Elevator predictive maintenance: from words to reality

  • It revolves around an outdated means vs. ends model in which the customers purchase maintenance visits and breakdown response.
  • This model encourages an inefficient productivity hunt by service providers, driving up breakdown rates and reducing the quality of service.
  • These productivity gains then entail structural churn of the portfolio units and a decrease in maintenance prices — we call this process the commoditization of the market.
  • This commoditization is barely counter-balanced by the new elevators installed and converted to maintenance services, as too many old lifts churn to local players.
  • The COVID-19 crisis is further endangering the model, as new equipment installations have come to a temporary halt and regulation softening might accelerate.
  • while they excel at product innovation for new elevators, this necessary defensive move is not enough to reverse the trend, and
  • their predictive maintenance efforts seem to be stuck at the marketing stage. This is due to their manufacturing culture, with innovation rooted in their factories. They lack a retrofit innovation culture and a software culture.

What is quality?

  1. technical quality: performance of the service, e.g., the breakdown rate; and
  2. perceived quality: customer engagement in a satisfactory relationship with the service provider, e.g., communication with customers during a breakdown.

Technical quality

  1. Breakdowns: their frequency, usually a total per year.
  2. Uptime rate: the total time when the machine was working, as opposed to not working, in %.
  3. Callbacks: the customers’ complaints, which can relate to a breakdown or any other visible problem, such as a broken light or a broken button.
  4. Asset preservation: whether the elevator is well maintained and its organs frequently repaired to guarantee the longest possible lifetime vs. a costly overhaul.
  5. Compliance: whether the elevator is complying with all local regulations.

Perceived quality

  1. Ride quality: how comfortable is the elevator ride, from waiting time to lighting and noise?
  2. Information: how easily accessible, clearly understandable, and transparent is the information provided?
  3. Brand: how does the brand experience generate trust toward the service provider?

Doesn’t quality decrease margins?

High technology can reignite portfolio growth

  • help provide around-the-clock, precise, and transparent data for customers, increasing perceived quality and the brand, and
  • help reshape the maintenance delivery model and thus reduce breakdowns and other failures, increasing technical quality and decreasing costs.

Predictive maintenance starts with data

Could you picture car maintenance without mileage counters and knowledge of past interventions? Well, that is precisely how elevator maintenance operates today.

Half the necessary data doesn’t even exist

  • The how-to for solving failures, adjusting various components together, and avoiding breakdowns is in the heads of the service technicians. They only share this knowledge-base orally.
  • There is no tracking of the actions these technicians perform on-site. Until recently, most maintenance companies still lived by paper-and-pen reporting. Digital reporting today is not normalized: a text field saying “I fixed the failure” does not create usable data.
  • There is not even any tracking of the components: every elevator is an assembly of multiple manufacturers, and most of its elements change over the years. Nobody keeps track.

Elevator data exists but needs processing

The most obvious way of building IoT for elevators is thus not by adding sensors, but by obtaining and processing the existing data.

Elevator IoT: technical choices are vital for quick ROI

Data is in the controller

  • the traffic, such as the number of trips, door openings, and floor usage;
  • the functioning of the components, the engine starts and stops, and the cabin door cycles; and
  • the sensor data and the statuses computed by the controller board.
uptime’s universal controller-IoT card

Vibration analysis is a long shot

  • in the elevator industry, to benchmark the ride quality of a newly installed elevator, and
  • in the manufacturing sector, to help maintain rotating engines.

Don’t OEMs already have controller data?

  1. Data structure. Receiving data once a day about an elevator’s current status is very different from accumulating, in real-time, all the events from a controller to feed into an AI model.
  2. Data usage. IoT data, if not associated with relevant technician data and not targeted at reshaping maintenance methods, is useless. Data alone will not produce any ROI.
  3. Universality. OEMs have access to their controllers, but they maintain dozens of makes. This might be one of the reasons Otis says it installed only 300k REMs out of a 2M unit portfolio. It is hard to change field methods when those changes can’t apply to all serviced elevators.

Data-driven maintenance is Phase 0

Know your portfolio

Know your failures and field actions

  • In what exact state was the elevator when the technician arrived?
  • On which components did he act and how?
  • What was the status when the technician left?
  • Exactly what procedures — visual check, operation test, repair, lubrication, adjustment, replacement, wear-level check…
  • Exactly which components.


  • % callbacks that are not breakdowns,
  • uptime rate,
  • breakdown rate by material or component type,
  • breakdown rate after a specific field action,
  • first-time fix rate on a failure type, and
  • success rate of various failure responses.

The right product vision wins

Product is focus

Successful technology startups do not win by the depth of their resources, but by the precision of their focus.

Technology-driven quality kills costs all day long

  • If your first-time fix rate increases and your breakdown rate decreases, this means fewer field hours for tackling callbacks.
  • If your maintenance operations are data-driven and standardized, it becomes easy to lower the skillset barrier for value-added interventions.
  • If your perceived quality is high and the information provided on the service is abundant, it becomes easier to sell necessary additional repairs.
  • Once the right quality is reached, it becomes easy to raise the average contract price and, therefore, the margins.

Predictive maintenance is not an add-on. It is a reconstruction.

  • Mostly useless compliance visits. We call them “ghost visits.”
  • Breakdown response: receive a callback, get to the building, reboot the elevator, and leave.

Not only is this add-on vision hard to achieve, as pure IoT data is rarely sufficient to anticipate breakdowns, but it entirely misses the point. It generates additional unplanned field hours (the most expensive ones) instead of reducing them.

  • Rebuild the technician user experience to gather normalized field data and to standardize field actions.
  • Within local regulation boundaries, avoid unnecessary field hours with a differentiated (high-value or compliance-only) visit model.
  • Focus predictive maintenance efforts on early breakdown avoidance by applying the right preventive actions: transform expensive curative field hours into cheaper, predictable planned hours.

The two phases of predictive elevator maintenance

The warm-up: enhanced breakdown response

  1. Breakdown detection: anticipate customers’ callbacks by detecting whether the elevator is up or down in real-time.
  2. Callback qualification: is the callback a real breakdown or a less urgent matter?
  3. 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?
  • 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

  1. Identify key preventive tasks.
  2. Compute their correct realization timing.
  3. Deploy them through a dynamic visit model.

Identify key preventive tasks

  • 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.

Identify when to perform these high-value actions

  • Early enough to avoid a breakdown.
  • Late enough to minimize field interventions and costs.
  • 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

Incumbents cannot solve predictive maintenance alone

Historical actors must rely on technology partners

  • disruptive integrated technology (hardware, software, and AI),
  • retrofitting and universal innovation, and
  • widespread cultural changes in the maintenance organization.

Technology partners need a laboratory portfolio

Digitalize or commoditize. What’s your call?

Scenario 1: full commoditization

Scenario 2: OEMs become tech-enabled and service-driven

  • Retrofit-oriented, universal innovation.
  • Real predictive maintenance in reconstructed service operations.
  • Proper technical quality and perceived quality.

Scenario 3: independents dominate with the right toolset





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Augustin Celier

Augustin Celier


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