The math behind epidemiological forecast models applies to work order survivability and can help make sense of what's going on in your building.
The West African Ebola outbreak of 2014 was horrific, but the eventual thousands of deaths were much lower than the worst case 1.4 million deaths forecast in the early days of the outbreak by the CDC. With so many factors unknown at the beginning of the outbreak, public health officials turned to sets of epidemiological models to forecast scenarios and then continually used data to get feedback on how the disease was progressing.
It turns out that those same mathematical methods are very handy for examining work order survivability (time-to-completion) data in buildings.
Consider some very simple questions that you might wonder when reviewing your work order data:
- We closed 22 work orders last week. Was that a good week?
- This work order has been open 104 days. Shouldn’t it have closed by now?
- Mary closed 32 work orders last week, and Bob only closed six. Should I talk to Bob?
These questions seem simple but are very difficult to answer. Maybe the 22 work orders from last week were easy, or marked urgent? Maybe the work order open for over three months really is complex? Maybe Bob was assigned more difficult issues than Mary?
Raw analytics show the varying time-to-resolution characteristics that different work order types tend to exhibit
As you can guess, Gridium’s analytical roots from energy management are quickly extending to building operations. With good analytics, we can use historical work order data to describe how work orders are closed, and describe differences by work order type or trade, urgency, location, assigned resource or even season. With that model, we can provide weekly detailed feedback on performance, as well as benchmarking and forecasting for budget planning.
The best news is that unlike the early days of Ebola, we aren’t guessing about something new. These techniques start to pay off with as little as 20 closed work orders. We’re still working to perfect the methods so analytics aren’t yet available in Tikkit, but are in preview with select customers. If you would like to join the discussion, please reach out.
