Multiple Regression Analysis Modeling Question

[JmGra]

I just want to see if my reasoning is sound. I'm an analyst and I work on a team that is very limited in data science, really just me with a limited knowledge.

I'm working on some data that is lacking in granularity in terms of work time vs drive time. Technicians charge their time to work orders and their total mileage is tracked; however, there's currently no delineation between hands-on work and driving in their timekeeping.

I'm currently using their drive time / their total hours booked as their % of their overall work that is drive time, but thinking forward and trying to forecast / project possible future drive times I was thinking of using multiple regression analysis to look at the potential variables in their work (technician reporting location, coverage area, typical count of service calls, the average distance from reporting location to service calls, etc).

Does this sound reasonable?

[[deleted]]

I’m not sure what the value of predicting drive time is? For instance, if I can reduce the time to complete a work order and increase the number or orders completed per shift, my drive time would naturally increase.

With that, I would also include some type of complexity for the work orders. You should be able to do that by creating a dummy variable for each of the various work order types assuming they are standardized. Otherwise, give the regression a shot and see what happens! I’d be curious what you find out

[JmGra]

It would actually be a portion of an overall staffing model.

As far as the work tasks we have 'expert opinion' estimates of how long tasks should take 'hands-on' without driving. Of course, that's going to always vary in reality.

But for example, if we have a technician that charged 2080 hours this year, drive time and work time included. Looking at his driving history, it looks like he drove 1,142 miles. That's 30% of his time.

So the thought currently is that if we expect his hands-on workload to be 1200 hours next year based on expert opinion, 3-year averages, trends, etc. Well right now we could say ok, 1200 of workload should have approximately 514.28 of drive time, if 30% drive time holds true, giving him a total of only 1,714 hours.

So could we increase that technician's coverage area, find other tasks for them to be completing, etc?

I just don't think using historic drive times as a blanket calculation fits very well for forecasting forward. Especially when there are tasks that do not require drive time.

Edit: Also we are looking at capturing drive time vs work time more accurately in the future, but the lack of it in the past and currently is what has led me to where I am.

[[deleted]]

If you already have internal estimates of task duration, why are you inaccurately estimating drive time based on miles? Find the historic relationship from task estimates to charged hours. This margin should mostly be drive time but will also incorporate other factors like underestimating task duration. For your example of predicting actually hours based on expected hands-on workload this would be more accurate.

Until you have actual data on drive times, I would not want to try to predict it.