Efficient Elevator Repairs Using Machine Learning

Business Solution Series

The Business Solutions Series is a compilation of solutions to various business challenges that I have encountered throughout my professional journey.

Context 

We were working with a company that sells and then services elevators for buildings all across the world.

Problem

Elevator technicians are expensive to hire and their time is very valuable. When an elevator breaks down and the technician is called, oftentimes they won’t know what is really wrong with the elevator until they are onsite. Also, more often than not, they won’t have with them all the parts they need to fix it (as it is impossible for them to preemptively bring all possible spare parts with them on all of their visits). Hence, they will have to arrange to come back at a later time with the right parts to fix it (and do it soon as elevators need to be fixed soon per the terms of service).

Objective

To reduce elevator servicing costs and decrease downtime of broken elevators.

Solution

The company we were working with had implemented a system on many of their elevators that would send all elevator system errors to their cloud service. Initially, they thought these reports were noisy and useless as elevators produce dozens of error codes all the time, and most of the time, nothing is really wrong with the elevator. 

However, they also had technician reports of what exactly was wrong with an elevator every time they had to go fix one. 

By using the technician reports together with the error system logs, we were able to identify sequences of errors (sequences of length 2 - 5) that were significantly correlated with instances where specific problems happened. We then picked the top 200 of these sequences and created a training set with the technician reported problems together with the counts of times each of those pre-selected 200 sequences were in the logs for a few hours before the elevator was reported as broken. We then trained a multi class classification model (using an AutoML managed service). Lastly, we made changes to the servicing process for the technicians to bring spare parts for the top 10 most likely failures - based on the model predictions - every time they went to check on a broken elevator.

Impact

We were able to substantially reduce the number of instances where the technicians -on their first visit - didn’t have the correct parts to fix the elevator with them. Hence, reduce servicing costs overall. Note that for this business solution to produce substantial savings, the multi class classification model didn’t really need to be very accurate on its top prediction, it only needed to be very good at making sure that any of the top 10 predictions was the correct one most of the times.