Augustin Celier, co-founder, Uptime looks at how predictive technology is transforming an archaic, broken maintenance model

It’s no secret that consumer equipment manufacturers have a sneaky habit of deliberately limiting the lifespan of their devices to increase profit from new sales and repairs over the long term. A slightly lesser-known fact is that industrial equipment often follows the same trend. As end users become savvier, various initiatives exist to boost their protection, with the goal of ending this wholly unfair practice once and for all. However, the industry harbours yet another little secret, one that encourages an inefficient productivity hunt by service providers, drives up failure rates and reduces the overall quality of service that users receive.

Today, big ticket equipment manufacturers rely on breakdowns to invoice expensive repairs, the prices of which continue to grow. Consider, for instance, the fact that the overall cost of motor repair rose by four percent in the first half of 2019. Or that, despite almost $12 billion spent every year to maintain European elevators, and despite service visits up to once per month, they still experience an average of five failures a year.

Sticking with elevators, our most utilised mode of transportation, the industry’s maintenance model currently provides around 75 percent of its total profits – and keep in mind that this is a $90bn market worldwide. Today, the relationship between building managers and elevator service providers revolves around a model born in the first part of the previous century. This model was designed to service an installed base of elevators under fixed terms. In reality, this means clients, property owners or building managers receive a maintenance contract that enforces a set number of mandatory visits and includes emergency breakdown response, with additional repairs sold on top of the contract. They are therefore purchasing ‘means’ (the visits and breakdown response), and not ‘results’ (a guarantee that the elevator works) – and that is exactly where the problem lies. This established model results in unnecessary maintenance visits, increased downtime and frustrated building occupants.

Empowering building managers with better insights

The good news is that recent advances in artificial intelligence (AI), as well as developments in cloud and smart sensors, have led to a new era of predictive maintenance, one that uses a combination of these technologies to proactively mitigate breakdowns. However, due to piecemeal adoption across many industries, predictive maintenance has been ineffective at ending unfair maintenance practices. In the aerospace sector, for example, equipment manufacturers sell materials at cost and only grow turnover by applying a very large margin during maintenance operations. As a result, the implementation of a predictive maintenance technology would considerably reduce their profitability in this area.

However, when it comes to buildings, managers are beginning to recognise how predictive maintenance can allow them to better understand the inner workings of their own facilities through improved access to available data, and ultimately, reduced costs. With the rise of connected prop tech in general, building managers must place equal value on data transparency as well as end performance, as having access to real-time data can empower them to prevent failures and prevent breakdowns. Going back to elevators, predictive maintenance combines precise, real-time sensor data with field information to determine the optimal time to perform specific maintenance tasks, guide engineers and improve the quality of service while controlling costs. Used correctly, predictive maintenance data enables productivity gains and reduced labour costs for manufacturers. For end users, this can also translate to fewer replacements and reductions in downtime.

Reversing the trend

Though these benefits are proven and measurable, many building managers are still trapped in one-way relationships with manufacturers, with contracts based on an archaic way of doing things. But, the building industry cannot maintain this long-term.

Some may argue that the reality promised by predictive maintenance is not a future utopia but has already been proven by pioneers in the transport sector. For example, Skoda’s artificial intelligence-driven Sound Analyser application allows car owners to evaluate the noises emitted by their vehicle to obtain a diagnosis for maintenance. French railway company SNCF has also cut breakdowns by more than half in trainsets with remote diagnostics, and by nearly two-thirds on lines with predictive maintenance.

These examples highlight the tangible benefits of predictive maintenance technologies, while demonstrating the ability to satisfy the financial interests of both service providers and end users. The model is finally shifting from a laser-focused effort to sell lucrative maintenance contracts. Instead, end users increasingly demand performance-based contracts based on an agreed commitment to results. This may initially appear more expensive, but over time it will actually reduce costs, improve building quality and slash the number of breakdowns. In turn, this will lead to greater relationships and a good perception of quality among the building’s occupants.

Modernising the building experience

In time, this transformation will reduce the tension between stakeholders – relationships between building managers and service providers will be based on transparency of information and a financial commitment to results. When taken seriously, real predictive maintenance, aimed at creating value for the customer instead of perpetuating an outdated model, can and will disrupt the service delivery model, boosting the reliability of equipment and end user satisfaction worldwide.