Augustin Celier
The two phases of predictive elevator maintenance
The warm-up: enhanced breakdown response
Once you have gone through Phase 0 — gathering field data — and plugged in your IoT device, you can start deploying smart failure response. We have constructed Phase 1 around three core features:
- Breakdown detection: anticipate customers’ callbacks by detecting whether the elevator is up or down in real-time.
- Callback qualification: is the callback a real breakdown or a less urgent matter?
- Technician guidance: depending on the elevator status, the visual checks by the technician, and the error codes provided by the controller, what are the right tasks to solve the root cause of the failure?
Building this first set of features will already yield significant impact:
- Detection builds perceived quality: customers crave instant notifications of failures instead of having to call an emergency number.
- Callback qualification saves field costs: the ability to distinguish real urgencies from problems that can be solved over a more extended timeframe allows transformation of unplanned hours into cheaper, planned ones.
- Guidance saves field costs and increases technical quality: precise elevator data, in addition to technician contextual help, raises the first-time fix rate, reduces the field hours needed, and increases satisfaction.
The real stuff: breakdown reduction
For Phase 2, we’ll describe how, at uptime, we are quickly building breakdown-reduction features through predictive maintenance. The process described below is part of our filed patents on the topic and can be summarized in three parts:
- Identify key preventive tasks.
- Compute their correct realization timing.
- Deploy them through a dynamic visit model.
Identify key preventive tasks
To define the correct tasks that avoid breakdowns months in advance, the first step is an in-depth analysis of the failures:
- Failure profile by component and frequency.
- Understanding of the root causes of these failures and their associated curative actions.
- Determining the correct preventive measures to entirely avoid the root cause.
This creates a dynamic list of preventive tasks per component associated with a failure profile, which can already be achieved with data-driven operations and technical expertise.
Identify when to perform these high-value actions
Now we need the Formula 1 computer: the IoT data and the AI to generate timely recommendations. The output we need is the correct timing for preventive tasks:
- Early enough to avoid a breakdown.
- Late enough to minimize field interventions and costs.
The data we combine to generate the most tailored timing includes
- field checks: initial wear check, installation date, and regular wear checks;
- IoT data: traffic statistics and component usage; and
- context: breakdown patterns of the specific component and the specific elevator.
Implement a dynamic visit model
The last step looks like the easiest: push these tasks to the high-value visit checklist of our technicians. Each maintenance visit is different to the previous one and tailored to each elevator — in other words, condition-based maintenance.
However, to achieve successful results, user-friendly guidance on how to act is required. Indeed, if the implementation is not standardized, the performance of the predictive maintenance campaigns will vary greatly depending on the technician profiles.
Cultural change is needed to both empower the technician and, at the same time, reduce the skillset barrier for value-added tasks.
In this new model, technicians are not alone in their cars anymore, running from visit to visit (or, in a high breakdown–rate context, from reboot to reboot), with their competences largely under-utilized. They participate in a broad program of quality enhancement that requires from them discipline and precise reporting (data-driven operations) and strong involvement in reducing the root causes of breakdowns (dynamic visits).
Incumbents cannot solve predictive maintenance alone
Historical actors must rely on technology partners
Elevator OEMs or SMEs have a classic engineering culture, not a software innovation culture. To reconstruct the maintenance model, one needs
- disruptive integrated technology (hardware, software, and AI),
- retrofitting and universal innovation, and
- widespread cultural changes in the maintenance organization.
The first two requisites can only happen with partners that have the right disruptive, focused, and fast innovation culture — mostly from the startup world. As described by Christensen in 1997 in The Innovator’s Dilemma, almost no global company can disrupt itself. The recent history of the elevator industry, with initiatives that are add-ons to current processes, focus on the OEM’s make only, or do not yield significant business outcomes, confirms it.
Technology partners need a laboratory portfolio
Why do innovation programs by the OEMs — in partnership with tech companies like IBM or Microsoft — not seem to disrupt the industry? Why have independent material suppliers from the elevator industry not yet commercialized results-oriented IoT?
As demonstrated, cold IoT data doesn’t solve problems. Field data from technicians is needed, together with a reconstructed service delivery with dynamic visits. It is impossible to create a compelling predictive maintenance product for elevators without operating a maintenance portfolio that serves as an agile test environment.
We understood this necessity early-on at uptime, and today we might be the only elevator tech startup with a test portfolio.
Digitalize or commoditize. What’s your call?
How will the market evolve? We believe there are three possible paths.
Scenario 1: full commoditization
In this scenario, the OEMs won’t be able to reverse the trend; their service market share will continue to slide over to local independent service providers. The market will become fragmented, and its total value will stabilize or even decrease.
A standalone player or two will emerge above the others, thanks to technology. The OEMs will focus on building qualitative equipment and increasing their manufacturing margins, but they will lose greatly in market capitalization.
Scenario 2: OEMs become tech-enabled and service-driven
One or multiple OEMs will achieve a turnaround with the appropriate partners and rebuild their service operations around three changes:
- Retrofit-oriented, universal innovation.
- Real predictive maintenance in reconstructed service operations.
- Proper technical quality and perceived quality.
Those OEMs will be renewed with organic portfolio growth and take significant market share from the others and from independent providers. There will be fewer actors, and the market’s value will expand.
The winning OEMs — now technology-enabled rather than technician-driven — will be seen as technology companies relying on a valuable base of long-term contracts, and their market capitalizations will rocket.
Scenario 3: independents dominate with the right toolset
SMEs will onboard new technology from the right players, such as uptime, and turbo-charge their growth. They will digitalize faster than the OEMs and focus efficiently on quality of service based on predictive maintenance.
The transfer of contracts from the OEMs will accelerate, and new service actors will be frequently created in all geographies, building on top of technology providers. There will also be some regional build-ups helped by private equity.
As in the first scenario, the OEMs will focus on product development, increase new equipment margins, and progressively leave the service business. Overall, there will be more players, and the market’s value will stabilize or even grow.
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Source: medium.com
