ServusConnect Blog The One Maintenance Metric that Matters- Service Cycle

Big Data is a hot topic in business right now. Because for the first time in history most organizations have too much data and now need to come with ideas on how to dissect it and make sense of it all. All this data can be important and meaningful to your business but sometimes you can get lost in the noise. The key is to find the one factor that cuts through all the noise and fluctuations and best correlates with what you want to improve or a derivative of that.This process often takes trial and error, wading through opinions, and finally deciding what is best right before your model blows up. I have been through this process many times and as a result hope to pass on some of our findings to you. Our search for the perfect variable came began when we first started dash boarding for clients on ServusConnect. We now had so much granular data that we could not see the forest for the trees, we found that managing the small actions, like individual work orders processed was not what mattered in this scenario. We needed to consolidate.Looking a little further downstream we decided that our goal was to found some consolidated variable that was a great measure of how the maintenance team was performing and one where we couldn't go any higher up without muddying the water with other factors that were out of the preview of maintenance operations. The variable that we decided on was the time the data of how long a work order was out before it was completed. At the end of the day, what maintenance operators want is something that allows them to see how much work the actually office is doing and to motivate them to do more whole at the same time ensuring a great experience for the customer. This time to complete a work order was then the perfect variable because it tracked a metric that the maintenance department could track and could see the changes driving the bottom line in the form of vacancy rate and percent of turn overs. However, we couldn't go any higher because then turnovers and vacancy rate could have many other factors that play a role creating a very cloudy data picture. Once we had our one variable nailed down, we had to decide what we were going to do with it, we immediately needed to start to understand what this variable was supposed to look like and how it was doing to behave, in short we needed to baseline what is going on and how the variable responds. Once you have a good understanding of the variable and what it does you really need to start setting goals and metrics to achieve in order to improve the variable and track to them.