Digital Transformation and Predictive Maintenance at the Otis Elevator Co. (CXOTalk #767)

Digital Transformation and Predictive Maintenance at the Otis Elevator Co. (CXOTalk #767)

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We have our own roughly 2.1 million  elevators in our portfolio and one-third   of our elevators are connected with the help  of these edge devices which can communicate   to the controllers and perform the data  extraction and share that through cloud.  Today, we're talking about IoT, digital  transformation in relation to elevators. That's  

Ezhil Nanjappan, Chief Technology Officer of Otis. Otis is the world's leading elevator/escalator   manufacturing, installation, and service company.  We maintain more than 2.1 million elevators   worldwide and we move 2 billion people today.  That means we safely move the equivalent of the   entire world's population every four days. Give us some insight into the kinds of  

technologies that are embedded  in elevators and escalators.  It's a 170-year-old company. There have  been a ton of evolutions along the way.  If you think back 40 years, we launched  the first remote elevator monitoring   coming back where we have the electromechanical  equipment, where we have the controllers, drives,   and we are able to communicate  that and get that information and   monitor that remotely and diagnose certain  service activities but not being onsite.   As technology evolved, we are able to take the  data, analyze the data, and able to predict   insights, and then decide what are the areas  of tasks we need to perform in the job site.  As we progress, the technology in the  elevators evolved over a period of time,   so we have elevators – which you've probably  heard about – that's run traditionally with robe.   We changed that into belts, and we were the  number one company to be belts. It's Gen2,  

which we launched in 2000. Then also, we evolved from   the technology side in the electromechanical  equipment, and we make it more into where we   are able to remotely access and perform most of  the activities. That's the evolution we are going   through from an elevator technology side. Elevators are electromechanical devices  

and you are enabling them in order to share  data, collect data. So, tell us about that.  From the electromechanical equipment,  we need to get the data from different   places. One from the controllers, two from the  drivers, and three from the door performance.  We put in an edge computing device. We are able  to aggregate the data from the controllers, drive   logs, and the door performance. Along with that,  we added sensors to capture the door vibrations,  

the door noise, and process the data in a central  cloud environment, and take insights associated   to that to share with our customers as well  as toward internal personas like our call   centers as well as our field technicians. It's very interesting to look at elevators   as essentially being edge computing  devices, as you were just describing.  That's right. Tell us about the kinds of   data that you collect. You mentioned a few things,  but dive in a little bit more to the data aspect.  Let's start with the controllers.  In controllers, we capture  

certain data types which explains about the state  of the elevator and also in which landing floor   right now the elevator is, whether it is in a door  open or a door closed state. Also, if we take the   sensor sitting on the top of the door elevators,  we are able to capture the noise and vibrations.  Along with that, the controllers  maintain a certain type of logs.   We call that drive logs, event logs. We aggregate the data in the edge device   and then we share that data in a certain frequency  to the cloud environment. Then we store that in   our data lake, process the data, and then make  that available to our downstream applications. 

These are IoT devices then, so the elevators  are really part of the Internet of Things. Is   that a correct way to describe it? We call it elevator as a service,   but basically we connect our edge devices to our  controllers. Then there is an edge device which   transmits the data from the elevator to the cloud. From a business standpoint, what's the purpose of   doing this? You mentioned transformation as  one of the foundation elements of your work.  One, to drive our service growth. As  I mentioned earlier, we have roughly   2.1 million units in our portfolio, so  we want to grow our service portfolio. 

Two is to increase our pricing. Three being the customer loyalty   while making Otis more competitive  against our independent service provider.  Just to your point, at the end of 2021,  about one-third of our global units were   connected and we plan to accelerate  the deployment over a period of time.  How does the data make that leap  to supporting the business goals?  Now I'm going to drive into the  downstream applications. As we walk  

through the scenario from the controller or the  electromechanical elements with the edge devices,   we are able to transfer the data to cloud. Now let's talk about the data there. As I   mentioned earlier, we bring all the data  and store that as part of our data lake.   The data lake builds the foundation for actionable  outputs for real-time performance information,   proactive communication, as  well as predictive insights.  Let's take a few personas. One, our external  customers. What they care about is what's the   state of their elevator, whether it's healthy or  not, whether the performance of the elevator is as   expected, and what role the previous maintenance  tasks or activities performed. Was it on time?   That's from a customer point of view. Now, if you expand the customer to like  

a skyscraper or a university where you have a  campus where instead of seeing each and every   elevator's health, they can use our customer  portal application. They can see the entire   health of the campus, and then they are able  to double-click and drill down to a specific   elevator in the specific building. That's  our external customer, customer persona.  Now let's take the second persona, which is our  internal, which is call centers. We call this   OTISLINE (the users internally)  where they receive the call   from a customer when there is an issue with the  elevator or performance issues with the elevator. 

With this information, what we bring to cloud  with the edge devices, our customers and our   OTISLINE users are able to see, real-time, what is  the state of the elevator. For example, you were a   customer for a building, and say it's not working.  If you're on a call with our OTISLINE users,   they can literally see in which floor the elevator  was two minutes prior or five minutes prior.  

Also, they are able to see what kind of  error types generated from the elevator.  This will enable them to perform two operations.  One, they can troubleshoot by themselves with   certain conditions and safety rules. Two, they  can pass this information to our remote experts   who are trained enough and skilled enough where  they can remotely access the elevator and perform   certain operations under safety conditions. Then if you're unable to resolve the problem,   that brings the fourth persona, which is our  field technicians where they dispatch to our   field technicians. But at the same time, using the  technology, we built applications and apps for our  

field technicians. They can see from their phone  (for their buildings which are associated to that)   what is the health of the elevator,  what is the issue in the elevator.  Based on that, they could prepare if there is a  parts replacement required or if there are certain   steps what they need to perform. Then the app will  guide through the steps to address the issues.  All these downstream applications are supporting  our key four personas: external customers,   OTISLINE (or call center) users, remote  experts, and our field technicians.  How do you enable all of these elevators, many of  which have been in service for decades probably?  We have around roughly 2.1 million elevators in  our portfolio, so we have taken certain processes  

and steps by regions, by countries, specific.  One-third of our elevators are connected with   the help of these edge devices which can  communicate to the controllers and perform   the data extraction and share that to cloud. The roadmap of what we have is based on the   controller types and based on the deployment  process. We will try to extend the portfolio   of connected units over a period of time. What does this all do to the relationship   that you have with your customers,  enabling this kind of data?  The customers could be a building owner or a  builder who is building skyscrapers, or there   are residential elevators as having residential  owners, or the customer types can be in the   airport authority or subway stations – X, Y, Z. If you take this, and if you put in the customer   segmentations, there are customers at high-rise  buildings. They care about information like  

either available real-time (either using  their apps or the applications), but also   they care about integrating the data into their  building management system. We have solutions   which allow them to extend our cloud-based API to  integrate with the building management systems.  If you take the other set of customer segmentation  – as I said, the residential owners – there might   be tenants living there or it could be an  office building where they care about the   performance of the elevator where we want to  show, real-time, the health and status to the   building owner or the building manager. If you take another set of customers,   like a university campus, schools, the campus  owner should have visibility on the state of the   elevators. That's where we enable the solution  using our customer portal solution where they  

can see the campus view and then they can go  to a specific building to see the health of   the elevator, or they can go to the specific  unit or the elevator to see the health data.  That's how we are sharing the  information to our customers.  You mentioned earlier predictive  maintenance on the basis of that   data. Can you tell us about that? Two areas where we focus on it.  Well, probably, I'm sure we all did it. We  tried to keep the door open for a certain   period of time to move a piece of furniture.  It's difficult to predict this kind of scenario   over here so, over a period of time, the  performance of the doors may get degraded. 

If you are able to detect that data using sensors  and bring the data to the cloud environment, which   is our data lake, where we are able to process the  historical data and the real-time data coming from   the elevator, then we are able to perform certain  predictive insights. Like, for example, if a   technician has a scheduled visit to go and perform  door maintenance. If the health of the door in a   particular landing floor or, if it's four floors,  all the floors look good, then there is an   opportunity to optimize that maintenance task. Whereas if a specific door (based on the   predictive maintenance) you're able to see an  issue there, then that can be an additional   task for our field technicians to go and perform  actions and take care of it. That's how we are   using our data using predictive maintenance. Subscribe to our newsletter. Hit the subscribe   button at the top of our website. Be sure to  subscribe to our YouTube channel because we  

can notify you of upcoming live shows. This presence of the technology and   then the data that results enables you to  provide better customer service and really,   in some ways, change the business model, the  core relationship between Otis and its customers.  I'd like to share an example here of one  fine morning in one of the hospitals. The   elevator to the surgery room was  shut down or it was not working.  Using this technology, what I outlined earlier,  our technician was able to see that information.   Then, in order to take proactive action there,  he decided and he went to the job site. He was  

in the front of the building lobby talking to  the front lobby person saying that the elevator   going to the surgery theater room is not working. It was a surprise for them to know, first of all,   how we are able to find out remotely and, two,  he's able to show (using the app) that this is   the problem happening in that elevator, so we  need to go and take care of it. It happens to   be he went there able to solve the problem in a  timely manner. This brings customer satisfaction,  

as well as the stickiness to the customer, and  transparency to the customers like what we are   doing in order to fix or maintain the elevators. The transformation inside   Otis also must be significant because you have  lots of repair technicians and they're used to   doing things differently, right? They show up  and they physically look at the electromechanical   device. Now you're asking them to relate to  this piece of equipment in a very different way.  One of the areas where you will see  the most transformation for us is   in our field professionals. We developed a  group of business applications for our field  

technicians to perform certain operations  in an optimum manner in the job site.  I would like to highlight one of the  applications. It's called the Tune App.   Basically, this app leverages the built-in sensors  and detect the level of noise and vibration.  

With that data, we leverage artificial  intelligence and machine learning to recommend   specific actions to our field technicians. For example, if you want to do a floor-to-floor   test or a door cycle or a brake test, they're  able to perform all of those things with the   app on their devices. That's a huge transformation  we've been through with our service technicians.  It sounds like Otis had to go through, of  course, many different transformations over time.   Where do you see this technology going? You may have seen or heard about the service   robots helping with the security or making full  deliverables. We are now partnering with the robot   companies, and we are communicating directly  with elevators, the electromechanical equipment,   and then able to perform access controls  autonomously. That's a big game-changer for us.  You'll be able to see that in elevators.  And one fine day, maybe elevators holding  

a door for you to go through. And the robots are  helping the elevators to hold the doors as well.  It'll be deployed in hospitals and  universities in a lot more areas.  I'm assuming that you must have or your team  must include software designers, developers,   of course, but also the folks who are developing  the electromechanical aspects because, after all,   elevators (despite being enabled with  all this technology), they're still   mechanical devices at the end of the day. Yes, there are. We have our engineering team   members. We partner together. We work very closely  in the technology transformation where we extend,  

enable, or transform the electromechanical  equipment along with the software development. It   helps to blend together to achieve the end-to-end  connectivity as well as transform our business.  We have an interesting question from Twitter  from Arsalan Khan. Arsalan is a regular   listener. He always asks the best questions.  He asks this. He says, "Has your digital   transformation effort led to new revenue streams?" Here's another interesting aspect. "How did you  

convince the executives and the frontline folks  that this massive change is good for everyone?"  It's a journey for us. As I said, the service  business is strong for us and we have 2.1   million elevators in our portfolio. The transformation journey is,   as I said earlier, we want to be transparent to  the customers performing insights. When we put   together an approach and strategy, we look into  the list of areas and then the customer feedback.  The key thing customers are looking  for is uptime of their elevators,   minimize their planned shutdown time, so we have  taken that into consideration and then see the   current state and then the current maintenance  tasks of what we have. We combine that together,   and then how we can enable technology end-to-end  in order to achieve the customer requirements.  

Also, in order to be transparent to our  customers as well. That's how we evolve.  Then while we are going through this, from  a technology side, we want to make sure we   scaled up the architecture in a manner which  can support this from an end-to-end point   of view. Right from an edge device or edge  computing, can get the required information   or the key information to support the need what  customers are looking for as well as the other   personas I outlined. Then bring the data to  cloud to support the downstream applications.  When we create the strategy and the approach, we  took this holistic view from a business point of   view, customer expectations, as well as from the  internal productivity and efficiency perspective.   Then we created the structure how we can move  forward. That's the approach we have taken and   we're able to move forward with this. I think about that kind of change,  

and it's such a massive technology shift because  I'm just imaging here's Otis building these large   machines, very heavy machines, and now you're  not only in the machine business but you're in   absolutely the software business (building APIs  and so forth), as well as the data business.   The shift is just completely massive. As we are able to bring the data into   our data lake, we can make this data  to perform insights. At the same time,   we can make the data available to our customers.  That's where we evolve the journey of cloud-based  

APIs. Then that enables a couple of things. One, as I said, the high-end customer segment   maybe looking for the information to integration  into that building management system. So,   we are able to have data and the API as a  service available which will enable a new   line of business for us. That's one area. Two, as I mentioned about the robots where   we are working with the partners.  We can enable and extend the API   services (what we have) to take it forward. At the same time, with the insights and predictive   maintenance, we are able to increase our  productivity because before going to the job site,   me as a technician, I can see what is  happening in the elevator. I can decide,  

do I need a part to go and fix the problem  or can I fix it with a list of steps what I   need to perform from a parameter side. We focused on (exactly what I said)   the topline business growth. At the same time,  how we can increase our internal productivity.  There's real value to not just the customer but  also from an efficiency standpoint to the service,   to the front-line service technicians themselves,  which then encourages them to make that adoption,   to make that change. Absolutely, yes.  It seems like that's also a very important  part of this. I know when I talk with other   business leaders, the culture change  aspect of digital transformation always   seems to be the most challenging. As you know, we went through this   journey five years before we started with our  service transformation journey with our field   technicians. The approach we have taken  there is it's not that we're trying to  

transform the entire end-to-end overnight. We did a lot of ride-a-longs with our   technicians. We were able to identify the areas  of opportunities where we can improve upon. Then   we wanted to build on an incremental phase. In order to do that, from a technology side,   we needed to make sure the architecture  can scale up and support that.  That's the parallel activity with my team. We're  able to implement that along with the business   team where we took key areas how we can implement. Then the development cycle we wanted to optimize  

as well, so we went through a process where it's  a standard process now, pretty much. But you have   a workshop for a couple of days, standardize  the end-to-end steps what we need to perform   on the job site, and then we build apps in a 12-  to 14-week period of time and make it available   for our champions team who can deploy it in the  job site and get the input and feedback. Then we   increment by adding additional enhancements. That's the journey we went through. Then we   are able to see the benefits over there from  a productivity and the opportunities. Then  

by having the connected elevators, the Otis One  program, we are able to support other personas,   including our external customers. Can you tell us about the composition   of your team? I'm so interested in this because,  again, when talking with somebody who works at a   software company, it's pretty clear what the team  will need to consist of, at least generally. But   Otis is so unusual in having both this very  strong hardware as well as the software aspect.  From the software side, we are transforming into  a standard of dev sec ops. When we started the  

transformation journey, we started with dev ops  as a standard practice. Then there is a training   and change management, which we went through  internally, so we have business partners acting   as product managers. Then we have a technical  team acting as technical product managers.  We combine and work together. We go through the  standard agile process over here. But I just want  

to highlight a couple of key points. Last year, we touched almost   15,000 story points from a development point  of view. The story point, as you know, a few   story points combined together enable the feature  sets from an agile development methodology. So,   we are able to accelerate to that volume by  establishing standard development methodology,   common tech stack, and leveraging the cloud-native  services in order to accelerate our development. 

Koustubh Bhattacharya says, "Elevators are  mostly public utilities. How do you really   measure the user experience of lifts using  data?" That's a really interesting question.  By putting the edge devices connecting to  our controllers, we are able to measure the   performance of the elevator. Having sensors,  we are able to measure the performance of the   door at the landing floors and respective floors. We do not capture any of the (from a privacy and   requirements point of view) people movement  and other things at this point of time,   but we do track the information coming out of  the drives, controls, and other things, and we   are able to make it available to our customers. Correct me if I'm wrong. It sounds like it is a  

function of measuring the elevator performance  metrics, things like are the doors closing   rapidly, things like that. Is that correct? Yes, and then if we take a particular customer   segment, probably you might have noticed we  started deploying a screen inside the elevator,   so the passengers, the riders, they can  see the content like any live news, music,   songs, and other things. Also, it displays the  position indicator of the elevator movement.  There are a lot of other initiatives we have in  our roadmap from enhancing the user experience   point of view beyond the aesthetics and the  screen inside the car or inside the elevators.   That's in our roadmap. We are going through that. This is from Matt Wood. Matt says he knows that   Rolls-Royce engines have a control room monitoring  the status of every jet engine globally from a   central control center in the UK. They know which  ones are at different stages of their flights,  

the maintenance stats, and so forth. He says, "In a sense, elevators, lifts,   are similar to jet engines, although not quite so  life critical. Do you see such functionality for   lifts, that kind of control room monitoring, the  ability to drill down to the individual elevator?"  We do have our OTISLINE, our call centers,  at the regional and the country level where   they are able to see the portfolio of units  deployed or installed in the particular country.   In addition to that, our field technicians  are assigned with a certain set of units,   and they can see that in their mobile  devices using the apps (what we have built).  From a control center point of view, at this  point of time, we are leveraging the OTISLINE   or the call center users where they can see the  visibility of the elevators in the particular   country but not like Rolls-Royce sitting in the  UK and see the entire thing. We are not there yet. 

Let's jump over to Twitter. Lisbeth  Shaw has a question. She says,   "There's scaling of the technology architecture.  How do you scale the human part of the equation?"  On the scaling of the technology  architecture, if you see it,   it's running as 100% cloud-native applications  in our data lake. We are able to scale up the   architecture to support millions of messages  coming from all these elevators in a day. 

From a human part to it, like you and me, we  are the riders or passengers of the elevator.   We have received, based on our interviews and  discussions with the customers, a lot of feedback   on the things we can build up on and enhance. Take an example if it's in a subway or a train   station. We know at 4:00 p.m. there are four  or five tracks, trains that are going to come   and stop there, which means are we able to put  in a logic where we can park all the elevators   in a place where they're able to get into the  elevators and reach their destination in the   shortest wait period of time. That's  the kind of things we are working on,  

putting in automatic logic, and then the machine  learning on top of it to minimize the wait time.  From a human point of view, based on the  density and the areas where we go to work on,   that in some areas, if you take smart cities,  there is traffic movement where we can capture.   If it's a public place like malls and other  things, we can get the data and the usage.  We know in the month of August the  usage has gone down. So, we can see,  

okay, let's park a few elevators because the  volume has gone down. Whereas, in the month of   November and December, the use is going to be  much higher around shopping and other things.  These are the kinds of things we are working  on and has been deployed in a few areas. We   are piloting and getting feedback. That's how  we're doing it from a human part, but there are   a lot of things we have in our pipeline as a  roadmap, which we'll continue on this journey. 

I suppose once you have this body of data and  you've created the technology that allows you   to analyze it, then your ability to do product  development is limited really by your imagination,   by what your customers need or tell you. Correct. Over a period of time,   we are able to aggregate and process  the data. It helps us to perform optimum   insights on the usage, the number of runs. We can sit here, and we can see in a building   what's the usage and number of runs. Then also, we  are able to see the usage over a period of time.  I just want to give you a few data points there.  As you know, during COVID time, the usage in  

the public place has gone down. We see that. Then, as you see towards the end of the pandemic,   we see the usage going up across  all areas. We see the growth rate   growing from 5%, 7%, 10%, so on and so on. This is from Arsalan Khan. He asks another really,   really good question here. He says, "How did  you invite or convince the business folks to be   collaborative when it comes to learning about  innovations inside and outside the company,   and then actually executing and  implementing those innovations?"  We co-partner, we work together, the  business. If you take the business,  

there are certain field team members,  marketing, and sales. We are all together   as one team along with DT and engineering. When we created the strategy, when we tried to   decide the list of functionalities and priorities  where we want to implement, we combined together   with the help of product management, and we  defined the priorities. We set the expectation by   implementing or enabling this future. This is the  ROI or the benefits we can see or the transparency   of data which we can share with the customers. We go through that process and then we put that  

(from an agile development methodology point  of view) into our program implement plan,   and we take that. We perform that on a monthly  basis, so that's how we work together as one team,   and we all know what functionalities  we are going to enable and when it is   going to be available to pilot in the field. You have a fairly formalized methodology or   approach for aligning technology  innovation with business goals.  That's right. Every functionality is tied with the  business priorities and business opportunities.   When we align it, when we discuss on a  particular feature or an enhancement,   we align end-to-end across business team members,  product management, on the technical team, those   who are working on executing the functionality. I have to ask you this. Does the close button  

actually do anything on an elevator? [Laughter] No, it does not. It does close the elevator,   but it is not— You know sometimes you   can see some locations where people – I mean it  takes like less than three seconds. But then,   obviously, sometimes people try to press  multiple times in order to close it.  But there are certain conditions we put in  place when to close it. That's due to certain   safety conditions and the leveling floor  in the particular landing floor as well.  Since you're willing to share with us  the deep secrets of elevators, how do you   make sure elevators don't fall? [Laughter] It's coming from Elisha Otis. One hundred  

and seventy years before was the first time he  demonstrated in the Bronx. It was on a rope so,   by cutting the rope, the elevator stopped. That's  where the brakes and pulleys were established.  As we developed from there, as we went into  the rope in the high-rise buildings, there are   a lot of controls and brakes put in place in our  system. Even if it travels at the speed of three  

meters per second to eight meters per second,  the brakes and controls are put in place. Then   that's how we are able to land on a specific  floor, whatever the speed of the elevator is.  Then we extended and expanded that into a lot  more, which you can see the landing. If you   go into a high-rise building, even if you go  up 80 floors or 90 floors, you won't see the   vibrations and noise. The way in which we put  the ropes and then the brakes associated to   that and the controls associated to it are in  such a manner that the riders and passengers   won't see any difference that they're moving six  meters per second or eight meters per second. 

You're constantly monitoring  that set of data as well.  Yes. Now we've started populating the  rope performance and other data. It is   in our roadmap to add that, so it'll bring  additional opportunities for us as well.  Okay. We have another question from LinkedIn.  This is again from Koustubh Bhattacharya. He   raises an interesting point. He says, in his  experience, the biggest problem with elevators  

is housekeeping, not cleaning them on time.  Maybe it smells because they're damp lifts.   He says he knows it's not an issue with  the elevator manufacturer, but it ruins   the overall elevator customer experience.  And so, to maintain the brand image of   Otis, do you look at those nontechnology  aspects and try to think how to make the   elevator a better place, a better environment? There is no visibility to get that information.   But now, with our sales supervisors, service  supervisors, and our field technicians,   they visit the job sites and customers in  a regular frequency, in a certain interval.  During that time, there are certain things which  we enable in an app format, which is to perform   some level of audit. That audit includes safety,  performance, cleanliness as well. With that, we   are able to collect that information and process  that information and see certain things are   responsible for us to take care, certain things  are responsible for our customers to take care. 

We share that information in full transparency  what we are going to do on the job site as well   as what our customers are going to do on the  job site. That has been deployed in progress   as well. But if it is a specific customer, I'll  get some more information, and then I will take a   look into it and see how we can help over there. We have a really excellent question on LinkedIn   from Mark Brewer. He is VP of Service Industries  at IFS. I'll say, Mark, we had your CEO as a guest   on CXOTalk. Just search for IFS on our website. Here's his question. He says, "Where are you on  

your predictive maintenance journey? Are you using  AI and machine learning today to accomplish this?"  With the data, what we are collecting from our  edge devices as well as with the sensors (what   we have on the door performance), we bring that,  and then we have AI and ML. Where we are able to   predict the door performance – I was answering  to the other question – we are able to see the   landing floor is in line with what we're looking  from the standard tasks point of view or are   there any tasks we need to perform based on the  landing floor performance or the door performance.  That kind of a prediction, we are making  it available to our field technicians.  

Then it triggers that as a task for  them. It will allow them to make sure   they include it as part of their next visit.  That's where we are using our predictions.  Also, in addition to that, the key thing is we  want to get the feedback because we need to have   a closed-loop process in order to make sure the  AI or ML model of what we built has been enhanced   and then extended. We get the feedback from our  technicians and insert that so that way we can   make our machine learning (and then the models  of what we developed) enhanced over a period of   time in order to be precise from a precision  and accuracy point of view as well. That's   the journey we are going through internally. We have another question from Twitter. This  

is again from Lisbeth Shaw who says, "How much  can the CTO control elevator system design given   the fact that you are—" she's making the  assumption "—integrating a large number of   components and systems from outside vendors?" We have our engineering team, and we work   very closely together as one team. There are  certain parts and other things, yes, that come   from external vendors. But then when we put it  together from a controller point of view (drives   and other things), that is coming from our team. From a DT or CTO perspective, we work closely with   our engineering team members, the data types, the  normalization of the data, what we need to bring,   make that available in the edge device  before we surface back to the cloud for   the downstream applications. We work together  as one team, so the level of involvement at   the controller level, the drives and other  things, are as one team. We work together. 

Any final thoughts on the future of elevators  and where the digital transformation of   Otis is headed? From a future perspective,   there are a few areas we touched upon. One about  the robust building management integrations and,   from a technology side, the areas where we  are working on. One, as we discussed earlier,   about the cloud API, and then also the data in the  cloud enables us to do the predictive maintenance.  In addition to that, the cloud transformation  is a key thing for us, which we are going   through in our journey. At the same  time, serverless architecture is the   other area where we are focusing on. In the future state, we could involve  

some advanced training methods of safety measures  using VR, AR, so that'll help us to optimize and   train from a safety point of view for our field  technicians and other things. These are all the   things we are evaluating from a technology side. There's a lot going on, and I'd say it's a very   interesting journey. I'm sure at some  time in the future when we discuss,   we'll share more about the amount of  transformation what we performed at the time. 

All right. Well, with that, it's been a  very fast conversation. A huge thank you   to Ezhil Nanjappan. He is the chief technology  officer of the Otis Elevator Company. Ezhil,   really, thank you. I'm very grateful for your  taking the time and sharing with us today. 

Thank you so much, Michael.  Thank you for having me here.  A huge thank you to everybody who watched,  especially to those folks who asked such excellent   questions. You guys really are such a smart,  intelligent, sophisticated audience. I love your   questions. Always keep those questions coming. Before you go, subscribe to our newsletter. Hit   the subscribe button at the top of our website. Be  sure to subscribe to our YouTube channel because   we can notify you of upcoming live shows where you  can again jump in and ask your questions. Thanks  

so much, everybody. I hope you have a great day  and check out We'll see you soon.

2022-11-22 21:46

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