transportation operators, as well as asset management industries.

For example, the low-cost airline EasyJet has implemented a predictive maintenance strategy for its entire fleet of more than 300 aircraft, following successful trial projects.

Thanks to the support of Airbus and its Skywise platform, 31 adverse events were successfully anticipated before they occurred last year. In another industry, Nestlé has updated its entire fleet of professional coffee machines with the addition of sensors for predictive maintenance services, thus optimizing use by technicians.

Finally, the compressor company Kaeser has implemented an business model based on the sale of air volume rather than machines, ensuring an optimal service rate through predictive maintenance.

Once these strategic objectives have been determined, what technical solutions should be adopted?



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